random assignment is a crucial component of experiment design

Random Assignment in Psychology: Definition & Examples

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Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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In psychology, random assignment refers to the practice of allocating participants to different experimental groups in a study in a completely unbiased way, ensuring each participant has an equal chance of being assigned to any group.

In experimental research, random assignment, or random placement, organizes participants from your sample into different groups using randomization. 

Random assignment uses chance procedures to ensure that each participant has an equal opportunity of being assigned to either a control or experimental group.

The control group does not receive the treatment in question, whereas the experimental group does receive the treatment.

When using random assignment, neither the researcher nor the participant can choose the group to which the participant is assigned. This ensures that any differences between and within the groups are not systematic at the onset of the study. 

In a study to test the success of a weight-loss program, investigators randomly assigned a pool of participants to one of two groups.

Group A participants participated in the weight-loss program for 10 weeks and took a class where they learned about the benefits of healthy eating and exercise.

Group B participants read a 200-page book that explains the benefits of weight loss. The investigator randomly assigned participants to one of the two groups.

The researchers found that those who participated in the program and took the class were more likely to lose weight than those in the other group that received only the book.

Importance 

Random assignment ensures that each group in the experiment is identical before applying the independent variable.

In experiments , researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. Random assignment increases the likelihood that the treatment groups are the same at the onset of a study.

Thus, any changes that result from the independent variable can be assumed to be a result of the treatment of interest. This is particularly important for eliminating sources of bias and strengthening the internal validity of an experiment.

Random assignment is the best method for inferring a causal relationship between a treatment and an outcome.

Random Selection vs. Random Assignment 

Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study.

On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. 

Random selection ensures that everyone in the population has an equal chance of being selected for the study. Once the pool of participants has been chosen, experimenters use random assignment to assign participants into groups. 

Random assignment is only used in between-subjects experimental designs, while random selection can be used in a variety of study designs.

Random Assignment vs Random Sampling

Random sampling refers to selecting participants from a population so that each individual has an equal chance of being chosen. This method enhances the representativeness of the sample.

Random assignment, on the other hand, is used in experimental designs once participants are selected. It involves allocating these participants to different experimental groups or conditions randomly.

This helps ensure that any differences in results across groups are due to manipulating the independent variable, not preexisting differences among participants.

When to Use Random Assignment

Random assignment is used in experiments with a between-groups or independent measures design.

In these research designs, researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables.

There is usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable at the onset of the study.

How to Use Random Assignment

There are a variety of ways to assign participants into study groups randomly. Here are a handful of popular methods: 

• Random Number Generator : Give each member of the sample a unique number; use a computer program to randomly generate a number from the list for each group.
• Lottery : Give each member of the sample a unique number. Place all numbers in a hat or bucket and draw numbers at random for each group.
• Flipping a Coin : Flip a coin for each participant to decide if they will be in the control group or experimental group (this method can only be used when you have just two groups) 
• Roll a Die : For each number on the list, roll a dice to decide which of the groups they will be in. For example, assume that rolling 1, 2, or 3 places them in a control group and rolling 3, 4, 5 lands them in an experimental group.

When is Random Assignment not used?

• When it is not ethically permissible: Randomization is only ethical if the researcher has no evidence that one treatment is superior to the other or that one treatment might have harmful side effects. 
• When answering non-causal questions : If the researcher is just interested in predicting the probability of an event, the causal relationship between the variables is not important and observational designs would be more suitable than random assignment. 
• When studying the effect of variables that cannot be manipulated: Some risk factors cannot be manipulated and so it would not make any sense to study them in a randomized trial. For example, we cannot randomly assign participants into categories based on age, gender, or genetic factors.

Drawbacks of Random Assignment

While randomization assures an unbiased assignment of participants to groups, it does not guarantee the equality of these groups. There could still be extraneous variables that differ between groups or group differences that arise from chance. Additionally, there is still an element of luck with random assignments.

Thus, researchers can not produce perfectly equal groups for each specific study. Differences between the treatment group and control group might still exist, and the results of a randomized trial may sometimes be wrong, but this is absolutely okay.

Scientific evidence is a long and continuous process, and the groups will tend to be equal in the long run when data is aggregated in a meta-analysis.

Additionally, external validity (i.e., the extent to which the researcher can use the results of the study to generalize to the larger population) is compromised with random assignment.

Random assignment is challenging to implement outside of controlled laboratory conditions and might not represent what would happen in the real world at the population level. 

Random assignment can also be more costly than simple observational studies, where an investigator is just observing events without intervening with the population.

Randomization also can be time-consuming and challenging, especially when participants refuse to receive the assigned treatment or do not adhere to recommendations. 

What is the difference between random sampling and random assignment?

Random sampling refers to randomly selecting a sample of participants from a population. Random assignment refers to randomly assigning participants to treatment groups from the selected sample.

Does random assignment increase internal validity?

Yes, random assignment ensures that there are no systematic differences between the participants in each group, enhancing the study’s internal validity .

Does random assignment reduce sampling error?

Yes, with random assignment, participants have an equal chance of being assigned to either a control group or an experimental group, resulting in a sample that is, in theory, representative of the population.

Random assignment does not completely eliminate sampling error because a sample only approximates the population from which it is drawn. However, random sampling is a way to minimize sampling errors. 

When is random assignment not possible?

Random assignment is not possible when the experimenters cannot control the treatment or independent variable.

For example, if you want to compare how men and women perform on a test, you cannot randomly assign subjects to these groups.

Participants are not randomly assigned to different groups in this study, but instead assigned based on their characteristics.

Does random assignment eliminate confounding variables?

Yes, random assignment eliminates the influence of any confounding variables on the treatment because it distributes them at random among the study groups. Randomization invalidates any relationship between a confounding variable and the treatment.

Why is random assignment of participants to treatment conditions in an experiment used?

Random assignment is used to ensure that all groups are comparable at the start of a study. This allows researchers to conclude that the outcomes of the study can be attributed to the intervention at hand and to rule out alternative explanations for study results.

Further Reading

• Bogomolnaia, A., & Moulin, H. (2001). A new solution to the random assignment problem .  Journal of Economic theory ,  100 (2), 295-328.
• Krause, M. S., & Howard, K. I. (2003). What random assignment does and does not do .  Journal of Clinical Psychology ,  59 (7), 751-766.

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Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

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March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

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Chapter 6: Experimental Research

Experimental Design

Learning Objectives

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a  between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 university  students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assign participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This matching is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called  random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence.  Table 6.2  shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this possibility is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Treatment and Control Conditions

Between-subjects experiments are often used to determine whether a treatment works. In psychological research, a  treatment  is any intervention meant to change people’s behaviour for the better. This  intervention  includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a  treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a  no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A  placebo  is a simulated treatment that lacks any active ingredient or element that should make it effective, and a  placebo effect  is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008) [1] .

Placebo effects are interesting in their own right (see  Note “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works.  Figure 6.2  shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in  Figure 6.2 ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This  difference  is what is shown by a comparison of the two outer bars in  Figure 6.2 .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a waitlist control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This disclosure allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999) [2] . There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002) [3] . The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

Within-Subjects Experiments

In a within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book.  However, not all experiments can use a within-subjects design nor would it be desirable to.

Carryover Effects and Counterbalancing

The primary disad vantage of within-subjects designs is that they can result in carryover effects. A  carryover effect  is an effect of being tested in one condition on participants’ behaviour in later conditions. One type of carryover effect is a  practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This  type of effect  is called a  context effect . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This  knowledge  could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is  counterbalancing , which means testing different participants in different orders. For example, some participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

An efficient way of counterbalancing is through a Latin square design which randomizes through having equal rows and columns. For example, if you have four treatments, you must have four versions. Like a Sudoku puzzle, no treatment can repeat in a row or column. For four versions of four treatments, the Latin square design would look like:

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 is “larger” than 221

Researcher Michael Birnbaum has argued that the lack of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this problem, he asked participants to rate two numbers on how large they were on a scale of 1-to-10 where 1 was “very very small” and 10 was “very very large”.  One group of participants were asked to rate the number 9 and another group was asked to rate the number 221 (Birnbaum, 1999) [4] . Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this difference is because participants spontaneously compared 9 with other one-digit numbers (in which case it is relatively large) and compared 221 with other three-digit numbers (in which case it is relatively small) .

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. There are many ways to determine the order in which the stimuli are presented, but one common way is to generate a different random order for each participant.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This possibility means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this design is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This difficulty is true for many designs that involve a treatment meant to produce long-term change in participants’ behaviour (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often take exactly this type of mixed methods approach.

Key Takeaways

• Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.
• Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.
• Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.
• You want to test the relative effectiveness of two training programs for running a marathon.
• Using photographs of people as stimuli, you want to see if smiling people are perceived as more intelligent than people who are not smiling.
• In a field experiment, you want to see if the way a panhandler is dressed (neatly vs. sloppily) affects whether or not passersby give him any money.
• You want to see if concrete nouns (e.g.,  dog ) are recalled better than abstract nouns (e.g.,  truth ).
• Discussion: Imagine that an experiment shows that participants who receive psychodynamic therapy for a dog phobia improve more than participants in a no-treatment control group. Explain a fundamental problem with this research design and at least two ways that it might be corrected.
• Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590. ↵
• Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press. ↵
• Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88. ↵
• Birnbaum, M.H. (1999). How to show that 9>221: Collect judgments in a between-subjects design. Psychological Methods, 4(3), 243-249. ↵

An experiment in which each participant is only tested in one condition.

A method of controlling extraneous variables across conditions by using a random process to decide which participants will be tested in the different conditions.

All the conditions of an experiment occur once in the sequence before any of them is repeated.

Any intervention meant to change people’s behaviour for the better.

A condition in a study where participants receive treatment.

A condition in a study that the other condition is compared to. This group does not receive the treatment or intervention that the other conditions do.

A type of experiment to research the effectiveness of psychotherapies and medical treatments.

A type of control condition in which participants receive no treatment.

A simulated treatment that lacks any active ingredient or element that should make it effective.

A positive effect of a treatment that lacks any active ingredient or element to make it effective.

Participants receive a placebo that looks like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness.

Participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it.

Each participant is tested under all conditions.

An effect of being tested in one condition on participants’ behaviour in later conditions.

Participants perform a task better in later conditions because they have had a chance to practice it.

Participants perform a task worse in later conditions because they become tired or bored.

Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions.

Testing different participants in different orders.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Advancing research • shaping policy • developing leaders, why randomize.

About Randomized Field Experiments Randomized field experiments allow researchers to scientifically measure the impact of an intervention on a particular outcome of interest.

What is a randomized field experiment? In a randomized experiment, a study sample is divided into one group that will receive the intervention being studied (the treatment group) and another group that will not receive the intervention (the control group). For instance, a study sample might consist of all registered voters in a particular city. This sample will then be randomly divided into treatment and control groups. Perhaps 40% of the sample will be on a campaign’s Get-Out-the-Vote (GOTV) mailing list and the other 60% of the sample will not receive the GOTV mailings. The outcome measured –voter turnout– can then be compared in the two groups. The difference in turnout will reflect the effectiveness of the intervention.

What does random assignment mean? The key to randomized experimental research design is in the random assignment of study subjects – for example, individual voters, precincts, media markets or some other group – into treatment or control groups. Randomization has a very specific meaning in this context. It does not refer to haphazard or casual choosing of some and not others. Randomization in this context means that care is taken to ensure that no pattern exists between the assignment of subjects into groups and any characteristics of those subjects. Every subject is as likely as any other to be assigned to the treatment (or control) group. Randomization is generally achieved by employing a computer program containing a random number generator. Randomization procedures differ based upon the research design of the experiment. Individuals or groups may be randomly assigned to treatment or control groups. Some research designs stratify subjects by geographic, demographic or other factors prior to random assignment in order to maximize the statistical power of the estimated effect of the treatment (e.g., GOTV intervention). Information about the randomization procedure is included in each experiment summary on the site.

What are the advantages of randomized experimental designs? Randomized experimental design yields the most accurate analysis of the effect of an intervention (e.g., a voter mobilization phone drive or a visit from a GOTV canvasser, on voter behavior). By randomly assigning subjects to be in the group that receives the treatment or to be in the control group, researchers can measure the effect of the mobilization method regardless of other factors that may make some people or groups more likely to participate in the political process. To provide a simple example, say we are testing the effectiveness of a voter education program on high school seniors. If we allow students from the class to volunteer to participate in the program, and we then compare the volunteers’ voting behavior against those who did not participate, our results will reflect something other than the effects of the voter education intervention. This is because there are, no doubt, qualities about those volunteers that make them different from students who do not volunteer. And, most important for our work, those differences may very well correlate with propensity to vote. Instead of letting students self-select, or even letting teachers select students (as teachers may have biases in who they choose), we could randomly assign all students in a given class to be in either a treatment or control group. This would ensure that those in the treatment and control groups differ solely due to chance. The value of randomization may also be seen in the use of walk lists for door-to-door canvassers. If canvassers choose which houses they will go to and which they will skip, they may choose houses that seem more inviting or they may choose houses that are placed closely together rather than those that are more spread out. These differences could conceivably correlate with voter turnout. Or if house numbers are chosen by selecting those on the first half of a ten page list, they may be clustered in neighborhoods that differ in important ways from neighborhoods in the second half of the list. Random assignment controls for both known and unknown variables that can creep in with other selection processes to confound analyses. Randomized experimental design is a powerful tool for drawing valid inferences about cause and effect. The use of randomized experimental design should allow a degree of certainty that the research findings cited in studies that employ this methodology reflect the effects of the interventions being measured and not some other underlying variable or variables.

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8.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

• random assignment of participants into experimental and control groups
• a “treatment” (or intervention) provided to the experimental group
• measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963).  The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level research

You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals.  For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change.  There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).  Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments.  For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

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Experimental Research

24 Experimental Design

Learning objectives.

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a  between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 university students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assigns participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average IQs, similar average levels of motivation, similar average numbers of health problems, and so on. This matching is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called  random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as they are tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence.  Table 5.2  shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Random assignment is not guaranteed to control all extraneous variables across conditions. The process is random, so it is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this possibility is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Matched Groups

An alternative to simple random assignment of participants to conditions is the use of a matched-groups design . Using this design, participants in the various conditions are matched on the dependent variable or on some extraneous variable(s) prior the manipulation of the independent variable. This guarantees that these variables will not be confounded across the experimental conditions. For instance, if we want to determine whether expressive writing affects people’s health then we could start by measuring various health-related variables in our prospective research participants. We could then use that information to rank-order participants according to how healthy or unhealthy they are. Next, the two healthiest participants would be randomly assigned to complete different conditions (one would be randomly assigned to the traumatic experiences writing condition and the other to the neutral writing condition). The next two healthiest participants would then be randomly assigned to complete different conditions, and so on until the two least healthy participants. This method would ensure that participants in the traumatic experiences writing condition are matched to participants in the neutral writing condition with respect to health at the beginning of the study. If at the end of the experiment, a difference in health was detected across the two conditions, then we would know that it is due to the writing manipulation and not to pre-existing differences in health.

Within-Subjects Experiments

In a  within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive  and  an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book .  However, not all experiments can use a within-subjects design nor would it be desirable to do so.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in order effects. An order effect   occurs when participants’ responses in the various conditions are affected by the order of conditions to which they were exposed. One type of order effect is a carryover effect. A  carryover effect  is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a  practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This  type of effect is called a  context effect (or contrast effect) . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This knowledge could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is  counterbalancing , which means testing different participants in different orders. The best method of counterbalancing is complete counterbalancing   in which an equal number of participants complete each possible order of conditions. For example, half of the participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others half would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With four conditions, there would be 24 different orders; with five conditions there would be 120 possible orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus, random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

A more efficient way of counterbalancing is through a Latin square design which randomizes through having equal rows and columns. For example, if you have four treatments, you must have four versions. Like a Sudoku puzzle, no treatment can repeat in a row or column. For four versions of four treatments, the Latin square design would look like:

You can see in the diagram above that the square has been constructed to ensure that each condition appears at each ordinal position (A appears first once, second once, third once, and fourth once) and each condition precedes and follows each other condition one time. A Latin square for an experiment with 6 conditions would by 6 x 6 in dimension, one for an experiment with 8 conditions would be 8 x 8 in dimension, and so on. So while complete counterbalancing of 6 conditions would require 720 orders, a Latin square would only require 6 orders.

Finally, when the number of conditions is large experiments can use  random counterbalancing  in which the order of the conditions is randomly determined for each participant. Using this technique every possible order of conditions is determined and then one of these orders is randomly selected for each participant. This is not as powerful a technique as complete counterbalancing or partial counterbalancing using a Latin squares design. Use of random counterbalancing will result in more random error, but if order effects are likely to be small and the number of conditions is large, this is an option available to researchers.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 Is “Larger” Than 221

Researcher Michael Birnbaum has argued that the  lack  of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this problem, he asked participants to rate two numbers on how large they were on a scale of 1-to-10 where 1 was “very very small” and 10 was “very very large”.  One group of participants were asked to rate the number 9 and another group was asked to rate the number 221 (Birnbaum, 1999) [1] . Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this  difference  is because participants spontaneously compared 9 with other one-digit numbers (in which case it is  relatively large) and compared 221 with other three-digit numbers (in which case it is relatively  small).

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. 

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This possibility means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect any effect of the independent variable upon the dependent variable. Within-subjects experiments also require fewer participants than between-subjects experiments to detect an effect of the same size.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this design is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This difficulty is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often take exactly this type of mixed methods approach.

• Birnbaum, M.H. (1999). How to show that 9>221: Collect judgments in a between-subjects design. Psychological Methods, 4 (3), 243-249. ↵

An experiment in which each participant is tested in only one condition.

Means using a random process to decide which participants are tested in which conditions.

All the conditions occur once in the sequence before any of them is repeated.

An experiment design in which the participants in the various conditions are matched on the dependent variable or on some extraneous variable(s) prior the manipulation of the independent variable.

An experiment in which each participant is tested under all conditions.

An effect that occurs when participants' responses in the various conditions are affected by the order of conditions to which they were exposed.

An effect of being tested in one condition on participants’ behavior in later conditions.

An effect where participants perform a task better in later conditions because they have had a chance to practice it.

An effect where participants perform a task worse in later conditions because they become tired or bored.

Unintended influences on respondents’ answers because they are not related to the content of the item but to the context in which the item appears.

Varying the order of the conditions in which participants are tested, to help solve the problem of order effects in within-subjects experiments.

A method in which an equal number of participants complete each possible order of conditions. 

A method in which the order of the conditions is randomly determined for each participant.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Unraveling the Mystery of Random Assignment in Psychology

Random assignment is a crucial concept in psychology research, ensuring the validity and reliability of experiments. But what exactly is random assignment, and why is it so important in the field of psychology?

In this article, we will discuss the difference between random assignment and random sampling, the steps involved in random assignment, and how researchers can effectively implement this technique. We will also explore the benefits and limitations of random assignment, as well as ways to ensure its effectiveness in psychology research.

Join us as we unravel the mystery of random assignment in psychology.

• Random assignment is a research method used in psychology to eliminate bias and increase internal validity by randomly assigning participants to different groups.
• Unlike random sampling, which selects participants for a study, random assignment randomly distributes participants into groups to ensure unbiased results.
• Researchers can ensure effective random assignment by using randomization tables, random number generators, and stratified random assignment to increase the accuracy and generalizability of their findings.
• 1 What is Random Assignment in Psychology?
• 2.1 What is the Difference between Random Assignment and Random Sampling?
• 3.1 What are the Steps Involved in Random Assignment?
• 4.1 Eliminates Bias
• 4.2 Increases Internal Validity
• 4.3 Allows for Generalizability
• 5.1 Practical Limitations
• 5.2 Ethical Concerns
• 6.1 Use a Randomization Table
• 6.2 Use a Random Number Generator
• 6.3 Use Stratified Random Assignment
• 7.1 What is random assignment and why is it important in psychology?
• 7.2 How is random assignment different from random selection?
• 7.3 What are some common methods of random assignment in psychology research?
• 7.4 Are there any limitations to random assignment in psychology research?
• 7.5 What are the advantages of using random assignment in psychology research?
• 7.6 Can random assignment be used in all types of psychology research?

What is Random Assignment in Psychology?

Random assignment in psychology refers to the method of placing participants in experimental groups through a random process to ensure unbiased distribution of characteristics.

This method is crucial in research studies as it allows for the elimination of potential biases that could skew results, leading to more accurate and generalizable findings. By randomly assigning participants, researchers can be more confident that any differences observed between groups are due to the treatment or intervention being studied rather than pre-existing individual characteristics.

For example, in a study investigating the effectiveness of a new therapy for anxiety, random assignment would involve randomly assigning participants with similar levels of anxiety to either the treatment group receiving the new therapy or the control group receiving a placebo. Variables such as age, gender, and severity of anxiety are controlled through random assignment to ensure that any differences in outcomes can be attributed to the therapy.

Why is Random Assignment Important in Psychology Experiments?

Random assignment holds paramount importance in psychology experiments as it enhances internal validity, establishes cause-and-effect relationships, and ensures accurate data analysis.

Random assignment involves the objective allocation of participants into different experimental groups without any bias or preconceived notions. This method is crucial in ensuring that researchers can confidently draw conclusions about the causal relationships being examined, rather than attributing any observed effects to other variables.

By randomly assigning participants, researchers can control for potential confounding variables and eliminate the influence of extraneous factors, thus strengthening the internal validity of the study. This process minimizes the likelihood of alternative explanations for the results, allowing for more accurate interpretations and conclusions.

In fields like clinical trials, the use of random assignment is fundamental in evaluating the effectiveness of new treatments or interventions. Test performance studies also rely on random assignment to evenly distribute factors that may impact scores, such as motivation levels or prior knowledge. In behavioral studies, random assignment ensures that participants are evenly distributed across conditions, reducing the risk of bias and increasing the generalizability of findings.

What is the Difference between Random Assignment and Random Sampling?

Random assignment and random sampling are distinct concepts in research methodology; while random assignment involves the allocation of participants to groups, random sampling pertains to the selection of a representative sample from a population.

In research design, random assignment plays a crucial role in ensuring the control and distribution of variables among different experimental groups, thereby minimizing bias and allowing researchers to establish cause-effect relationships. On the other hand, random sampling is essential for obtaining a sample that accurately represents the larger population being studied, increasing the generalizability of research findings.

For instance, in a study investigating the effects of a new medication, researchers may use random assignment to assign participants randomly to either the treatment group receiving the medication or the control group receiving a placebo. This random allocation helps in isolating the impact of the medication from other variables.

Conversely, when employing random sampling, researchers aim to select participants in a way that every individual in the population has an equal chance of being included in the study. This method ensures that the sample closely reflects the characteristics of the entire population under investigation.

How is Random Assignment Used in Psychology Research?

Random assignment is a fundamental component of psychology research, utilized to allocate participants randomly to groups in controlled experiments to investigate the impact of variables on study outcomes.

In experimental design, researchers use random assignment to ensure that participants have equal chances of being assigned to different conditions, reducing bias and increasing the validity of the study results.

This method allows researchers to confidently infer causality between variables, as any differences observed in outcomes can be attributed to the manipulation of independent variables, rather than pre-existing participant characteristics.

Clinical research often relies on random assignment to assess the efficacy of new treatments or interventions, helping to establish evidence-based practices that improve patient outcomes.

What are the Steps Involved in Random Assignment?

The steps in random assignment entail the creation of groups, selection of participants, and the assignment process itself, ensuring a randomized distribution in the experimental design.

The creation of groups involves categorizing the participants based on relevant criteria such as age, gender, or other demographics to form distinct experimental and control groups. Then, the selection of participants requires a systematic approach to avoid bias, ensuring that each individual has an equal chance of inclusion.

Following this, the assignment process involves using randomization methods like coin flipping, random number generators, or computer algorithms to determine which group each participant will be allocated to. By doing this, the randomization helps reduce the impact of confounding variables, making the results more reliable and valid.

What are the Benefits of Using Random Assignment in Psychology?

Using random assignment in psychology offers multiple benefits such as eliminating bias , increasing internal validity, and establishing causal relationships crucial for accurate data analysis in behavioral studies.

Random assignment is a method that involves every participant having an equal chance of being assigned to any condition or group within a study. By implementing this technique, researchers can ensure that potential confounding variables are evenly distributed across groups, leading to more reliable and valid results . This process is integral in psychology research as it not only strengthens the internal validity of a study but also allows researchers to confidently attribute any observed differences to the treatment being studied.

Eliminates Bias

One of the key benefits of random assignment is its ability to eliminate bias by ensuring that participants are equally distributed between the control and treatment groups, mitigating the impact of confounding variables.

Reducing bias in research is crucial as it enhances the internal validity of the study, making the results more reliable and generalizable.

• Random assignment is particularly vital in experimental studies, where the goal is to determine causality.

For instance, imagine a study on the effectiveness of a new medication for hypertension. If participants with severe hypertension are all placed in the treatment group, and those with mild hypertension in the control group, the results may not accurately reflect the medication’s true impact.

Increases Internal Validity

Random assignment enhances internal validity by ensuring that any observed effects are attributed to the manipulation of the independent variable rather than external factors, strengthening the causal inference between variables.

Control and treatment groups play a crucial role in this process. The control group does not receive the treatment , serving as a baseline comparison to evaluate the impact of the independent variable. On the other hand, the treatment group is exposed to the independent variable. This distinction allows researchers to isolate the effects of the intervention accurately.

The relationship between the independent and dependent variables is key. The independent variable is manipulated by the researcher to observe its effect on the dependent variable. For instance, in a study testing a new drug’s efficacy (independent variable), the patient’s health outcomes (dependent variable) are measured.

Allows for Generalizability

Random assignment enables generalizability by creating samples that represent the broader population, increasing the validity of research findings and supporting the generalization of hypotheses to larger groups.

When researchers use random assignment, it helps to eliminate bias and ensure that participants are equally distributed between different experimental conditions. This method enhances the likelihood that the results are not skewed by pre-existing differences among participants, thus making the findings more reliable and applicable to a wider range of individuals.

By having diverse and representative samples through random assignment, researchers can draw conclusions that are more likely to be valid for the entire population, rather than just a specific subgroup. This approach also enhances the ability to make predictions and recommendations based on the study’s outcomes that can be beneficial for decision-making processes in various fields.

What are the Limitations of Random Assignment in Psychology?

Despite its advantages, random assignment in psychology experiments faces limitations such as practical constraints that may affect the implementation process and ethical considerations related to participant welfare.

One practical challenge encountered with random assignment is the logistical complexity of ensuring a truly random allocation of participants to experimental conditions. Researchers may find it difficult to maintain perfect randomization due to issues like accessibility, time constraints, and resources required. For instance, in a study aiming to investigate the effects of sleep deprivation on cognitive performance, ensuring that participants are randomly assigned to control and experimental groups might be challenging.

Ethical dilemmas arise concerning the well-being of participants. Random assignment can lead to unequal group distributions, potentially exposing some individuals to risks without corresponding benefits. For instance, assigning participants with a history of mental health issues to a placebo group in a study testing the efficacy of a new treatment can raise ethical concerns.

Addressing these challenges requires researchers to adopt measures such as stratified random assignment, where participants are grouped based on specific characteristics to ensure balanced representation across experimental conditions. By predefining strata, researchers can control for variables that may affect outcomes.

Practical Limitations

Practical limitations of random assignment include logistical challenges in participant recruitment, constraints in experimental design, and potential impacts on study outcomes due to practical considerations.

One of the major challenges researchers face is the difficulty of ensuring a truly randomized sample, especially when dealing with complex recruitment processes and limited resources for participant selection. The logistics involved in coordinating experimental procedures for each participant can be overwhelming, leading to delays in data collection and analysis.

These issues can significantly affect the internal validity of a study, as deviations from random assignment may introduce bias and confound the results. To mitigate these challenges, researchers can adopt strategies such as stratified randomization or matching to improve participant allocation and minimize the impact of logistical constraints on the study outcomes.

Ethical Concerns

Ethical concerns in random assignment revolve around participant welfare, equitable treatment in the control and treatment groups, and the ethical implications of manipulating variables that may impact individuals’ well-being.

When conducting a psychology experiment, researchers must ensure that the random assignment of participants to different groups is carried out in a fair and unbiased manner. This is crucial in maintaining the integrity of the study and upholding ethical principles.

Participant welfare is paramount, and researchers have a responsibility to safeguard the well-being of individuals involved in the research.

How Can Researchers Ensure Effective Random Assignment?

Researchers can ensure effective random assignment by utilizing tools such as randomization tables , random number generators , and stratified random assignment methods to enhance the randomness and validity of group allocations.

Randomization tables help match participants to different treatment groups based on a predefined criteria or algorithm, ensuring an unbiased assignment process. Random number generators play a crucial role in allocating participants to groups without any conscious or subconscious bias, fostering transparent and fair treatment allocations.

Implementing stratified assignments involves dividing participants into subgroups based on specific characteristics, such as age, gender, or severity of the condition, to create more homogeneous groups for more accurate results.

Best practices for maintaining the integrity of the random assignment process include double-blinding the study, ensuring proper concealment of allocation mechanisms, and conducting randomization procedures by an independent party to minimize potential biases.

Use a Randomization Table

A randomization table is a valuable tool in research that aids in the allocation of participants to different groups using a predetermined random sequence, ensuring an unbiased distribution in the random assignment process.

By utilizing a randomization table, researchers can avoid selection bias and ensure that each participant has an equal chance of being assigned to any group. This method promotes fairness and helps in achieving comparability among the groups in a study. For example, in a clinical trial testing a new medication, a randomization table can be employed to assign participants either to the treatment group receiving the medication or the control group receiving a placebo.

The benefits of using randomization tables include increased internal validity, reduced confounding variables, and the ability to demonstrate causal relationships with greater confidence. This tool enhances the reliability and replicability of research findings by minimizing systematic errors in group allocations.

Use a Random Number Generator

In research, a random number generator is employed to allocate participants randomly to groups, ensuring an unbiased distribution and enhancing the validity and reliability of study outcomes.

Random number generators play a crucial role in the scientific method by enabling researchers to achieve randomness essential for reliable experiments. They aid in minimizing selection bias, thereby contributing to the integrity of the study design. Random number generators uphold the principle of chance, fostering a fair and equal opportunity for each participant to be assigned to a specific condition. This methodological approach ensures that the treatment and control groups are comparable, leading to more accurate conclusions and interpretations.

Use Stratified Random Assignment

Stratified random assignment involves grouping participants based on specific characteristics before random assignment, allowing for the control of variables and ensuring a balanced representation within groups.

This methodology is particularly useful in research design as it helps minimize the potential biases that can arise in studies. By dividing participants into homogeneous subgroups, such as age, gender, or socio-economic status, researchers can ensure that each subgroup is appropriately represented in the study sample. For example, in a healthcare study, stratified random assignment can ensure that both younger and older age groups are equally represented, providing more comprehensive results that can be generalized to the larger population.

Frequently Asked Questions

What is random assignment and why is it important in psychology.

Random assignment is the process of randomly assigning participants to different groups in a research study. It is important in psychology because it helps to eliminate bias and ensure that the groups being compared are similar, allowing researchers to determine the true effects of a variable.

How is random assignment different from random selection?

Random assignment involves randomly assigning participants to different groups, while random selection involves randomly choosing participants from a larger population. Random assignment is done within the chosen sample, while random selection is done before the sample is chosen.

What are some common methods of random assignment in psychology research?

Some common methods of random assignment include simple random assignment, stratified random assignment, and matched random assignment. Simple random assignment involves randomly assigning participants to groups with no restrictions. Stratified random assignment involves dividing participants into subgroups and then randomly assigning participants from each subgroup to different groups. Matched random assignment involves pairing participants based on certain characteristics and then randomly assigning one of each pair to a group.

Are there any limitations to random assignment in psychology research?

Yes, there are some limitations to random assignment. For example, it may not always be feasible or ethical to randomly assign participants to different groups. Additionally, random assignment does not guarantee that the groups will be exactly equal on all characteristics, which could potentially impact the results of the study.

What are the advantages of using random assignment in psychology research?

The main advantage of using random assignment is that it helps to eliminate bias and ensure that the groups being compared are similar. This allows researchers to make more accurate conclusions about the relationship between variables and determine causality.

Can random assignment be used in all types of psychology research?

Random assignment is commonly used in experimental research, where participants are randomly assigned to different conditions. However, it may not be as useful in other types of research, such as correlational studies, where participants are not manipulated and groups cannot be randomly assigned.

Lena Nguyen, an industrial-organizational psychologist, specializes in employee engagement, leadership development, and organizational culture. Her consultancy work has helped businesses build stronger teams and create environments that promote innovation and efficiency. Lena’s articles offer a fresh perspective on managing workplace dynamics and harnessing the potential of human capital in achieving business success.

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What Is Random Assignment in Psychology?

Categories Research Methods

Random assignment means that every participant has the same chance of being chosen for the experimental or control group. It involves using procedures that rely on chance to assign participants to groups. Doing this means that every participant in a study has an equal opportunity to be assigned to any group.

For example, in a psychology experiment, participants might be assigned to either a control or experimental group. Some experiments might only have one experimental group, while others may have several treatment variations.

Using random assignment means that each participant has the same chance of being assigned to any of these groups.

Table of Contents

How to Use Random Assignment

So what type of procedures might psychologists utilize for random assignment? Strategies can include:

• Flipping a coin
• Assigning random numbers
• Rolling dice
• Drawing names out of a hat

How Does Random Assignment Work?

A psychology experiment aims to determine if changes in one variable lead to changes in another variable. Researchers will first begin by coming up with a hypothesis. Once researchers have an idea of what they think they might find in a population, they will come up with an experimental design and then recruit participants for their study.

Once they have a pool of participants representative of the population they are interested in looking at, they will randomly assign the participants to their groups.

• Control group : Some participants will end up in the control group, which serves as a baseline and does not receive the independent variables.
• Experimental group : Other participants will end up in the experimental groups that receive some form of the independent variables.

By using random assignment, the researchers make it more likely that the groups are equal at the start of the experiment. Since the groups are the same on other variables, it can be assumed that any changes that occur are the result of varying the independent variables.

After a treatment has been administered, the researchers will then collect data in order to determine if the independent variable had any impact on the dependent variable.

Random Assignment vs. Random Selection

It is important to remember that random assignment is not the same thing as random selection , also known as random sampling.

Random selection instead involves how people are chosen to be in a study. Using random selection, every member of a population stands an equal chance of being chosen for a study or experiment.

So random sampling affects how participants are chosen for a study, while random assignment affects how participants are then assigned to groups.

Examples of Random Assignment

Imagine that a psychology researcher is conducting an experiment to determine if getting adequate sleep the night before an exam results in better test scores.

Forming a Hypothesis

They hypothesize that participants who get 8 hours of sleep will do better on a math exam than participants who only get 4 hours of sleep.

Obtaining Participants

The researcher starts by obtaining a pool of participants. They find 100 participants from a local university. Half of the participants are female, and half are male.

Randomly Assign Participants to Groups

The researcher then assigns random numbers to each participant and uses a random number generator to randomly assign each number to either the 4-hour or 8-hour sleep groups.

Conduct the Experiment

Those in the 8-hour sleep group agree to sleep for 8 hours that night, while those in the 4-hour group agree to wake up after only 4 hours. The following day, all of the participants meet in a classroom.

Collect and Analyze Data

Everyone takes the same math test. The test scores are then compared to see if the amount of sleep the night before had any impact on test scores.

Why Is Random Assignment Important in Psychology Research?

Random assignment is important in psychology research because it helps improve a study’s internal validity. This means that the researchers are sure that the study demonstrates a cause-and-effect relationship between an independent and dependent variable.

Random assignment improves the internal validity by minimizing the risk that there are systematic differences in the participants who are in each group.

Key Points to Remember About Random Assignment

• Random assignment in psychology involves each participant having an equal chance of being chosen for any of the groups, including the control and experimental groups.
• It helps control for potential confounding variables, reducing the likelihood of pre-existing differences between groups.
• This method enhances the internal validity of experiments, allowing researchers to draw more reliable conclusions about cause-and-effect relationships.
• Random assignment is crucial for creating comparable groups and increasing the scientific rigor of psychological studies.

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• Knowledge Base
• Methodology
• Random Assignment in Experiments | Introduction & Examples

Random Assignment in Experiments | Introduction & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 13 February 2023.

In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomisation.

With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomised designs .

Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors.

Table of contents

Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, frequently asked questions about random assignment.

Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment.

In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.

This is called a between-groups or independent measures design.

You use three groups of participants that are each given a different level of the independent variable:

• A control group that’s given a placebo (no dosage)
• An experimental group that’s given a low dosage
• A second experimental group that’s given a high dosage

Random assignment to helps you make sure that the treatment groups don’t differ in systematic or biased ways at the start of the experiment.

If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.

• Participants recruited from pubs are placed in the control group
• Participants recruited from local community centres are placed in the low-dosage experimental group
• Participants recruited from gyms are placed in the high-dosage group

With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym users may tend to engage in more healthy behaviours than people who frequent pubs or community centres, and this would introduce a healthy user bias in your study.

Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.

Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.

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Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.

Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups.

While random sampling is used in many types of studies, random assignment is only used in between-subjects experimental designs.

Some studies use both random sampling and random assignment, while others use only one or the other.

Random sampling enhances the external validity or generalisability of your results, because it helps to ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .

You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8,000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.

Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .

• A control group that receives no intervention
• An experimental group that has a remote team-building intervention every week for a month

You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.

To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.

• Random number generator: Use a computer program to generate random numbers from the list for each group.
• Lottery method: Place all numbers individually into a hat or a bucket, and draw numbers at random for each group.
• Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
• Use a dice: When you have three groups, for each number on the list, roll a die to decide which of the groups they will be in. For example, assume that rolling 1 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.

This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.

Random assignment in block designs

In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .

For example, a randomised block design involves placing participants into blocks based on a shared characteristic (e.g., college students vs graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.

In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.

Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.

When comparing different groups

Sometimes, differences between participants are the main focus of a study, for example, when comparing children and adults or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.

In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women). All participants are tested the same way, and then their group-level outcomes are compared.

When it’s not ethically permissible

When studying unhealthy or dangerous behaviours, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.

When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers).

These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

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Module 2: Research Design - Section 2

• Section 1 Discussion
• Section 2 Discussion

Section 2: Experimental Studies

Unlike a descriptive study, an experiment is a study in which a treatment, procedure, or program is intentionally introduced and a result or outcome is observed. The American Heritage Dictionary of the English Language defines an experiment as "A test under controlled conditions that is made to demonstrate a known truth, to examine the validity of a hypothesis, or to determine the efficacy of something previously untried."

This means that no matter who the participant is, he/she has an equal chance of getting into all of the groups or treatments in an experiment. This process helps to ensure that the groups or treatments are similar at the beginning of the study so that there is more confidence that the manipulation (group or treatment) "caused" the outcome. More information about random assignment may be found in section Random assignment.

Definition : An experiment is a study in which a treatment, procedure, or program is intentionally introduced and a result or outcome is observed.

Case Example for Experimental Study

Experimental studies — example 1.

Experimental Studies — Example 2

A fitness instructor wants to test the effectiveness of a performance-enhancing herbal supplement on students in her exercise class. To create experimental groups that are similar at the beginning of the study, the students are assigned into two groups at random (they can not choose which group they are in). Students in both groups are given a pill to take every day, but they do not know whether the pill is a placebo (sugar pill) or the herbal supplement. The instructor gives Group A the herbal supplement and Group B receives the placebo (sugar pill). The students' fitness level is compared before and after six weeks of consuming the supplement or the sugar pill. No differences in performance ability were found between the two groups suggesting that the herbal supplement was not effective.

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Completely Randomized Design: The One-Factor Approach

Completely Randomized Design (CRD) is a research methodology in which experimental units are randomly assigned to treatments without any systematic bias. CRD gained prominence in the early 20th century, largely attributed to the pioneering work of statistician Ronald A. Fisher . His method addressed the inherent variability in experimental units by randomly assigning treatments, thus countering potential biases. Today, CRD serves as an indispensable tool in various domains, including agriculture, medicine, industrial engineering, and quality control analysis.

CRD is particularly favored in situations with limited control over external variables. By leveraging its inherent randomness, CRD neutralizes potentially confounding factors. As a result, each experimental unit has an equal likelihood of receiving any specific treatment, ensuring a level playing field. Such random allocation is pivotal in eliminating systematic bias and bolstering the validity of experimental conclusions.

While CRD may sometimes necessitate larger sample sizes , the improved accuracy and consistency it introduces to results often justify this requirement.

Understanding CRD

At its core, CRD is centered on harnessing randomness to achieve objective experimental outcomes. This approach effectively addresses unanticipated extraneous variables —those not included in the study design but that can still influence the response variable. In the context of CRD, these extraneous variables are expected to be uniformly distributed across treatments, thereby mitigating their potential influence.

A key aspect of CRD is the single-factor experiment. This means that the experiment revolves around changing or manipulating one primary independent variable (or factor) to ascertain its effect on the dependent variable . Consider these examples across different fields:

• Medical: An experiment might be designed where the independent variable is the dosage of a new drug, and the dependent variable is the speed of patient recovery. Researchers would vary the drug dosage and observe its effect on recovery rates.
• Agriculture: An agricultural study could alter the amount of water irrigation (independent variable) given to crops and measure the resulting crop yield (dependent variable) to determine the optimal irrigation level.
• Psychology: A psychologist might introduce different intensities of a visual cue (independent variable) to participants and then measure their reaction times (dependent variable) to understand the cue's influence.
• Environmental Science: Scientists might introduce different concentrations of a pollutant (independent variable) to a freshwater pond and measure the health and survival rate of aquatic life (dependent variable) in response.
• Education: In an educational setting, researchers could change the duration of digital learning (independent variable) students receive daily and then observe its effect on test scores (dependent variable) at the end of the term.
• Engineering: In material science, an experiment might adjust the temperature (independent variable) during the curing process of a polymer and then measure its resultant tensile strength (dependent variable).

For each of these scenarios, only one key factor or independent variable is intentionally varied, while any changes or outcomes in another variable (the dependent variable) are observed and recorded. This distinct focus on a single variable, while keeping all others constant or controlled, underscores the essence of the single-factor experiment in CRD.

Advantages of CRD

Understanding the strengths of Completely Randomized Design is pivotal for effectively applying this research tool and interpreting results accurately. Below is an exploration of the benefits of employing CRD in research studies.

• Simplicity: One of the most appealing features of CRD is its straightforwardness. Focusing on a single primary factor, CRD is easier to understand and implement compared to more complex research designs.
• Flexibility: CRD enhances versatility by allowing the inclusion of various experimental units and treatments through random assignment, enabling researchers to explore a range of variables.
• Robustness: Despite its simplicity, CRD stands as a robust research tool. The consistent use of randomization minimizes biases and uniformly distributes the effects of uncontrolled variables across all groups, contributing to the reliability of the results.
• Generalizability: Proper application of CRD enables the extension of research findings to a broader population. The minimization of selection bias , thanks to random assignment, increases the probability that the sample closely represents the larger population.

Disadvantages of CRD

While CRD is marked by simplicity, flexibility, robustness, and enhanced generalizability, it is essential to carefully consider its limitations. A thoughtful analysis of these aspects will guide researchers in making informed decisions about the applicability of CRD to their specific research context.

• Ignoring Nuisance Variables: CRD operates primarily under the assumption that all treatments are equivalent aside from the independent variable. If strong nuisance factors vary systematically across treatments, this assumption becomes a limitation, making CRD less suitable for studies where nuisance variables significantly impact the results.
• Need for Large Sample Size: The pooling of all experimental units into one extensive set necessitates a larger sample size, potentially leading to increased time, cost, and resource investment.
• Inefficiency in Some Cases: CRD might demonstrate statistical inefficiency with significant within-treatment group variability . In such cases, other designs that account for this variability may offer enhanced efficiency.

Differentiating CRD from other research design methods

CRD stands out in the realm of research designs due to its foundational simplicity. While its essence lies in the random assignment of experimental units to treatments without any systematic bias, other designs introduce varying layers of complexity tailored to specific experimental needs.

For instance, consider the Randomized Block Design (RBD) . Unlike the straightforward approach of CRD, RBD divides experimental units into homogenous blocks, based on known sources of variability, before assigning treatments. This method is especially useful when there's an identifiable source of variability that researchers wish to control for. Similarly, the Latin Square Design , while also involving random assignment, operates on a grid system to simultaneously control for two lurking variables , adding another dimension of complexity not found in CRD.

Factorial Design investigates the effects and interactions of multiple independent variables. This design can reveal interactions that might be overlooked in simpler designs. Then there's the Crossover Design , often used in medical trials. Unlike CRD, where each unit experiences only one treatment, in Crossover Design, participants receive multiple treatments over different periods, allowing each participant to serve as their own control.

The choice of research design, whether it be CRD, RBD, Latin Square, or any of the other methods available, is fundamentally guided by the nature of the research question , the characteristics of the experimental units, and the specific objectives the study aims to achieve. However, it's the inherent simplicity and flexibility of CRD that often makes it the go-to choice, especially in scenarios with many units or treatments, where intricate stratification or blocking isn't necessary.

Let us further explore the advantages and disadvantages of each method.

While CRD's simplicity and flexibility make it a popular choice for many research scenarios, the optimal design depends on the specific needs, objectives, and contexts of the study. Researchers must carefully consider these factors to select the most suitable research design method.

The role of CRD in mitigating extraneous variables

Within the framework of experimental research, extraneous variables persistently challenge the validity of findings, potentially compromising the established relationship between independent and dependent variables . CRD is a methodological safeguard that systematically addresses these extraneous variables. Below, we describe specific types of extraneous variables and how CRD counteracts their potential influence:

• Definition: Variables that induce variance in the dependent variable, yet are not of primary academic interest. While they don't muddle the relationship between the primary variables, their presence can augment within-group variability, reducing statistical power.
• CRD's Countermeasure: Through the mechanism of random assignment, CRD ensures an equitably distributed influence of nuisance variables across all experimental conditions. This distribution, theoretically, leads to mutual nullification of their effects when assessing the efficacy of treatments.
• Definition: Variables not explicitly incorporated within the study design but can influence its outcomes. Their impact often manifests post-hoc, rendering them alternative explanations for observed phenomena.
• CRD's Countermeasure: Random assignment intrinsic to CRD assures a uniform distribution of these lurking variables across experimental conditions. This diminishes the probability of them systematically influencing one group, thus safeguarding the experiment's conclusions.
• Definition: Variables that not only influence the dependent variable but also correlate with the independent variable. Their simultaneous influence can mislead interpretations of causality.
• CRD's Countermeasure: The tenet of random assignment inherent in CRD ensures an equitable distribution of potential confounders among groups. This bolsters confidence in attributing observed effects predominantly to the experimental treatments.
• Definition: Deliberately held constant to ensure that they do not introduce variability into the experiment. They are intentionally kept constant to preserve experimental integrity.
• CRD's Countermeasure: While CRD focuses on randomization, the nature of the design inherently assumes that controlled variables remain constant across all experimental units. By maintaining these constants, CRD ensures that the focus remains solely on the treatment effects, further validating the experiment's findings.

The foundational principle underpinning the Completely Randomized Design—randomization—serves as a bulwark against the influences of extraneous variables. By uniformly distributing these variables across experimental conditions, CRD enhances the validity and reliability of experimental outcomes. However, researchers should exercise caution and continuously evaluate potential extraneous influences, even in randomized designs.

Selecting the independent variable

The selection of the independent variable is crucial for research design . This pivotal step not only shapes the direction and quality of the research but also underpins the understanding of causal relationships within the studied system, influencing the dependent variable or response. When choosing this essential component of experimental design , several critical considerations emerge:

• Relevance: Paramount to the success of the experiment is the variable's direct relevance to the research query. For instance, in a botanical study of phototropism, the light's intensity or duration would naturally serve as the independent variable.
• Measurability: The chosen variable should be quantifiable or categorizable, enabling distinctions between its varying levels or types.
• Controllability: The research environment must allow for steadfast control over the variable, ensuring extraneous influences are kept at bay.
• Ethical Considerations: In disciplines like social sciences or medical research, it's vital to consider the ethical implications . The chosen variable should withstand ethical scrutiny, safeguarding the well-being and rights of participants.

Identifying the independent variable necessitates a methodical and structured approach where each step aligns with the overarching research objective:

• Review Literature: Thoroughly review existing literature to provide invaluable insights into past research and highlight unexplored areas.
• Define the Scope: Clearly delineating research boundaries is crucial. For example, when studying dietary impacts on metabolic health, the variable could span from diet types (like keto, vegan, Mediterranean) to specific nutrients.
• Determine Levels of the Variable: This involves understanding the various levels or categories the independent variable might have. In educational research, one might look beyond simply "innovative vs. conventional methods" to a broader range of teaching techniques.
• Consider Potential Outcomes: Anticipating possible outcomes based on variations in the independent variable is beneficial. If potential outcomes seem too vast, the variable might need further refinement.

In academic discourse, while CRD is praised for its rigor and clarity, the effectiveness of the design relies heavily on the meticulous selection of the independent variable. Making this choice with thorough consideration ensures the research offers valuable insights with both academic and wider societal implications.

Applications of CRD

CRD has found wide and varied applications in several areas of research. Its versatility and fundamental simplicity make it an attractive option for scientists and researchers across a multitude of disciplines.

CRD in agricultural research

Agricultural research was among the earliest fields to adopt the use of Completely Randomized Design. The broad application of CRD within agriculture not only encompasses crop improvement but also the systematic analysis of various fertilizers, pesticides, and cropping techniques. Agricultural scientists leverage the CRD framework to scrutinize the effects on yield enhancement and bolstered disease resistance. The fundamental randomization in CRD effectively mitigates the influence of nuisance variables such as soil variations and microclimate differences, ensuring more reliable and valid experimental outcomes.

Additionally, CRD in agricultural research paves the way for robust testing of new agricultural products and methods. The unbiased allocation of treatments serves as a solid foundation for accurately determining the efficacy and potential downsides of innovative fertilizers, genetically modified seeds, and novel pest control methods, contributing to informed decision-making and policy formulation in agricultural development.

However, the limitations of CRD within the agricultural context warrant acknowledgment. While it offers an efficient and straightforward approach for experimental design, CRD may not always capture spatial variability within large agricultural fields adequately. Such unaccounted variations can potentially skew results, underscoring the necessity for employing more intricate experimental designs, such as the Randomized Complete Block Design (RCBD), where necessary. This adaptation enhances the reliability and generalizability of the research findings, ensuring their applicability to real-world agricultural challenges.

CRD in medical research

The fields of medical and health research substantially benefit from the application of Completely Randomized Design, especially in executing randomized control trials. Within this context, participants, whether patients or others, are randomly assigned to either the treatment or control groups. This structured random allocation minimizes the impact of extraneous variables, ensuring that the groups are comparable. It fortifies the assertion that any discernible differences in outcomes are genuinely attributable to the treatment being analyzed, enhancing the robustness and reliability of the research findings.

CRD's randomized nature in medical research allows for a more objective assessment of varied medical treatments and interventions. By mitigating the influence of extraneous variables, researchers can more accurately gauge the effectiveness and potential side effects of novel medical approaches, including pharmaceuticals and surgical techniques. This precision is crucial for the continual advancement of medical science, offering a solid empirical foundation for the refinement of treatments that improve health outcomes and patient quality of life.

However, like other fields, the application of CRD in medical research has its limitations. Despite its effectiveness in controlling various factors, CRD may not always consider the complexity of human health conditions where multiple variables often interact in intricate ways. Hence, while CRD remains a valuable tool for medical research, it is crucial to apply it judiciously and alongside other research designs to ensure comprehensive and reliable insights into medical treatments and interventions.

CRD in industrial engineering

In industrial engineering, Completely Randomized Design plays a significant role in process and product testing, offering a reliable structure for the evaluation and improvement of industrial systems. Engineers often employ CRD in single-factor experiments to analyze the effects of a particular factor on a certain outcome, enhancing the precision and objectivity of the assessment.

For example, to discern the impact of varying temperatures on the strength of a metal alloy, engineers might utilize CRD. In this scenario, the different temperatures represent the single factor, and the alloy samples are randomly allocated to be tested at each designated temperature. This random assignment minimizes the influence of extraneous variables, ensuring that the observed effects on alloy strength are primarily attributable to the temperature variations.

CRD's implementation in industrial engineering also assists in the optimization of manufacturing processes. Through random assignment and structured testing, engineers can effectively evaluate process parameters, such as production speed, material quality, and machine settings. By accurately assessing the influence of these factors on production efficiency and product quality, engineers can implement informed adjustments and enhancements, promoting optimal operational performance and superior product standards. This systematic approach, anchored by CRD, facilitates consistent and robust industrial advancements, bolstering overall productivity and innovation in industrial engineering.

Despite these advantages, it's crucial to acknowledge the limitations of CRD in industrial engineering contexts. The design is efficient for single-factor experiments but may falter with experiments involving multiple factors and interactions, common in industrial settings. This limitation underscores the importance of combining CRD with other experimental designs. Doing so navigates the complex landscape of industrial engineering research, ensuring insights are comprehensive, accurate, and actionable for continuous innovation in industrial operations.

CRD in quality control analysis

Completely Randomized Design is also beneficial in quality control analysis, where ensuring the consistency of products is paramount.

For instance, a manufacturer keen on minimizing product defects may deploy CRD to empirically assess the effectiveness of various inspection techniques. By randomly assigning different inspection methods to identical or similar production batches, the manufacturer can gather data regarding the most effective techniques for identifying and mitigating defects, bolstering overall product quality and consumer satisfaction.

Furthermore, the utility of CRD in quality control extends to the analysis of materials, machinery settings, or operational processes that are pivotal to final product quality. This design enables organizations to rigorously test and compare assorted conditions or settings, ensuring the selection of parameters that optimize both quality and efficiency. This approach to quality analysis not only bolsters the reliability and performance of products but also significantly augments the optimization of organizational resources, curtailing wastage and improving profitability.

However, similar to other CRD applications, it is crucial to understand its limitations. While CRD can significantly aid in the analysis and optimization of various aspects of quality control, its effectiveness may be constrained when dealing with multi-factorial scenarios with complex interactions. In such situations, other experimental designs, possibly in tandem with CRD, might offer more robust and comprehensive insights, ensuring that quality control measures are not only effective but also adaptable to evolving industrial and market demands.

Future applications and emerging fields for CRD

The breadth of applications for Completely Randomized Design continues to expand. Emerging fields such as data science, business analytics, and environmental studies are increasingly recognizing the value of CRD in conducting reliable and uncomplicated experiments. In the realm of data science, CRD can be invaluable in assessing the performance of different algorithms, models, or data processing techniques. It enables researchers to randomize the variables, minimizing biases and providing a clearer understanding of the real-world applicability and effectiveness of various data-centric solutions.

In the domain of business analytics, CRD is paving the way for robust analysis of business strategies and initiatives. Businesses can employ CRD to randomly assign strategies or processes across various departments or teams, allowing for a comprehensive assessment of their impact. The insights from such assessments empower organizations to make data-driven decisions, optimizing their operations, and enhancing overall productivity and profitability. This approach is particularly crucial in the business environment of today, characterized by rapid changes, intense competition, and escalating customer expectations, where informed and timely decision-making is a key determinant of success.

Moreover, in environmental studies, CRD is increasingly being used to evaluate the impact of various factors on environmental health and sustainability. For example, researchers might use CRD to study the effects of different pollutants, conservation strategies, or land use patterns on ecosystem health. The randomized design ensures that the conclusions drawn are robust and reliable, providing a solid foundation for the development of policies and initiatives. As environmental concerns continue to mount, the role of reliable experimental designs like CRD in facilitating meaningful research and informed policy-making cannot be overstated.

Planning and conducting a CRD experiment

A CRD experiment involves meticulous planning and execution, outlined in the following structured steps. Each phase, from the preparatory steps to data collection and analysis, plays a pivotal role in bolstering the integrity and success of the experiment, ensuring that the findings stand as a valuable contribution to scientific knowledge and understanding.

• Selecting Participants in a Random Manner: The heart of a CRD experiment is randomness. Regardless of whether the subjects are human participants, animals, plants, or objects, their selection must be truly random. This level of randomness ensures that every participant has an equal likelihood of being assigned to any treatment group, which plays a crucial role in eliminating selection bias.
• Understanding and Selecting the Independent Variable: This is the variable of interest – the one that researchers aim to manipulate to observe its effects. Identifying and understanding this factor is pivotal. Its selection depends on the experiment's primary research question or hypothesis , and its clear definition is essential to ensuring the experiment's clarity and success.
• The Process of Random Assignment in Experiments: Following the identification of subjects and the independent variable, researchers must randomly allocate subjects to various treatment groups. This process, known as random assignment, typically involves using random number generators or other statistical tools , ensuring that the principle of randomness is upheld.
• Implementing the Single-factor Experiment: After random assignment, researchers can launch the main experiment. At this stage, they introduce the independent variable to the designated treatment groups, ensuring that all other conditions remain consistent across groups. The goal is to make certain that any observed effect or change is attributed only to the manipulation of the independent variable.
• Data Cleaning and Preparation: The first step post-collection is to prepare and clean the data . This process involves rectifying errors, handling missing or inconsistent data, and eradicating duplicates. Employing tools like statistical software or languages such as Python and R can be immensely helpful. Handling outliers and maintaining consistency throughout the dataset is essential for accurate subsequent analysis.
• Statistical Analysis Methods: The next step involves analyzing the data using appropriate statistical methodologies, dependent on the nature of the data and research questions . Analysis can range from basic descriptive statistics to complex inferential statistics or even advanced statistical modeling.
• Interpreting the Results: Analysis culminates in the interpretation of results, wherein researchers draw conclusions based on the statistical outcomes. This stage is crucial in CRD, as it determines if observed effects can be attributed to the independent variable's manipulation or if they occurred purely by chance. Apart from statistical significance, the practical implications and relevance of the results also play a vital role in determining the experiment's success and potential real-world applications.

Navigating common challenges in CRD

While the Completely Randomized Design offers numerous advantages, researchers often encounter specific challenges when implementing it in real-world experiments. Recognizing these challenges early and being prepared with strategies to address them can significantly improve the integrity and success of the CRD experiment. Let's delve into some of the most common challenges and explore potential solutions:

• Lack of Homogeneity: One foundational assumption of CRD is the homogeneity of experimental units . However, in reality, there may be inherent variability among units. To mitigate this, researchers can use stratified sampling or consider employing a randomized block design.
• Improper Randomization: The essence of CRD is randomization. However, it's not uncommon for some researchers to inadvertently introduce biases during the assignment. Utilizing computerized random number generators or statistical software can help ensure true randomization.
• Limited Number of Experimental Units: Sometimes, the available experimental units might be fewer than required for a robust experiment. In such cases, using a larger number of replications can help, albeit at the cost of increased resources.
• Extraneous Variables: These external factors can influence the outcome of an experiment. They make it hard to attribute observed effects solely to the independent variable. Careful experimental design, pre-experimental testing, and post-experimental analysis can help identify and control these extraneous variables.
• Overlooking Practical Significance: Even if a CRD experiment yields statistically significant results, these might not always be practically significant. Researchers need to assess the real-world implications of their findings, considering factors like cost, feasibility, and the magnitude of observed effects.
• Data-related Challenges: From missing data to outliers, data-related issues may skew results. Regular data cleaning, rigorous validation, and employing robust statistical methods can help address these challenges.

While CRD is a powerful tool in experimental research, its successful implementation hinges on the researcher's ability to anticipate, recognize, and navigate challenges that might arise. By being proactive and employing strategies to mitigate potential pitfalls, researchers can maximize the reliability and validity of their CRD experiments, ensuring meaningful and impactful results.

In summary, the Completely Randomized Design holds a pivotal place in the field of research owing to its simplicity and straightforward approach. Its essence lies in the unbiased random assignment of experimental units to various treatments, ensuring the reliability and validity of the results. Although it may not control for other variables and often requires larger sample sizes, its ease of implementation frequently outweighs these drawbacks, solidifying it as a preferred choice for researchers across many fields.

Looking ahead, the future of CRD remains bright. As research continues to evolve, we anticipate the integration of CRD with more sophisticated design techniques and advanced analytical tools. This synergy will likely enhance the efficiency and applicability of CRD in varied research contexts, perpetuating its legacy as a fundamental research design method. While other designs might offer more control and complexity, the fundamental simplicity of CRD will continue to hold significant value in the rapidly evolving research landscape.

Moving forward, it is imperative to champion continuous learning and exploration in the field of CRD. Engaging in educational opportunities, staying abreast of the latest research and advancements, and actively participating in pertinent discussions and forums can markedly enrich understanding and expertise in CRD. Embracing this ongoing learning journey will not only bolster individual research skills but also make a significant contribution to the broader scientific community, fueling innovation and discovery in numerous fields of study.

Header image by Alex Shuper .

Chapter 6: Experimental Research

6.2 experimental design, learning objectives.

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 college students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assign participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. Table 6.2 “Block Randomization Sequence for Assigning Nine Participants to Three Conditions” shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Table 6.2 Block Randomization Sequence for Assigning Nine Participants to Three Conditions

Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Treatment and Control Conditions

Between-subjects experiments are often used to determine whether a treatment works. In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008).

Placebo effects are interesting in their own right (see Note 6.28 “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Figure 6.2 Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This is what is shown by a comparison of the two outer bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a waitlist control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?”

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999). There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002). The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

Research has shown that patients with osteoarthritis of the knee who receive a “sham surgery” experience reductions in pain and improvement in knee function similar to those of patients who receive a real surgery.

Army Medicine – Surgery – CC BY 2.0.

Within-Subjects Experiments

In a within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in carryover effects. A carryover effect is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This is called a context effect . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is counterbalancing , which means testing different participants in different orders. For example, some participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 Is “Larger” Than 221

Researcher Michael Birnbaum has argued that the lack of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this, he asked one group of participants to rate how large the number 9 was on a 1-to-10 rating scale and another group to rate how large the number 221 was on the same 1-to-10 rating scale (Birnbaum, 1999). Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this is because participants spontaneously compared 9 with other one-digit numbers (in which case it is relatively large) and compared 221 with other three-digit numbers (in which case it is relatively small).

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. There are many ways to determine the order in which the stimuli are presented, but one common way is to generate a different random order for each participant.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often do exactly this.

Key Takeaways

• Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.
• Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.
• Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.

Discussion: For each of the following topics, list the pros and cons of a between-subjects and within-subjects design and decide which would be better.

• You want to test the relative effectiveness of two training programs for running a marathon.
• Using photographs of people as stimuli, you want to see if smiling people are perceived as more intelligent than people who are not smiling.
• In a field experiment, you want to see if the way a panhandler is dressed (neatly vs. sloppily) affects whether or not passersby give him any money.
• You want to see if concrete nouns (e.g., dog ) are recalled better than abstract nouns (e.g., truth ).
• Discussion: Imagine that an experiment shows that participants who receive psychodynamic therapy for a dog phobia improve more than participants in a no-treatment control group. Explain a fundamental problem with this research design and at least two ways that it might be corrected.

Birnbaum, M. H. (1999). How to show that 9 > 221: Collect judgments in a between-subjects design. Psychological Methods, 4 , 243–249.

Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88.

Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590.

Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press.

• Research Methods in Psychology. Provided by : University of Minnesota Libraries Publishing. Located at : http://open.lib.umn.edu/psychologyresearchmethods . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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• Fixed and Random Factors: Key Concepts in Research Design

The Role of Fixed and Random Factors in Effective Statistical Research Design

Designing a research study requires careful consideration of various factors that influence your data analysis and results. Among these, fixed and random factors play a crucial role in determining how you approach your study design and statistical analysis . Whether you're working on a research project or trying to solve your statistics assignment , understanding these concepts is essential for producing accurate and meaningful results. Fixed factors refer to variables with specific, predetermined levels that are of particular interest, while random factors involve levels that are randomly selected from a larger population, allowing for broader generalizations. This blog explores these concepts in depth, providing you with the knowledge needed to apply them effectively in your research.

Understanding Fixed and Random Factors

In statistical study design, factors are the variables that researchers manipulate or observe to determine their effect on the dependent variable. These factors can be classified into two categories: fixed and random.

• Fixed Factors: A fixed factor is one where the levels of the factor are specifically chosen and of interest in the study. The researcher is typically interested in comparing these levels directly and the conclusions drawn from the analysis are only applicable to the specific levels included in the study.
• Random Factors: A random factor, on the other hand, includes levels that are randomly selected from a larger population. The primary interest is in generalizing the results beyond the specific levels studied, to the broader population from which they were drawn.

When to Use Fixed or Random Factors

The choice between fixed and random factors depends on the goals of your study.

• Fixed Factors: Use fixed factors when you are interested in understanding the effects of specific, predetermined levels of a variable. For example, if you want to compare the efficacy of three different teaching methods, and you are only interested in those three methods, the teaching method would be considered a fixed factor.
• Random Factors: Random factors are used when the levels of a variable are randomly sampled from a larger population, and you want to generalize the results to the entire population. For instance, if you were studying the effect of different schools on student performance and the schools were randomly selected from all schools in a region, then the school variable could be considered a random factor.

Designing a Study with Fixed Factors

When designing a study with fixed factors, it’s essential to clearly define the levels of each factor and ensure that these levels are consistently applied throughout the research. The analysis should focus on comparing these specific levels to determine if there is a significant effect.

Example: Suppose you are studying the effect of different training programs on employee productivity. You have three distinct programs that you want to compare. Since you are specifically interested in these three programs, they are considered fixed factors.

1. Step 1: Define the Factor Levels

In this step, clearly identify and define the specific levels of the fixed factor that will be included in your study. For instance, if you are examining the effects of different training programs, specify each program as a distinct level. Ensure these levels are relevant to your research question and are consistently applied throughout the study. This precise definition is crucial for accurately comparing the outcomes associated with each level and drawing valid conclusions.

2. Step 2: Ensure Consistency in Application

To maintain the integrity of your study with fixed factors, apply each level of the fixed factor consistently across all experimental conditions. Ensure that every participant or observation is exposed to only one level of the fixed factor, avoiding any overlap or variation. Consistency in application is crucial for accurately assessing the effects of the fixed factor and ensuring that any observed differences are due to the factor itself, not variations in how it was applied.

3. Step 3: Analyze the Data

Once the study is designed and data collected, it’s crucial to analyze the data using appropriate statistical methods. For fixed factors, conduct an ANOVA to assess whether there are significant differences between the levels. Ensure that the analysis accounts for any potential interactions between factors. Properly interpreting these results will help determine the impact of each fixed factor on the dependent variable and provide insights into the effectiveness of the study conditions.

4. Step 4: Interpret the Results

Interpreting results from a study with fixed factors involves comparing the outcomes across different levels of the factor to determine if there are significant differences. Analyze the statistical outputs, such as p-values and confidence intervals, to assess the significance of the effects. Ensure that the conclusions drawn are supported by the data and reflect the practical implications of the findings. Clear interpretation helps in understanding the impact of each fixed factor on the dependent variable.

Designing a Study with Random Factors

Designing a study with random factors requires a different approach. Here, the focus is on ensuring that the levels of the factor are randomly selected and represent a broader population.

Example: Imagine you are researching the impact of different classroom environments on student engagement. If you randomly select classrooms from a large number of schools, the classroom variable is a random factor.

1. Step 1: Random Selection of Factor Levels

When designing a study with random factors, start by randomly selecting the levels from a larger population to ensure they are representative. This random selection helps mitigate biases and allows your findings to generalize to a broader context. Use random sampling techniques to choose these levels, ensuring that each level has an equal chance of being included. This approach enhances the validity of your study and supports more robust and generalizable conclusions.

2. Step 2: Incorporate Randomness into the Study Design

To incorporate randomness, ensure that the selection of levels for random factors is truly random and representative of the broader population. Implement random sampling techniques to avoid bias and ensure that your sample reflects the diversity of the population. Integrate these random levels into your study design and use statistical methods, such as mixed-effects models, to account for the variability introduced by these random factors. This enhances the generalizability and validity of your findings.

3. Step 3: Use Mixed-Effects Models

When working with random factors, apply mixed-effects models to account for both fixed and random effects. These models help analyze data by incorporating variability from random factors, such as differences across schools or subjects. Use software tools to fit the model, estimate variance components, and assess the impact of both fixed and random factors. Mixed-effects models provide a comprehensive analysis, allowing for more accurate interpretation of how random factors influence the overall outcomes.

4. Step 4: Generalize the Findings

When generalizing findings from a study with random factors, extend the results to the broader population from which the random levels were sampled. Assess how the random factor contributes to overall variability and ensure that your conclusions are applicable beyond the specific sample. Consider the representativeness of your random levels and the external validity of your results. Proper generalization provides valuable insights that can be applied to similar contexts or populations.

Formulating Hypotheses for Fixed and Random Factors

One of the key steps in any statistical analysis is formulating the null and alternative hypotheses. These hypotheses will differ depending on whether you are dealing with fixed or random factors.

• Fixed Factors: The null hypothesis typically states that there is no effect of the fixed factor on the dependent variable. For example, if you are comparing three teaching methods, the null hypothesis would be that all methods result in the same average outcome.
• Null Hypothesis (H₀): μ₁ = μ₂ = μ₃ (where μ represents the mean outcome for each teaching method).
• Random Factors: For random factors, the null hypothesis often states that the variance components associated with the random factor are zero, meaning that the levels of the random factor do not contribute to the variability in the outcome.
• Null Hypothesis (H₀): σ²₀ = 0 (where σ²₀ represents the variance component of the random factor).

Practical Applications and Considerations

When approaching assignments or research projects involving fixed and random factors, it’s important to consider the practical implications of your study design.

• Sample Size Considerations: Ensure that you have an adequate sample size to detect effects, especially when dealing with random factors, as they require larger samples to accurately estimate variance components.
• Data Collection: Pay close attention to how data is collected, ensuring that the random factors are truly random and that fixed factors are applied consistently.
• Software and Tools: Utilize statistical software like R, SPSS, or SAS that offer advanced capabilities for analyzing data with both fixed and random factors. Mixed-effects models are particularly useful for these types of analyses.
• Interpreting Results: Be cautious when interpreting results, especially with random factors, as the conclusions may extend beyond the specific data collected. Always consider the broader context of the findings.
• Reporting and Documentation: Clearly document your study design, including how factors were classified as fixed or random, and ensure transparency in how the data was analyzed. This is crucial for the reproducibility and validity of your research.

Common Challenges and Solutions

When working on statistics assignments that involve mixed-effects models, students often face challenges like misclassifying factors, inadequate sample sizes, and complexity in data analysis. Addressing these issues requires careful study design, appropriate statistical methods, and sometimes, seeking expert assistance with mixed-effects models assignments to ensure accurate and meaningful results.

Challenge: Misclassification of Factors

Misclassifying factors as fixed or random is a common issue in research design, leading to incorrect data analysis and misleading conclusions. Fixed factors should be specifically chosen and relevant to the study, while random factors are randomly selected and represent a broader population. Misclassification often occurs when researchers are unclear about the nature of their variables or lack familiarity with these concepts.

Solution: To avoid misclassification, it's crucial to clearly define your study objectives and consult relevant literature or experts in the field. Take the time to understand whether your factors should be treated as fixed or random based on the research question and the generalizability of the results. If in doubt, consider using mixed-effects models that can accommodate both fixed and random factors, providing more accurate analysis.

Challenge: Inadequate Sample Size for Random Factors

One common challenge when working with random factors in research design is having an inadequate sample size. Random factors require a sufficient number of observations to accurately estimate variance components and ensure that the results are reliable and generalizable. Small sample sizes can lead to biased estimates and reduced statistical power, making it difficult to detect true effects.

Solution: To address this issue, conduct a power analysis before data collection to determine the required sample size for your study. Power analysis helps you estimate the minimum sample size needed to achieve reliable results, considering the effect size, significance level, and desired power. Additionally, consider increasing the sample size if feasible, or utilize statistical techniques that can handle smaller sample sizes, such as bootstrapping or Bayesian methods.

Challenge: Complexity in Data Analysis

Data analysis involving fixed and random factors can be complex due to the need for advanced statistical models and the potential for intricate interactions between factors. Understanding how to appropriately apply mixed-effects models or hierarchical models can be daunting. Additionally, interpreting the results requires a strong grasp of statistical theory and the ability to manage multiple layers of variability.

Solution: To navigate this complexity, it is essential to utilize statistical software that can handle mixed-effects models, such as R, SPSS, or SAS. These tools provide built-in functions for analyzing data with both fixed and random effects, simplifying the process. Additionally, investing time in learning about these models through tutorials or seeking help from a statistician can greatly enhance your ability to perform accurate and efficient data analysis.

Challenge: Interpretation of Interaction Effects

Interpreting interaction effects between fixed and random factors can be challenging because these interactions often involve complex relationships between variables. In studies with multiple factors, the effects of one factor may depend on the levels of another, complicating the interpretation of results. For instance, when examining how different instructional methods impact performance across various grade levels, the interaction effect can reveal if certain methods work better in specific grades.

Solution: To address this challenge, use statistical software to perform detailed analyses of interaction effects. Visualize the interactions using graphs to understand how factors influence each other. Additionally, consult with statistical guidelines or seek expert help with mixed-effects models assignments if needed to ensure accurate interpretation. Properly addressing interaction effects will enhance the clarity and validity of your findings.

Successfully incorporating fixed and random factors into your research design is key to producing reliable and generalizable results. Whether you’re dealing with specific variables of interest or working with randomly selected samples, understanding these concepts will enhance the quality of your analysis. By correctly identifying and applying fixed and random factors, you can ensure that your study design is robust and your conclusions are sound. This knowledge is not only crucial for academic research but also essential when you need to solve your statistics assignment. As you continue to work on similar projects, remember that the clarity and accuracy of your study design significantly impact the outcomes of your research. With the insights gained from this blog, you’re better equipped to tackle complex statistical challenges and solve your statistics assignment effectively.

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• Open access
• Published: 17 August 2024

TLR9 activation in large wound induces tissue repair and hair follicle regeneration via γδT cells

• Xinhui Li 1 , 2   na1 ,
• Tiantian An 1 , 2   na1 ,
• Yang Yang 1 , 2 ,
• Zhaoyu Xu 1 , 2 ,
• Shuaidong Chen 1 , 2 ,
• Zumu Yi 1 , 2 ,
• Chen Deng 1 , 2 ,
• Feng Zhou 1 , 2 ,
• Yi Man   ORCID: orcid.org/0000-0002-7546-3486 1 , 2 &
• Chen Hu   ORCID: orcid.org/0000-0001-6826-9637 1 , 2  

Cell Death & Disease volume  15 , Article number:  598 ( 2024 ) Cite this article

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• Inflammation
• Mucosal immunology
• Regeneration

The mechanisms underlying tissue repair in response to damage have been one of main subjects of investigation. Here we leverage the wound-induced hair neogenesis (WIHN) models in adult mice to explore the correlation between degree of damage and the healing process and outcome. The multimodal analysis, in combination with single-cell RNA sequencing help to explore the difference in wounds of gentle and heavy damage degrees, identifying the potential role of toll-like receptor 9 (TLR9) in sensing the injury and regulating the immune reaction by promoting the migration of γδT cells. The TLR9 deficient mice or wounds injected with TLR9 antagonist have greatly impaired healing and lower WIHN levels. Inhibiting the migration of γδT cells or knockout of γδT cells also suppress the wound healing and regeneration, which can’t be rescued by TLR9agonist. Finally, the amphiregulin (AREG) is shown as one of most important effectors secreted by γδT cells and keratinocytes both in silicon or in the laboratory, whose expression influences WIHN levels and the expression of stem cell markers. In total, our findings reveal a previously unrecognized role for TLR9 in sensing skin injury and influencing the tissue repair and regeneration by modulation of the migration of γδT cells, and identify the TLR9-γδT cells-areg axis as new potential targets for enhancing tissue regeneration.

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Introduction.

Scar-free healing and functional regeneration after tissue injury have long been desired goals. However, the fundamental logics that regulate the initiation and process of tissue repair and regeneration remains unclear. Damage associated molecular patterns (DAMPs) serve as central players of signaling molecules that indicate tissue injury, and involve in the initiating of inflammation and regenerative processes [ 1 ].

The intricate signaling transduction pathway between damage-associated molecular patterns (DAMPs) and Toll-Like receptor 2 (TLR2) has shown to stimulate the proliferation and differentiation of renal tubular progenitor cells, thereby facilitating the regeneration and repair of renal tubules [ 2 ]. However, advancing research on various types of DAMPs and pattern recognition receptors (PPRs) has revealed that their involvement in tissue repair or regeneration is not invariably advantageous. Excessive responses triggered by DAMPs have been implicated in exacerbating left ventricular remodeling post-myocardial infarction, leading to inflammatory infiltration extending beyond the infarcted myocardium, activation of pro-apoptotic pathways, further loss of myocardial cells, escalated matrix degradation, compromised collagen deposition, and the promotion of scar formation [ 3 ].

Skin and its appendages have served as pivotal model systems in regenerative medicine due to their remarkable regenerative capacity, often yielding novel insights and principles. While conventional wisdom suggests that skin wounds primarily heal through regeneration during embryonic or neonatal stages, a remarkable ability for hair follicle (HF) regeneration in adult mice and rabbits has been observed in substantial full-thickness wound centers within the context of wound-induced hair follicle neogenesis (WIHN) models [ 4 , 5 ]. Notably, WIHN exclusively manifests in sufficiently large wounds, with small wounds failing to elicit regenerative responses. This phenomenon prompts contemplation on the interplay between pathogen-associated molecular patterns (PAMPs), inflammatory cascades, and regenerative outcomes. It raises the question of whether the generation of hair follicles in WIHN wounds stems from more extensive skin damage, resulting in heightened release of DAMPs and activation of PPRs. The precise mechanisms underlying how these factors modulate immune and reparative responses, impact stem cell fate, and influence hair follicle neogenesis remain incompletely elucidated.

While some literature suggests that excessive or prolonged inflammatory reactions may culminate in fibrosis [ 6 , 7 , 8 ], others propose that a robust regenerative response can be sparked by heightened inflammatory signals [ 9 , 10 ]. Hence, it beckons exploration into how the interplay of DAMPs and PPRs with inflammatory responses alters under conditions of exacerbated tissue damage and the subsequent impact of this heightened inflammatory milieu on stem cells and other cells mobilization.

Our prior investigations have demonstrated that the introduction of aligned extracellular matrix (ECM) scaffold yields enhanced regeneration of hair follicles, concomitant with an immunomodulatory effect [ 11 , 12 ]. Additionally, our another study has delved into the essential role played by adaptive immune cells in the process of wound healing and regeneration [ 12 ]. However, further research is warranted to unravel how diverse immune cells respond to signals emanating from tissue damage, initiating and coordinating tissue repair and particularly regeneration-related responses. To mitigate the potential confounding factors arising from variations in wound sizes when comparing large and small wounds, we crafted several wound models with equated surface areas but differing degrees of tissue damage.

We firstly revealed the association between the extent of tissue damage with the number of regenerated hair follicles and the expression levels of Toll-like Receptor 9 (TLR9). The role of TLR9 in mediating the correlation between degrees of injury and WIHN levels was investigated by comparing mice with enhanced or repressed activation levels of TLR9 or knockout of TLR9. Then the single cell RNA sequencing, were conducted and revealed the comprehensive immune reaction changes under the enhanced activation of TLR9. Of note, the number of γδT cells greated elevated with higher expression of pertinent chemokines in TLR9 activated wounds. Inhibiting the migration or knockout of TCRδ eliminated the function of TLR9. Finally, we also figured out the effector Areg, secreted by γδT and keratinocytes, played a role in promoting the expression of stem cell markers and regeneration markers including Twist1, Wnt7b, Ctnnb1 and so on. Collectively, our study delineates a comprehensive process and offers fresh perspectives on how varying levels of damage-associated molecular patterns modulate the immune response through their interaction with PPRs, ultimately shaping the behavior of stem cells pertinent to regeneration.

Hair follicle neogenesis was positively correlated with extent of tissue damage, and this process was dependent on TLR9

We refer to the classic WIHN model, which involves creating a large wound on the back of mice and allowing it to heal naturally without splinting. In order to trigger and explore signals associated with tissue damage as strongly as possible, we enlarged the diameter of the wound to 1.8 cm. To eliminate the asynchrony of wound healing caused by different healing speed under different situations, we used the second day after re-epithelialization (SD2) as a detection window phase for hair follicle regeneration, as hair follicles begin to form at around SD 0-7 days reported in relevant literatures (Fig. 1A ) [ 13 , 14 ]. To investigate the impact of wound severity on regeneration outcomes, we made the enhanced injury on large wound (WT_eLW) model in study of Nelson et al. [ 15 ] by adding 6–8 short radial incisions around the circular wound, which increased the degree of wound damage without changing the wound area. Since many studies suggest that many factors including biomechanical forces/tension and tissue electric current etc. may also affect the degree of regeneration [ 16 ], we designed three different types of enhanced large wound models to reduce the interference of these confounding factors (Fig. S1A ). By comparing various wound types, we found multiple types of wounds could enhance hair follicle regeneration compared with standard large wounds (WT_LW) at 28 days post wounded (PWD28) (Fig. S1B ).

A Workflow for evaluating large-scale wound healing. B Surgical processes for skin wound models. C Representative H&E images and appearances of WT_LW and WT_eLW at PWD28. D GO enrichement terms up-regulated in WT_eLW compared with WT_LW. Yellow arrows are terms related with skin and hair follicle development. Red arrows are terms related with toll-like receptor signaling pathway. E The differential expressed genes between WT_LW and WT_eLW. F The different types of skin wounds and reagents dropwise added in wounds. G The healing speed and residual wound area (%) in groups of standard wounds. Wound were treated with TLR9agonist (ODN2395, Invivogene, 4 μg per time per mouse) at PWD 0,3,6 (TLR9agonist_LW); Ctrl ODN (ODN2395 control, Invivogene, 4 μg per time per mouse) (WT_LW); TLR9antagonist (TLR9antagonist_LW) (ODN2088, Invivogene, 4 μg per time per mouse); or wound in TLR9 −/− mice treated with Ctrl ODN (ODN2088 control, Invivogene, 4 μg per time per mouse) (TLR9 −/−_LW) respectively. n  = 4–8 for each group. H The residual defects area (%) in enhanced wounds. Wounds were treated with Ctrl ODN (ODN2088 control, Invivogene, 4 μg per time per mouse) (WT_eLW); or TLR9antagonist (TLR9antagonist_eLW) (ODN2088, Invivogene, 4 μg per time per mouse) at PWD0,3,6 respectively. n  = 4–8 for each group. I The number of regenerated hair follicles in PWD28. n  = 3 for each group. All error bars ±SD.

To further explore the relationship between injury severity and hair follicles, and to dissect the biological effects of enhanced injury on the skin, we collected the tissues at wound center of WT_eLW and WT_LW at SD2 and conducted bulk-RNA sequencing (bulk-RNA seq) (Figs. 1A and S1C ).The gene ontology (GO) enrichment analysis showed that multiple TLRs family pathways were upregulated in WT_eLW compared with WT_LW, especially TLR7 and TLR9, as observed by qRT-PCR (Figs. 1D, E and S1D–G ).

Toll-like receptor (TLR) family, as one of the important pattern recognition receptor families in the in–nate immune system, has long been believed to play a role in sensing infections and tissue damage. Members of the TLR family such as TLR3, 7, and 9 can be activated by signals related to tissue damage [ 17 ]. Therefore, based on the significant upregulation of the Toll-like receptor family in RNA-seq data, we speculate that greater tissue damage promotes the activation of the Toll-like receptor family. Additionally, GO enrichment analysis suggests that the TLR7 and TLR9 signaling pathways are the most significantly upregulated TLR signaling pathways. Overall, there have been numerous studies investigating the role of TLR7 in skin wounds, inflammation, and development because of its significant role in various skin inflammations, allergies, autoimmune diseases and so on [ 18 , 19 ]. However, research on the role of TLR9 in skin, especially in wound repair and regeneration, is rare. Some studies have found that TLR9 is upregulated after tissue damage and promotes wound repair [ 20 ]; however, other studies suggest that TLR9 may promote the transformation of fibroblasts into myofibroblasts, leading to fibrosis [ 21 , 22 ]. Therefore, overall, there is less research on TLR9, and conclusions about its characteristics are not yet clear. How the activation of TLR9 affects the wound healing process, especially in the late stage, and whether it can influence the immune microenvironment to control the reactivation of embryonic-like programs related to hair follicle development, remains unknown.

To explore the effects of TLR9 on wound healing and HF regeneration, we dropwise added the TLR9 agonist ODN 2395 (4 μg per injection, InvivoGen, America) or the TLR9 antagonist ODN 2088 (4 μg per injection, InvivoGen, America) and TLR9 −/− mice (C57BL/6Smoc-Tlr9em1Smoc) (Cat. NO. NM-KO-190168, Shanghai Southern Model Biological Co., LTD) to compare the healing speed and regeneration results. The workflow for evaluating large wound healing is summarized in Fig. 1F . The TLR9 agonist significantly accelerated the healing speed, while inhibiting or knock out of TLR9 dampened wound healing greatly both in WT_LW or WT_eLW (Fig. 1G, H ). In addition, the number of hair follicles in PWD28 was increased by 3.6 times in TLR9 activated standard wounds (TLR9agonist_LW) and 3.9 times in wounds with enhanced damage (WT_eLW) (Fig. 1I ). The injection of TLR9 antagonist eliminated the enhanced hair follicle regeneration in WT_eLW (Fig. 1I ), indicating the positive correlation between hair follicle regeneration with extent of tissue damage was dependent on TLR9. Therefore, we inferred that TLR9 might play an essential role in wound healing and HF regeneration in large wounds as a wound-related sentinel.

The activation of TLR9 was from the enhanced release of mtDNA in wounds with larger extent of damage

After we proved the effects of TLR9 activation in wound healing, we want to figure out the sources of enhanced expression of TLR9 in WT-eLW. TLR9, as one of the main nucleic acid-sensing toll-like receptors, discovered to recognize unmethylated CpG DNA, typical of bacterial DNA and mtDNA [ 23 ]. MtDNA, being structurally analogous to bacterial DNA, is characterized by the presence of numerous unmethylated DNA regions referred to as CpG islands. Mitochondrial DNA is generally known as an immune stimulatory molecule and is released from necrotic cells after traumatic injury or surgery to activate TLR9, stimulator of interferon genes (STING) and so forth [ 24 ]. Therefore, we guessed that larger injuries might cause stronger release of mtDNA, which affected the activation of TLR9. To test this speculation, we firstly detected the copy numbers of mtDNA in plasma of WT_LW and WT_eLW mice and found higher copy numbers of Cytochrome B (CytB) in mtDNA in larger extent of wounds (Fig. 2B ), accompanied by a higher Tlr9 mRNA in PWD14 (Fig. 2B ).

A Workflow for evaluating the release of mtDNA and activation of TLR9 in vivo. B The in vivo changes of copy numbers of mtDNA in plasma of mice and qRT-PCR mRNA expression changes of Tlr9 in standard (WT_LW) or enhanced_injury wounds (WT_eLW) and enhanced_injury wounds rejected with DNase I (15 units) (WT_eLW+DNase) at PWD 0, 3, 6. The qRT-PCR of Tlr9 was conducted in tissues collected at PWD14 of the wound center. C The in vitro detect of copy numbers of mtDNA and qRT-PCR mRNA fold changes at 24 h after being scratched. Supernate from control M0-THP1 (treated with 100 ng/ml PMA for 24 h) and M0-THP1 with 8 scratches per well in six-well plates (scratched) and scratched cells incubated with 1 μg/ml DNase I (scratched + DNase) were collected to detect the copy numbers of mtDNA. D The immunofluorescent staining of TLR9 (red), EEA1 (green) and DAPI (blue) in control M0-THP1 (treated with 100 ng/ml PMA for 24 h), control + mtDNA M0-THP1 (treated with 100 ng/ml PMA for 24 h and then added with 100 ng/ml mtDNA for 24 h), M0-THP1 with 8 scratches per well in six-well plates (scratched) and scratched cells incubated with 1 μg/ml DNase I (scratched + DNase). E The Masson staining sections and appearance of wounds for every group. The photos and sections were collected at PWD28. n  = 3 for each group. All error bars ±SD.

To more intuitively study the association between cellular damage, mtDNA, and TLR9, we simulated tissue damage through scratch experiments using adherent THP-1 cells induced by phorbol ester (PMA). We found that the copy numbers of cytochrome b (CytB) and the mRNA levels of Tlr9 increased after 24 h of cell scratch (Fig. 2C ). TLR9 is usually located in the endoplasmic reticulum of the cytoplasm and then transferred to endosomes after sensing of dsDNA [ 25 ]. Therefore, we performed immunofluorescent (IF) staining on TLR9 and the marker of endosomes, EEA1, and found that TLR9 was co-localized with EEA1 after scratch-induced cellular damage. In addition, the process was dependent on mtDNA (Fig. 2D ).

To prove mtDNA affected hair follicle regeneration through TLR9, different levels of mtDNA extracted from mouse liver was injected into mice through the vein, and at the same time the DNase I was injected into wounds at PWD0,3,6 to ablate mtDNA in another group of mice. Different doses of mtDNA (20 μg (mtDNA lo _LW) and 80 μg (mtDNA hi _LW) were chosen in this test. We found that the numbers of hair follicles decreased after clearance of mtDNA, while injecting mtDNA into vessels increased hair follicles, which was inhibited by TLR9 antagonist (Fig. 2E ).

The immune changes caused by TLR9 activation on large wound healing and HF regeneration was investigated by single-cell RNA sequencing

The activation of TLR9 has been reported to induce the inflammation and polarization of T helper 1cell (Th1), Th17/Th23, promotion of release of type I interferon (IFN), Interleukin-6 (IL-6), IL-1β so forth [ 20 , 26 ]. In order to investigate the specific characteristics influenced by the activation of TLR9 in wounds, we employed the single-cell RNA sequencing (scRNA-seq) to visually compare immune cells and inflammatory factors and cytokines under the influence of TLR9.Tissue samples were collected from the center of the wound (φ = 5 mm) on the second day after scab detachment (SD2), comprising two groups: WT_LW and TLR9agonis_LW, each consisting of six mice. These samples were subsequently subjected to analysis using the 10x scRNA-seq platform (Fig. 3A ). Following cell filtering, unsupervised clustering utilizing Seurat software assigned cells into distinct clusters based on their global gene expression patterns. This was followed by the categorization of these clusters into primary cell classes at the first level. Ten cell types were defined: T cells (TC), fibroblasts (Fib), myeloid cells (Myl), keratinocytes (Ker), pericyte cells (Perc), neural crest-derived cells (Neur), endothelial cells (Endo), other cells (Others), neutrophils (Neu), lymphatic endothelial cell (Lyen) (Fig. 3B ). The composition of each main cluster was listed so that the proportion of cells from two groups could be identified across all cell clusters. Marker genes for each main cluster were shown in the heatmap and listed in Fig. 3C . Subsequently, we performed enrichment analysis of overall gene expression in both groups. GO functional enrichment revealed significant up-regulation of many entries related to hair follicle development、epithelial tube morphogenesis, gland and appendage morphogenesis in the TLR9agonist_LW group, as well as hair follicle stem cell-related genes such as Krt17 [ 27 ], Sox9 [ 28 ], Msx2 [ 29 ], Lgr5 [ 30 ] (Fig. S3B, C ). In contrast, the WT_LW group mainly upregulated genes associated with myeloid cell differentiation, phagocytosis, WNT and TGFβ (Fig. S3C ).

A Schematic for generating scRNA-seq data from large area excisional wounds on SD2. B Clustering of all cells, showing ten subsets from the two samples. C The marker genes for each subset are listed. D The subcluster of T cells, annotations, marker genes and cell ratios of each subset. E The flow cytometry results of γδT cells in different groups. F The statistics of ratios of γδT cells in all live cells for different groups. G The GO enrichment terms in T cells of TLR9agonist_LW and WT_LW. H The healing process of WT_LW, TCRδ−/−_LW and TLR9agonist activated WT and TCRδ−/− LW. n  = 3–5 for each group. All error bars mean ± SD.

To better analyze the mechanisms underlying the effects of TLR9 activation on wound healing and HF regeneration, we first annotated the two most important stromal cell populations, keratinocytes and fibroblasts, based on previously reported markers. Keratinocytes were divided into permanent epidermis (EPI), upper and middle hair follicles and cells defined as or highly related with hair follicle stem cells (HFSC) (Fig. S3D ). The subclusters of keratinocytes were further divided and annotated according to markers reported before [ 11 , 31 , 32 , 33 ]. The GO enrichment analysis also showed genes related with hair follicle morphogenesis/hair cycle and skin development were upregulated in TLR9agonist_LW, while genes related with extracellular matrix organization and stem cell differentiation enriched in WT_LW (Fig. S3E ). Furthermore, in order to clarify the effects on keratinocytes and the resulting changes more clearly, we conducted detailed analysis of the development trajectory of keratinocytes and their interaction with immune cells, which can be seen in Figs. 5 and 6 .

Similarly, we specifically focused on the main clusters that were identified as fibroblasts and subjected them to a secondary round of clustering (Fig. S3F, G ). Fibroblasts play a crucial role as the primary mesenchymal cells in the dermal layer of the skin, and different subclusters of fibroblasts are spatially distinct with significant functional diversity. Generally, dermal fibroblasts originate from several distinct lineages: (1) the upper lineage consists of papillary fibroblasts (PF), which are in direct contact with the epidermis and contribute to the dermal component of hair follicles; (2) the lower lineage comprises reticular fibroblasts (RF), responsible for synthesizing most of the extracellular matrix (ECM) proteins, and lipo-fibroblasts (LF), which give rise to preadipocyte progenitors in the hypodermis.

Additionally, certain populations of fibroblasts play unique roles in WIHN and are therefore of particular interest to us, namely dermal papilla (DP) and dermal condensate (DC), which possess unique transcriptional characteristics along with the general fibroblast population [ 34 ]. DP is located at the base of mature hair follicles and serves as the principal signaling niche regulating hair follicle activities [ 9 , 35 ]. Dermal condensate (DC), originating from the papillary fibroblasts, is believed to be the progenitor of DP during embryonic development. In our dataset, we defined six types of fibroblasts consisting of ten subclusters based on previously established marker genes: Inhba+Prdm1+Ereg+ Fabp5+Sdc1+ representing papillary fibroblasts (PF), Pappa2+Mdk+Wnt5a+ Hhip+Vcan+ indicating dermal papilla (DP), Fst+Twist2+Smad3+ characterized dermal condensate (DC), Plac8+Gpx3+Mest+ indicating reticular fibroblasts (RF), Plin2+Ptprc+Lyz2+ indicating myeloid-derived adipocyte progenitors (Macf), and Birc5+Mki67+Tagln+ representing myofibroblasts (MF) (Fig. S3F, G ).

In the WT_LW group, the initial phase of dermal repair was mediated by the lower lineage fibroblasts, particularly the reticular fibroblasts, which were associated with the organization of extracellular matrix organization, external encapsulating structure organization, as revealed by gene ontology enrichment analysis (Fig. S3G ). In addition, the fibroblasts in WT_LW highly enriched terms of transforming growth factor beta (TGF-β) receptor signaling pathway, which was known associated with fibrosis [ 36 ]. In contrast, the TLR9agonist_LW wounds exhibited a higher proportion of upper lineage Crabp1+Prdm1+ papillary fibroblasts, which are known to have the capacity to support hair follicle initiation [ 37 , 38 ]. Interestingly, we also identified the presence of Fst+ Twist2+Smad3+ dermal condensate (DC) cells in this dataset (Fig. S3F ). During embryonic hair follicle development, Dc acts as a signaling niche that promotes epithelial placode growth and subsequently contributes to hair follicle morphogenesis [ 35 ]. In GO enrichment analysis, we observed the genes related with morphogenesis of embryonic epithelium, regulation of tumor necrosis factor (TNF) production were highly regulated in TLR9agonist_LW (Fig. S3G ), which reminded us of the positive correlation between TNF-α [ 39 ] or IL-1β [ 13 ] with hair growth in wounds. Additionally, as observed by Gay et al. [ 8 ], the WNT signaling pathway was also more significant in low-regeneration group at similar late wound healing, after the end of re-epithelialization. Given the significantly increased proportion of specific cell populations and enrichment terms in the TLR9agonist_LW group during the critical period of hair follicle regeneration, it is plausible to suggest that they may provide an adequate mesenchymal component for subsequent hair follicle formation in the TLR9-activated group.

TLRs are reported to be expressed in antigen-presenting cells, establishing a crucial link between pathogen recognition and the activation of both innate immune effector mechanisms that restrict pathogen replication and adaptive immunity initiation [ 17 ]. To further explore the impact of TLRs on the host’s overall immune response, we conducted analyses of various immune cell types using single-cell RNA sequencing.

Firstly, neutrophils, as the primary responders and critical mediators of the innate immune system, play a vital role in the recruitment and differentiation of monocytes [ 40 ], particularly during the early stages of immune response. In our scRNA sequencing data obtained from the late stage of wound healing, we observed that most subclusters of neutrophils exhibited comparable proportions in both experimental groups (Fig. S4A ). Macrophage-monocytes and dendritic cells are the main cells expressing TLRs and equipped to coordinate the activation of other immune cells as well as tissue repair [ 20 ]. Five types of macrophage-dendritic cells were identified, according to markers reported in literature [ 41 ]: Inhba+Ptgs2+ Mmp12+pro-inflammatory macrophages (PIM), Ccl8+Mrc1+ Folr2+Fcgr1+ anti-inflammatory macrophages (AIM), Irf7+Il3ra+ plasmacytoid dendritic cells (pDC), Cd74+Cd86+Cd207+Rgs1+ monocyte-derived dendritic cell1 (mDC), Cd8a + Cadm1+ Cadm3+ Clec10a+ Cpvl + conventional dendritic cell (cDC) (Fig. S4C ). As speculated, TLR9 agonist, as a type of pro-inflammatory agent, increased the presence of PIM in wounds while decreasing AIM (Fig. S4C ). Functional enrichment analysis of the two groups revealed that the TLR9 agonist upregulated numerous items related to protein synthesis and secretion in the large wound group, in consistent with the macrophage activation response to TLRs as reported by Fitzgerald et al. [ 17 ]. Further subcluster comparisons and enrichment analysis of macrophage-dendritic cells demonstrated that macrophage-dendritic cells were stimulated and activated in the TLR9agonist group (Fig. S4C ). pDC, as one of the cell types expressing TLR9 and recognizing self-nucleic acids, have been shown to quickly migrate into wound and produce proinflammatory cytokines such as type I interferon (IFN). However, we did not observe an increase of number of pDC in the late-stage wound of TLR9agonist_LW. We suppose this attributed to rapid recovery of pDC number [ 20 ]. However, in the TLR agonist group, pDC showed significant upregulation of pathways such as toll-like receptor signaling pathway and cytosolic DNA-sensing pathway, as well as activation-related pathways like NF-kb and MAPK (Fig. S4D ).

γδT cell numbers significantly increased, which was further confirmed to be correlated with the activation of TLR9

The activation of TLRs in various cells can also lead to the initiation of adaptive immunity [ 17 ]. In our single-cell data, T cells were found to be most abundant immune cells, inspiring our interest of the function and changes in T cells (Fig. 3B ). The subclustering of T cells resulted in five main subsets including CD4-IL17a+Cd7+ γδT cell (γδT), CD4+Ifng+Il18+ T helper 1 (Th1), CD4+Gata3+Il4+ T helper 2 (Th2), Foxp3+Ctla4 + T regulatory cell (Treg), Cd8b1+Nkg7+ cytotoxic T cell (CTL), based on markers from published research [ 42 ] (Fig. 3D ). After defining cell subsets, we observed prominent increase in the number and proportion of γδT cells (Fig. 3D ) and numerous enriched GO terms related with activation and differentiation of γδT cells in the TLR9 agonist_LW group (Fig. 3G ). The increased γδT cells were primarily IL17-producing γδT cells from the dermis, characteristic of Cd27-Cd44+ IL-17a+Il7r+ Blk+ Maf+ Rorc + [ 43 ]. The changes in γδT cells were further confirmed by flow cytometry (Fc) of wounds with different levels of activation of TLR9 (Fig. 3E, F ). The wounds with enhanced tissue damage (WT_eLW) also resulted in an increase in the number of γδT cells, further suggesting that γδT cells are influenced by the degree of tissue damage and the activation of TLR9, while the number of γδT cells decreased in TLR9 knockout mice (Fig. 3E, F ). These results may indicate that the activation of TLR9 may have great influence on γδT cells.

Given the close association between γδT cells and TLR9, we hypothesized that γδT cells are key cells mediating the effects of TLR9 activation on wound repair and regeneration. Therefore, we utilized the TCRδ−/− mice which lack γδT cells. We found that mice with TCRδ−/− mice exhibited significantly slower wound healing rates (Figs. 3 H and 5I ). Moreover, in TCRδ−/− mice, the promotion of wound healing by TLR9 agonist was abolished upon injection (Fig. 3H ). Furthermore, we also found that the impact of TLR9 agonists on WIHN also relies on γδT cells (Fig. 5H, I ). These results collectively indicate that γδT cells are critical cells mediating the actions of TLR9.

The CCL2-CCR2 axis, known to promote the recruitment of γδT cells to the periphery, was found to be influenced by TLR9, accounted for the increase of γδT cells

To explore the source of the increased γδT cell numbers, we speculate that TLR9 may promote the increase in the number of γδT cells by enhancing their local proliferation. We scored the proliferation-related genes of γδT cells with AddModuleScore in Seurat and found that the cell cycle-related genes of γδT cells in the TLR9 stimulated group didn’t increase, indicating the increase of γδT cells in wound might not be caused by enhancement of local expansion or proliferation (Fig. 4F ).

A The score of cycle cycle-related genes in γδT cells. B The expression of Ccr2 and Ccr6 in γδT cells of two groups. C The L-R pair contribution of CCL family. D Immunofluorescence staining was performed to visualize the expression of the CCL2 protein in the dermal part of skin wounds with different treatments at SD2. Scale bar: 20 μm. E The expression of Ccl2 in all cells from TLR9agonist_LW and WT_LW. F The expression of CCL2 in wounds at SD2 was detected by western blotting. GAPDH was used as loading control. G , H The proportions of γδT cells in wounds treated with TLR9agonist +anti-CCL2 antibody (26161-1-AP, Proteintech) (10 μg per time for per mouse) at PWD 0,3,6 or TLR9agonist +control-IgG from same species. n  = 4 for each group. All error bars mean ± SD.

Due to the constant migration and movement of γδT cells between lymph nodes and peripheral tissues, their migration speed and pattern undergo changes in cases of tissue infection or injury [ 44 ]. We therefore investigated whether the increase in γδT cell numbers result from an increase in their migration into the skin. As reported by McKenzie et al., in steady-state conditions, CCR6 controls γδT 17 trafficking to the dermis; however, in cases of tissue damage, CCR2 controls the rapid migration of γδT 17 to damaged sites [ 45 ]. Therefore, we first observed and compared the expression of CCR2 and CCR6 in γδT cells. We found that almost all γδT cells in TLR9-activated wounds were CCR2 hi CCR6 lo cells (Fig. 4B ). The expression of CCR2 in γδT cells was significantly increased in TLR9 agonist wounds (Fig. 4B ). To delve deeper into the ligands of CCR2, we isolated γδT cells and analyzed their interactions with other cells using cellchat (Fig. 4C ). Notably, the ranking of the contribution of the CCL family L-R pair in cellchat affirmed the pivotal role of CCR2 (Fig. 4C ). Among the various CCR2 ligands, CCL2 emerged as the most potent and prominently expressed, with significant upregulation observed in the TLR9 agonist group. Immunofluorescence staining and western blot analyses further underscored the strong association between CCL2 expression and TLR9 activation levels, peaking in the TLR9 agonist or enhanced injury groups and diminishing in TLR9 knockout mice (Fig. 4D–F ). Consequently, we postulated that TLR9 may facilitate the chemotaxis of γδT cells via the CCL2-CCR2 axis. Intriguingly, concurrent administration of anti-CCL2 or Ctrl-IgG with TLR9 agonists revealed that the reduction in γδT cell numbers at the wound site mediated by anti-CCL2 antibodies counteracted the effects of TLR9 agonists on increase of γδT cell (Fig. 4G, H ).

The role of γδT in promoting hair follicle regeneration is achieved through Areg rather than IL-17

Although anti-CCL2 reduced the number of γδT17 cells and had a significant inhibitory effect on wound healing and hair follicle regeneration, we were still unsure which molecule was responsible for promoting hair follicle regeneration via γδT cells. It has been previously reported that the γδT17 promotes WIHN by secretion of fgf9 and then activates the WNT pathway of keratinocytes [ 46 ]. However, in our scRNA data, the levels of Fgf9 mRNA are extremely low in γδT cells both in TLR9agonist_LW and WT_LW (Fig. S5B ). Therefore, we hope to investigate whether there is another molecule secreted by γδT cells that exerts significant influence on the WIHN level. We analyzed the cell interactions between T cells and keratinocytes using cellchat (Figs. 5A , S5A ), observing the increased number of T-ker interactions in the TLR9 agonist group, and prominent importance of γδT cells in the interaction with keratinocytes(Fig. 5A, B ). Among the increased pathways, IL-17 was the most prominent (Fig. S5C, D ). Through Cellchat and featureplot, we identified that IL-17 was produced by γδT17 cells and mainly received by upper hair follicle cells, interfollicular keratinocytes and hair germs which may be closely related to hair follicle development (Fig. S5D ). The increased of IL-17A was also confirmed in both scRNA data and WB staining (Fig. S5B, E ). Given the reported role of IL-17A in promoting wound healing [ 47 ] and the stemness of keratinocytes [ 48 , 49 ], we hypothesized that the action of γδT17 cells is also achieved through IL-17A. However, by injecting anti-IL-17A into the wound site early in the experimental period, we found that although IL-17A had a significant effect on wound healing speed, antagonizing IL-17A did not affect the hair follicle regeneration promoted by TLR9 agonists (Fig. S5F–H ). Moreover, according to literature, in WIHN model without intervention, there was no difference in regeneration outcomes between IL-17a/f−/− mice and WT mice [ 13 ]. Therefore, we speculated that the action of γδT cells is achieved through other signaling molecules.

A The information stream plot indicating the incoming and outgoing interaction strength in T cells and keratinocytes. The most intense outgoing interaction of T cells to keratinocytes in TLR9agonist_LW was launched by γδT cells, but in WT_LW it was Th2. B The differential interaction pathways in TLR9agonist_LW and WT_LW. C The interaction numbers between T cells and keratinocytes in TLR9agonist_LW and WT_LW. D The bubble diagram showing the pathways mediating the interaction between γδT cells and keratinocytes in two groups. E The expression level of Areg in γδT cells. F Immunofluorescence staining was performed to visualize the expression of the AREG protein in skin wounds with different treatments at PWD7, PWD10, SD2, respectively. G The mRNA changes of areg in WT_LW, TCRdelta−/−_LW and TLR9agonist+ TCRdelta−/−_LW at SD2. H The number of hair follicles at PWD28 in different group. I The healing speed differences in different group. The injection of anti-AREG (15 μg per time per mouse. HY-P77868, MedChemExpress) or Control-IgG (30000-0-AP, Proteintech) into the wound bed was conducted at PWD 0,3,6,10. J The mRNA changes of Areg, Wnt10b, Ctnnb1, Twist1, Twist2 in Hacat cells after culturing with various concentrations of rhAREG for 48 h.

The EGF family was also a highly represented signaling molecule in T cell and keratinocyte interactions, and one of the significantly different molecules between the two groups (Fig. 5C , S5I, J ). Akiyama et al. found Bulge HFSC was highly co-localized with EGFR during embryonic development [ 50 ], and EGFR KO mice exhibit impaired hair follicle differentiation and multiple hair shaft abnormalities, indicating the crucial role of EGF signaling in hair follicle development. Through cellchat L-R scoring, we found that Areg was the strongest acting signal in the EGF family (Fig. 5D ). The areg signal was involved in almost all γδT17-related actions on various keratinocytes, with upper hair follicle cells being the most significant cell populations involved in hair follicle development (Fig. 5D ). The increased expression of Areg was also verified in scRNA data, immunofluorescent staining and RT-qPCR analysis (Fig. 5E–G ). Mice without mature γδT cells has significantly decreased expression of Areg, and adding TLR9agonist into wounds couldn’t rescue the low expression of Areg (Fig. 5F, G ). These results help proved the correlation between TLR9 activation, γδT cells and expression of Areg.

As a member of the EGF family, AREG is considered one of the low-affinity ligands of EGFR and has been implicated in a variety of physiologic processes, including regulation of keratinocyte proliferation and gland development [ 51 ]. While AREG has been shown to influence wound healing speed [ 52 ], its impact on follicle development and whether it affects the de novo formation of follicles in adult individuals in the context of WIHN remains unclear. Therefore, we aimed to investigate the hypothesis that AREG may influence WIHN levels and that the function of γδT cells is primarily achieved through AREG. By injecting monoclonal antibodies against AREG to antagonize its function, we observed a significant reduction in wound healing and hair follicle regeneration, highlighting the role of AREG (Fig. 5H, I ). Additionally, to elucidate the mechanism by which AREG acts on keratinocytes, we conducted an in vitro study using HaCaT cells. Various concentrations of recombinant AREG (rh-AREG) were added to the cell cultures, and through RT-qPCR analysis, we observed a significant promotion of cell proliferation (Fig. S6A ) and upregulation of genes associated with hair follicle regeneration, such as Wnt10b, Ctnnb1, Twist1, and Twist2 , while lower expression of Bmp6 , which is known to be adverse for WIHN (Fig. 5F ). Concurrently, we also observed a significant increase in the expression level of Areg itself upon addition of recombinant AREG, suggesting the presence of a positive feedback loop (Fig. 5F ), which was consistent with the results in cellchat where keratinocytes are recognized as key recipients and responders to AREG (Fig. S5I, J ). Therefore, we hypothesized that AREG produced and secreted by γδT cells may act on keratinocytes via paracrine signaling, boosting the production of AREG by keratinocytes themselves, and leading to a localized rapid elevation of AREG levels and enhancement of its effects. To obtain further information regarding the association of AREG with healing, particularly WIHN, we conducted additional analysis of biological samples from different WIHN outcomes sourced from public databases (GSE159939). We discovered that AREG exhibits relatively high expression in the high-WIHN outcome area (center of wounds in Mus musculus mice, or global area in Acomys spiny mice), while showing relatively low expression in the peripheral wounds of Mus musculus mice, which characteristic as low-WIHN outcome mice (Fig. S6B ). We believe this further support the potential positive role of AREG in WIHN.

AREG was found to be associated with development of keratinocytes

To elucidate the changes in behavior and differentiation tendency of keratinocytes under TLR9 agonist stimulation, we analyzed the development and differentiation trajectory of keratinocytes in two groups using monocle2 and RNA velocity. Unsupervised clustering of RNA velocity in keratinocytes revealed four major differentiation pathways. Based on cell annotation and spatial localization of annotated cells (Fig. 6B ), we determined four developmental trajectories, namely as: self-renewal of bulgeHFSCs (trajectory 1), regeneration of new hair germs (HG) (trajectory 2), development of hair follicles (trajectory 3), and differentiation of the permanent epidermis (trajectory 4) (Fig. 6A ).

A The RNA velocity analysis of keratinocytes. Trajectory 1: self-renewal of bulgeHFSCs; trajectory 2: regeneration of new hair germs (HG); trajectory 3: development of hair follicles; trajectory 4: differentiation of the permanent epidermis. B The annotation of subsets in keratinocytes. C The expression of marker genes used for identification of development trajectories. D The scores of EGF in every trajectory. E The marker genes of bulgeHFSC and proliferation related genes. F The HG related genes and EGF scores changes in trajectory 2. G The heatmap plotting differential gene expression from pseudotime analysis in trajectory 3. H The pseudotime analysis of main cells in trajectory 3. Vlnplot and box plot graphs indicated the value of minimum, first, quartile, median, third quartile, and maximum. * p  < 0.05, ** p  < 0.01, *** p  < 0.001, and **** p  < 0.0001.

Trajectory 1 originated from the highly proliferative Cluster 12 cells characterized by Mki67+ Ccnb1+ Pcna+ and terminated at Lhx2+Cd34+ Ptn + Lgr5+ bulgeHFSC (K8, K1), suggesting that pathway one may represent the proliferation and self-renewal of bulge HFSCs (Fig. 6E ). Trajectory 2 was initiated by IFE (K7) which did not express any HG markers and culminated in K2 characterized by highly expressed HG markers (Krt79+Krt17+Sox9+) (Fig. 6F ) [ 10 ], which was mainly derived from the development of IFE with lineage plasticity and migration after injury in WIHN, indicating that trajectory 2 represented the de nono formation of new HGs in WIHN. Trajectory 3 originated from moderately differentiated keratinocytes and gradually underwent differentiation and maturity towards hair follicle, indicating that trajectory 3 represented the process of hair follicle development (Fig. 6A ). Trajectory 4 originated from basal-like keratinocytes and underwent gradual differentiation, indicating that trajectory 4 represented the process of differentiation of the interfollicular epidermis.

To elucidate the distinct cellular states during epidermal differentiation and the generation of new hair follicles in the context of WIHN, we initially extracted the major cell clusters (clusters K0, K3, K6, K10) mainly composed of trajectory 3 and investigated the differentiation state of these keratinocytes through pseudotime analysis (Fig. 6H ). As pseudotime progressed (Fig. 6H ), the expression of many genes proved to be involved in maturity and differentiation in hair follicles such as Bhlhe40, Zfp750, Grhl1, Cebpa et al. [ 32 ] increased, indicating the developmental pattern (Fig. 6H ). Concurrently, there was a higher concentration of TLR9agonist cells in the late stages of the pathway, suggesting a stronger tendency towards hair follicle differentiation (Fig. 6H ). To understand the role of AREG in hair follicle development, we also performed EGF scoring. During the process of hair follicle development, both the expression of AREG and EGF scoring increased as the developmental pathway advanced (Fig. 6D, H ), revealing the involvement of Areg and EGF in this trajectory. These results suggest that the expression of Areg and EGF were highly synchronous with multiple development trajectories of keratinocytes.

Since the discovery of the WIHN phenomenon, one of the most interesting and urgent phenomena to study is the requirement of sufficiently large wound areas for hair follicle regeneration. These hypotheses encompass a range of factors, including the unique mechanical topology of large wounds, the extent of tissue damage within such wounds, and the varying concentration and spatial distribution of signaling molecules that either promote or hinder regeneration in large wounds [ 53 ]. These hypotheses have received partial support through empirical evidence. Interestingly, previous investigations have documented a positive correlation between the size of wounds and their regenerative potential, whereby larger or more severely damaged wounds elicit more robust regeneration [ 4 , 15 ]. In this study, we harnessed diverse wound types that encompassed different levels of injury on the dorsal skin to search for early, vital events linked damage-associated signals and the launch of repair and regeneration.

The recognition of damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs) represents a fundamental mechanism in the initial response to tissue damage or pathogen invasion. In the aftermath of the activation of DAMPs and PAMPs, two pivotal steps must be orchestrated to facilitate tissue repair and regeneration: (1) the coordinated mobilization and activation of pertinent precursor cells, facilitating the reconstitution of damaged tissue structures, and (2) the induction of morphogenetic and regenerative pathways within both precursor cells and stromal cells [ 54 ]. The elucidation of the impact of PPR activation on the immune microenvironment may shed light on this inquiry. Activation of PPRs elicits intricate inflammatory responses, characterized by the recruitment, proliferation, and activation of diverse hematopoietic and non-hematopoietic cells, thereby initiating and coordinating tissue repair and defense responses [ 55 ].

In this study, we firstly identified the influence of DAMPs release and recognition of PPRs on the immune microenvironment and the repair process by assessing the number of regenerated hair follicles associated with varying degrees of injury. By analyzing bulk RNA sequencing data from large wounds with different levels of damage, we observed variations in the activation levels of TLRs and specifically TLR9. Previous research has demonstrated the involvement of TLRs, a significant subtype of PPRs, in damage sensing and the initiation of tissue repair and regeneration [ 56 ]. TLRs have also been implicated in the regeneration of skin wounds. For instance, TLR3 activation triggered by the detection of released dsRNA from injured cells upregulates IL-6 expression and induces STAT3 phosphorylation in epidermal keratinocytes and finally promotes the regeneration of hair follicles [ 9 , 15 ]. However, TLR9 has often been overlooked due to its lower expression levels, being presumed to be expressed solely in response to skin injury or external stimuli in human and mouse skin [ 57 , 58 ]. Gregorio et al. [ 20 ] found that tissue damage can activate plasmacytoid dendritic cells (pDCs) through TLR7 and TLR9-dependent pathways to enhance skin wound healing, without addressing the effect of TLR9 activation on various types of immune cells and whether they can affect hair follicle regeneration. Moreover, our study has not only confirmed the involvement of TLR9 in wound healing but also, for the first time, revealed its role in reactivating embryonic-like developmental programs by modulating the late immune microenvironment of the wound.

Furthermore, we discovered that the enhancement of large wounds compared to ordinary large wounds is primarily facilitated by the release of free mtDNA, which activates TLR9. During cellular trauma, mitochondrial DAMPs, including mtDNA containing CpG DNA repeats, have been observed to be released and activate TLR9, GMPAMP synthase-stimulator of interferon genes (cGAS-STING), and neutrophil extracellular traps (NETs) [ 24 ]. Previous studies have frequently associated mtDNA with NETs, which was demonstrated to impede WIHN, thus implying a negative effect of mtDNA on hair follicle regeneration [ 59 ]. Through meticulous in vitro and in vivo investigations, we discovered that lower levels of mtDNA can stimulate TLR9 activation and hair follicle regeneration (Fig. 2G ). Although the WIHN phenomenon is absent in human skin, mtDNA has also been detected in cases of human tissue damage, accompanied by increased TLR9 expression, demonstrating a similar mechanism [ 24 ]. These findings offer novel insights into the activation of signaling receptors implicated in tissue damage, the orchestration of immune responses, and ultimately, the outcome of tissue repair and regeneration. Therefore, we proposed the hypothesis that greater tissue damage and promotes WIHN by inducing heightened activation of TLR9 pathway through mtDNA release in a dose-dependent manner.

As one of the key barrier cells residing in the skin, gamma delta T cells (γδT) have been found to play a vital role in detecting skin integrity, maintaining skin homeostasis, aiding in wound repair, preventing infection, and preventing malignant tumors [ 44 , 60 ]. Specifically, wounding has been shown to up-regulate epidermal-derived IL-1α, which serves as a potent activator of γδT cells. In addition, γδT cells have also been suggested to be associated with Toll-like receptors (TLRs). The number of γδT cells decreases in skin wounds of TLR3−/− mice [ 15 ]. In addition, recent evidence has emerged that γδT plays a role in cutaneous wound healing [ 61 ]. In our study, we also observed that under stronger TLR9 activation levels, either through administering TLR9 agonists or enhancing tissue damage, there was an increase in the number of γδT cells in the wounds. Mice lacking γδT cells exhibited significantly impaired healing speed and follicle regeneration, and they could’t be rescued by TLR9 agonists or enhanced tissue damage, indicating that the function of TLR9 was mediated through γδT cells. Consequently, our research further underscores the critical role of γδT cells in sensing signals related to tissue damage and their key contribution to tissue repair and regeneration processes.

Previous literature has proposed several findings regarding the mechanisms by which γδT cells exert their effects on healing outcomes and their impact on keratinocytes. Activated γδT cells promote hair follicle stem cell (HFSC) proliferation and migration, thereby facilitating injury-induced hair regeneration [ 46 , 47 ]. Furthermore, Gay [ 46 ] et al. proved that γδT plays a vital role in WIHN by secreting FGF9. However, in the context of larger wounds than models studied by Gay et al. [ 46 ], the FGF9 was barely detected in both groups from our data, suggesting that γδT cells may affect regeneration outcomes through other pathways in this condition. Additionally, our findings demonstrate that although most of the recruited γδT cells were IL-17-producing γδT cells, the addition or antagonism of IL-17A did not alter hair follicle regeneration outcomes. By analyzing the intercellular communication between γδT and multiple keratinocytes, we found γδT cells mainly affected keratinocytes by secreting AREG. The production of γδT promotes AREG secreted by keratinocytes through paracrine signaling, leading to a positive feedback loop that rapidly and significantly increases AREG levels.

AREG has been demonstrated to function as one of the alarm systems following injury. Recent studies have highlighted its crucial role in promoting epithelial cell proliferation and differentiation, particularly in the late stages of wound healing where it aids in resolving inflammation [ 62 ]. However, whether this promotion of keratinocyte differentiation is beneficial for WIHN remains unclear. Some observations suggest that AREG promotes interfollicular epidermal fate at the expense of consuming keratinocytes’ fate [ 63 ]; yet, other research has found that AREG enhances hair follicle neogenesis [ 64 ]. Given the impact of the late-stage inflammatory environment on cell fate and the complexity of stem cell fate in WIHN, we think that further experimental evidence is needed to fully understand the role of AREG. Our single-cell analysis and experiments revealed that AREG affects the expression of genes such as Twist1, Twist2, Wnt10b , and Ctnnb1 in keratinocytes and significantly influences the multiple processes and cell fates of keratinocytes, including the process of hair follicle development, the de nono formation of hair germs and the self-renew of bulge HFSC. Our study not only sheds new light on the role of γδT cells in wound-induced hair neogenesis (WIHN), but also provides explanations for how these cells interact with injury-related signals and ultimately affect the behavior of matrix cells involved in repair.

Our work raises more questions that need to be addressed for the field. Firstly, one of the critical questions is whether similar mechanisms exist in humans. As previously discussed, TLR9 expression is up-regulated in human keratinocytes after injury [ 57 ], and γδT cells also exist in the human dermis. Furthermore, AREG-producing γδT cells have been identified in tumors, promoting the proliferation of tumor epithelial cells and exhibiting wound healing functions [ 65 ]. Therefore, it remains to be determined whether γδT cells in human skin will be similarly affected by TLR9 stimulation and produce AREG in response to skin injury. Additionally, aside from dermal γδT cells, will skin injury and TLR9 activation also affect epidermal DETCs or macrophages? DETCs, as one of the critical cells that reside in the epidermis and monitor the status of adjacent keratinocytes through dendritic projections, undergo rapid morphological and functional changes in response to epithelial damage [ 66 ]. Although we were unable to collect sufficient DETCs for analysis due to the limit of single-cell transcriptome cell numbers in this study, the extent of tissue damage and how TLR9 affects DETCs remain important issues worthy of further research. In apart, lower WNT signaling in macrophages and fibroblasts and phagocytosis of macrophages were observed in TLR9 activated group within a few days after re-epithelialization, as reported in Gay et al. [ 8 ]. Could TLR9 function through similar mechanisms that inhibit late excessive and prolonged WNT activation? In the future, we will explore the detailed mechanisms by which TLRs influence other processes of skin repair and regeneration and to investigate whether any of these mechanisms could explain the healing outcome changes and the lack of WIHN in humans.

Materials and methods

Ethical approval.

The experimental procedures undertaken in this study were granted ethical approval by the Institution Review Board of West China Hospital of Stomatology, Sichuan university (Approval No. WCHSIRB-D-2023-018).

Sex as a biological variable

Our study examined male mice because male animals exhibited less variability in phenotype.

Excisional wound model and implantation procedures

Male wildtype C57BL/6J mice (Dossy Experimental Animals Co., Ltd.) and C57BL/6Smoc-Tlr9em1Smoc mice (TLR9 −/− mice) (Cat. NO. NM-KO-190168) ((Shanghai Model Organisms Center Inc., Shanghai, China, aged 6–8 weeks and weighing approximately 20 g, were utilized for the research. Genotyping of the Tlr9 locus was performed using the following primers: 5′-GCTCTCCCACTTTCTCTTCCTCTC-3′ and 5′-TGCCGCCCAGTTTGTCAG-3′ for the wild-type allele, and 5′-TGCCGCCCAGTTTGTCAG-3′ and 5′-ACGGGAAAAGGGTGGGTGTG-3′ for the Tlr9 knockout allele. The mice were housed under standard conditions including a temperature range of 21–27 °C, a humidity level of 40–70%, and a 12-h light–dark cycle with ad libitum access to food. The number of animals utilized for each experiment is specified in the figure legends. Circular full-thickness wounds with diameters of 0.6 cm or 1.8 cm were created on the dorsal skin of the mice. The mice were divided into several groups for further study: large wounds (1.8 cm diameter) treated with PBS (WT_LW) or TLR9 agonist in the wound bed (TLR9agonist_LW), and small wounds (0.6 cm diameter) treated with PBS (WT_SW). Additionally, to investigate the effects of different degrees of damage, three types of large wounds (1.8 cm diameter) with additional incisions were designed and used: dotted WT_eLW with 6–8 poke points through the epidermis and dermis, parallel WT_eLW with 6-8 short incisions parallel to the cutting edge, and vertical WT_eLW with 6–8 radial short incisions perpendicular to the cutting edge. Mice were euthanized at 1–4 weeks after the surgery, and round full-thickness samples with diameters of 10 mm (for small wounds) or 25 mm (for large wounds) were harvested. To investigate the function of CCL2, IL-17A, and AREG, anti-CCL2 (26161-1-AP, Proteintech) at a dosage of 10 μg per mouse, or anti-IL-17A (PAB30184, Bioswamp) at a dosage of 10 μg per mouse, or anti-AREG (66433-1-Ig, protech) at a dosage of 15 μg per mouse was injected into the wound bed at postoperative day (PWD 0, 3, and 6. Rabbit IgG served as the control antibody (30000-0-AP, Proteintech) in all cases.

RNA isolation and real-time PCR

For in vivo mouse samples, isolation of total RNA was conducted after homogenization using the RNeasy Mini Kit (Qiagen, 74106) as appropriate. RNA concentration and purity were determined using the NanoDrop2000c (Thermo Fisher Scientific. Reverse transcription of RNA into cDNA was accomplished with the PrimeScript RT reagent Kit with gDNA Eraser (RR047A, Takara, followed by quantitative RT-PCR (qRT-qPCR) using the TB Green® Premix Ex Taq™ (Tli RNaseH Plus) (RR420A, Takar(A). Data were normalized to GAPDH expression utilizing the 2 -ΔΔCT method. The standard deviation (SD) of samples carried out in technical triplicates was represented by error bars. The supplemental materials contain the primers used for RT-qPCR.

Cell culture and scratch test

Since TLR9 expression is primarily documented in monocytes-macrophages in existing literature, we utilized TRAIL-resistant human acute myeloid leukemia cells (THP-1) for our research. THP-1 cells were obtained from the cell library of the Chinese Academy of Sciences Cells after undergoing multiple tests including mycoplasma detection, endotoxin testing, and DNA fingerprinting. The THP-1 cells were cultured in RPMI medium supplemented with 10% FBS, 100 U/mL penicillin, and 100 mg/mL streptomycin at 37 °C in a 5% CO 2 incubator. Scratch assays were employed on densely adherent M0-THP1 cells to simulate tissue injury, following the method used by Nelson et al. [ 7 ]. To induce differentiation of THP-1 cells into a macrophage-like phenotype, 106 THP-1 cells were transferred to each of the six-well plates and cultured with 100 ng/mL phorbol 12-myristate 13-acetate (PMA) (P8139, Sigma-Aldrich) for 24 h. Eight scratches were made in cells within each well.

Human HaCaT cell lines were obtained from the cell library of the Chinese Academy of Sciences and cultured in RPMI medium supplemented with 10% FBS, 100 U/mL penicillin, and 100 mg/mL streptomycin at 37 °C in a 5% CO 2 incubator. The influence of AREG was studied by adding different concentrations of recombinant AREG protein (100, 500, 1000 ng/mL) (P15514, novoprotein) to the culture medium for 48 h.

Extraction and copy number determination of cell-free mtDNA

To detect the copy numbers of mtDNA, the DNeasy Blood & Tissue Kit (QIAGEN) was utilized following the manufacturer’s instructions to isolate the cell-free mtDNA in plasma and cell supernatant. Quantitative real-time PCR analyses were conducted to determine the mtDNA copy number in samples, using plasmids expressing mouse or human Cytochrome (CytB) with known copy numbers as standards. The mouse or human CytB sequences were inserted into the pUC57 plasmid, transformed and amplified in Escherichia coli ( E. coli ). The recombinant plasmid DNA was isolated from the bacteria and sequenced to confirm the sequences and acquire the DNA mass. The copy numbers of plasmids were determined by the formula: (6.02 × 10 23 ) (OD26050)/(base number6601014) = copies/μL. Standard plasmids with concentrations of 100, 10, 1, 10 −1 , 10 −2 , 10 −3 , 10 −4 , 10 −5 , 10 −6  ng/μL were diluted from the plasmids. The samples with standard substances were subjected to RT-PCR simultaneously. The CT-copy number standard curve was calculated using the data of standard plasmids, and the copy numbers of samples were calculated using the same formula.

Extraction and application of mtDNA

The Mitochondrial DNA isolation kit (ab65321) was used according to the manufacturer’s instructions to extract mtDNA from mouse liver for injection into wound beds. The concentration and purity of mtDNA were determined using the NanoDrop2000c and stored at −20 °C. For injection into wound beds, 20 μg (mtDNA lo _LW) and 80 μg (mtDNA hi _LW) of mtDNA were used at PWD 0, 3, and 6. For in vitro tests, 100 ng/mL mtDNA was added to the cell culture medium for 24 h.

Bulk-RNA sequencing

Bulk-tissue RNA sequencing was conducted using three to four replicates of mice skin wounds in each group using standard methods to make sure samples were strictly controlled for quality. The Illumina sequencing of the libraries was performed. Through z-transformation of fragments per kilobase of transcript per million mapped reads of the selected gene, gene expression was analyzed. Differential expression analysis of two groups (three biological replicates per group) was performed using the DESeq2 R package (v1.32.0). A p value < 0.05 and |log2(foldchange)| > 1.5 were set as thresholds for significant differential expression. We used the cluster Profiler R package to test the statistical enrichment of marker genes in KEGG and GO pathways.

Single cell RNA sequencing

Tissue dissociation.

For scRNA-seq analysis, we collected five fresh samples at 2 days post scab detachment (SD2) per group. The wound tissues underwent enzymatic epidermal-dermal separation using the Epidermis Dissociation Kit (Epidermis Dissociation Kit, mouse; Miltenyi Biotec followed by dissociation of the epidermis and dermis parts. The resulting cells were then filtered, centrifuged, and resuspended in phosphate-buffered saline (PBS) with 0.5% bovine serum albumin (BSA). The dermis part was further dissociated using a mixed enzyme solution containing type I collagenase and trypsin, digested, and then processed similarly to the epidermis part. Subsequently, the dermis cells were combined with the epidermis cells after being subjected to red blood cell lysis buffer and removal of dead cells and debris by Dead Cell Removal MicroBeads (Miltenyi).

Sequencing and data processing

Single-cell suspensions were used for Single-Cell RNA-seq (10x Genomics Chromium Single Cell Kit), followed by sequencing using an Illumina 1.9 mode. Subsequently, the reads were aligned, and expression matrices were generated using the Cell Ranger pipeline software. Downstream computational analysis involved merging different samples into one Seurat object using the RunHarmony function and performing filtering, normalization, scaling, canonical correlation analysis, principal component analysis, and dimensionality reduction using various R packages. Unsupervised clustering and differential gene expression analysis were carried out, followed by gene set enrichment analysis and receptor-ligand probability prediction among cell subpopulations.

Pseudotime analysis

We utilized Monocle 2 for pseudo-temporal trajectory analysis to elucidate cell differentiation trajectories. These sophisticated algorithms position cells along a trajectory that corresponds to a specific biological process, such as cell differentiation, leveraging the distinct asynchronous progression of individual cells within an unsupervised framework [ 67 , 68 ]. In the analysis, raw count data of highly variable genes, identified using the FindVariableGenes function from the Seurat package (with parameter y.cutoff = 0.5), were employed for pseudo-temporal trajectory analysis.

RNA velocity analysis

ScVelo package was employed to perform RNA velocity analysis, which allowed for the identification of transient cellular states and prediction of directional progression of transcriptomic signatures along developmental trajectories. This analysis was based on gene-specific rates of transcription, splicing, and degradation of mRNA, with the results projected as a stream of arrows on the UMAP Embedding.

Gene signature scoring based on scRNA-seq data

In the assessment of module scores and enrichment fractions for EGF pathway genes and cell cycle related genes’ expression in individual cells, the scRNA-seq data underwent AddModuleScore() computation. The hallmark genes, associated with the EGF or cell cycle in GO terms, were obtained from the Molecular Signatures Database ( http://www.gsea-msigdb.org/gsea/msigdb/index.jsp ) and utilized as the gene signature for scoring.

Histopathology, Immunohistochemistry, and immunofluorescence microscopy

For histopathology, immunohistochemistry, and immunofluorescence microscopy, the samples were fixed with 4% paraformaldehyde and underwent ethanol and xylene dehydration at least 24 h beforehand. H&E staining and Masson’s trichrome staining were performed to observe re-epithelialization and collagen fiber deposition. In addition, frozen sections were collected for antibodies that can only be stained for IF-Fr. The primary antibodies used for immunofluorescence staining were as follows: TLR9 (Abcam, Ab134368, 1:150), IRF7 (sc-74472, Santa Cruz Biotechnology, 1:133), EEA1 (C45B10, Cell signaling technology, 1:133), CD3 (14-0032-82, Thermo Fisher Scientific, 1:100), Ki67 (Servicebio, GB121141, 1:100), TCR γ/δ (118101, Biolegend, 1:150), Goat Anti-Armenian hamster IgG H&L (Alexa Fluor® 647) (ab173004), CCL2 (26161-1-AP, Proteintech, 1:150), and AREG (16036-1-AP, Proteintech, 1:200). Biopsy sections were de-paraffinized and underwent antigen retrieval using Target Retrieval Solution. After washing and permeabilization with Tris-buffered saline (Quality Biological, 351-086-101) and 0.1% Tween 20 (Sigma, P2287) (TBST) buffer, the sections were blocked with 5% sheep serum and 1% Bovine Serum Albumin (BSA) (Fisher Bioreagents, BP9703-100) at room temperature for 1 h. Subsequently, the sections were incubated with the primary antibodies (as mentioned above) diluted in Antibody Diluent (Agilent Dako, S0809) overnight at 4 °C. After washing with TBST, the sections were incubated with fluorescent binding secondary antibodies at room temperature for 1 h. Following a final wash, the cell nuclei were stained with DAPI, and the sections were mounted with mounting medium. Immunofluorescent images were analyzed using DFC365FX (Leica or Olympus FV3000 Confocal Laser Scanning Microscope and processed with FIJI/ImageJ (National Institutes of Health, Bethesda, MD).

Western blot analysis

To perform western blot analysis, protein lysates were extracted from the wound bed of three different types of snap-frozen skin tissue: WT_LW, TLR9agonist_LW, and WT_eLW. The proteins (15 µg per lane) were then separated using sodium dodecyl sulfate (SDS)-polyacrylamide gels and transferred to polyvinylidene fluoride membranes. After being blocked with 5% BSA, the membranes were incubated overnight with primary antibodies obtained from Cell Signaling Technology, Inc. (Danvers, MA), which included CCL2 (26161-1-AP, Proteintech, 1:500), AREG (16036-1-AP, Proteintech,1:400), IL-17A (sc-374218, Santa Cruz, 1:500), and GAPDH antibody (sc47724, Santa Cruz, 1:5000). The membrane was then washed and incubated with secondary horse radish peroxidase-labeled antibody. Bands were visualized using FluorChem E (ProteinSimple, San Jose, CA). Densitometry graphs were obtained by quantifying the phosphoprotein and total protein bands through densitometry with the use of Image-Pro Plus software (Media Cybernetics, Inc., Rockville, MD. Full and uncropped western blots were present in the supplemental materials.

ALP staining

Whole-mount HFN assay to detect ALP+ dermal papilla (DP) was performed following the protocols in [ 4 ].

Flow cytometry analysis

For flow cytometry analysis, single cells were digested from skin wounds and pre-incubated with purified anti-CD16/CD32 antibody (101301, BioLegend (1.0 μg per 106 cells in 100 μl volume) for 5–10 min to block Fc receptors. The cell suspensions were then co-incubated with fixable viability dye (eFluor™ 780, 65-0865-14, eBioscience) and antibodies against surface markers CD45(PE/Cy7, 147703, BioLegend, Cd3e(APC, 100311, BioLegend, CD4(FITC, 11-0041-82, eBioscience), CD8a(Alexa Fluor™ 700,56-0081-82, eBioscience), and TCRγ/δ(eflour-450,48-5711-82, eBioscience) at 1:400 dilution for 30 min at 4 °C in the dark (100 μl per antibody per sample). After fixation and permeabilization, cells were incubated with antibodies against intracellular marker IL-17A (PE,12-7177-81, eBioscience) at 1:400 dilution for 30 min at 4 °C in the dark (100 μl per antibody per sample). Fluorescence Minus One (FMO) groups were applied in every test. Flow cytometry analysis was performed using Attune Nxt flow cytometer (Thermo Fisher Scientific and FlowJo (v10.8.1). The experiments were performed independently three times ( n  = 3).

Statistics and reproducibility

Statistical analyses were performed with Case Viewer software, Image J software, and Prism 9.0 software using two-tailed t -tests/ t ′-test or one-way analysis of variance (ANOVA)with Tukey post-hoc test. Prior to performing t-tests or ANOVA, distributional checks were conducted to ensure normality. For multiple group comparisons, a test of homogeneity of Variance was conducted, and ANOVA analysis was applied only after confirming similar variances. Data were presented as mean ± standard deviation, and a p-value less than 0.05 was considered statistically significant (* p  < 0.05, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001), while ns indicates no statistically significant difference.

For normally distributed data, outliers were primarily identified by excluding values that deviated more than 3 standard deviations from the mean. For non-normally distributed data, outliers were determined using a box plot approach, where values below (Q1 − 1.5IQR) or above (Q3 + 1.5IQR) were considered outliers. These criteria were established based on standard statistical analysis practices.

Random group assignment was conducted using a randomized design. Animals were initially ordered by their original body weight from smallest to largest, then random numbers were generated from a random number table to determine group allocation.

Blinding procedures were implemented as follows: each animal was assigned a unique numeric code with concealable group information during model construction and grouping. During data collection, information was gathered while concealing group details. Subsequently, data analysis and processing were performed by another researcher who was also blinded to the group allocations.

Data and materials availability

The sequencing data in this study were deposited in the NCBI Gene Expression Ominbus (GEO) under accession number GSE272509 . All other data supporting the findings of this study are available within the article and its supplementary files. Any additional requests for information can be directed to, and will be fulfilled by, the corresponding authors.

Karin M, Clevers H. Reparative inflammation takes charge of tissue regeneration. Nature. 2016;529:307–15.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sallustio F, Costantino V, Cox SN, Loverre A, Divella C, Rizzi M, et al. Human renal stem/progenitor cells repair tubular epithelial cell injury through TLR2-driven inhibin-A and microvesicle-shuttled decorin. Kidney Int. 2013;83:392–403.

Article   CAS   PubMed   Google Scholar  

Frangogiannis NG. Regulation of the inflammatory response in cardiac repair. Circ Res. 2012;110:159–73.

Ito M, Yang Z, Andl T, Cui C, Kim N, Millar SE, et al. Wnt-dependent de novo hair follicle regeneration in adult mouse skin after wounding. Nature. 2007;447:316–20.

Plikus MV, Guerrero-Juarez CF, Ito M, Li YR, Dedhia PH, Zheng Y, et al. Regeneration of fat cells from myofibroblasts during wound healing. Science. 2017;355:748.

Larson BJ, Longaker MT, Lorenz HP. Scarless fetal wound healing: a basic science review. Plast Reconstruct Surg. 2010;126:1172–80.

Article   CAS   Google Scholar  

Wulff BC, Parent AE, Meleski MA, DiPietro LA, Schrementi ME, Wilgus TA. Mast cells contribute to scar formation during fetal wound healing. J Investig Dermatol. 2012;132:458–65.

Gay D, Ghinatti G, Guerrero-Juarez CF, Ferrer RA, Ferri F, Lim CH, et al. Phagocytosis of Wnt inhibitor SFRP4 by late wound macrophages drives chronic Wnt activity for fibrotic skin healing. Sci Adv. 2020;6:eaay3704.

Kim D, Chen R, Sheu M, Kim N, Kim S, Islam N, et al. Noncoding dsRNA induces retinoic acid synthesis to stimulate hair follicle regeneration via TLR3. Nat Commun. 2019;10:2811.

Article   PubMed   PubMed Central   Google Scholar  

Wang G, Sweren E, Andrews W, Li Y, Chen J, Xue Y, et al. Commensal microbiome promotes hair follicle regeneration by inducing keratinocyte HIF-1α signaling and glutamine metabolism. Sci Adv. 2023;9:eabo7555.

Hu C, Chu C, Liu L, Wang C, Jin S, Yang R. et al. Dissecting the microenvironment around biosynthetic scaffolds in murine skin wound healing. Sci Adv. 2021;7:eabf0787.

Yang Y, Chu C, Liu L, Wang C, Hu C, Rung S, et al. Tracing immune cells around biomaterials with spatial anchors during large-scale wound regeneration. Nat Commun. 2023;14:5995.

Wang GF, Sweren E, Liu HY, Wier E, Alphonse MP, Chen RS, et al. Bacteria induce skin regeneration via IL-1 beta signaling. Cell Host Microbe. 2021;29:777.

Wang X, Hsi TC, Guerrero‐Juarez CF, Pham K, Cho K, McCusker CD, et al. Principles and mechanisms of regeneration in the mouse model for wound‐induced hair follicle neogenesis. Regeneration. 2015;2:169–81. https://onlinelibrary.wiley.com/doi/10.1002/reg2.38 .

Nelson AM, Reddy SK, Ratliff TS, Hossain MZ, Katseff AS, Zhu AS, et al. dsRNA released by tissue damage activates TLR3 to drive skin regeneration. Cell Stem Cell. 2015;17:139–51. https://linkinghub.elsevier.com/retrieve/pii/S1934590915003070 .

Brockes JP, Kumar A. Comparative aspects of animal regeneration. Annu Rev Cell Dev Biol. 2008;24:525–49.

Fitzgerald KA, Kagan JC. Toll-like receptors and the control of immunity. Cell. 2020;180:1044–66.

Huang Y, Liu D, Chen M, Xu S, Peng Q, Zhu Y, et al. TLR7 promotes skin inflammation via activating NFκB-mTORC1 axis in rosacea. Peerj. 2023;11:e15976.

Hackstein H, Hagel N, Knoche A, Kranz S, Lohmeyer J, von Wulffen W, et al. Skin TLR7 triggering promotes accumulation of respiratory dendritic cells and natural killer cells. PLoS ONE. 2012;7:e43320.

Gregorio J, Meller S, Conrad C, Di Nardo A, Homey B, Lauerma A, et al. Plasmacytoid dendritic cells sense skin injury and promote wound healing through type I interferons. J Exp Med. 2010;207:2921–30.

Fang F, Marangoni RG, Zhou X, Yang Y, Ye B, Shangguang A, et al. Toll-like Receptor 9 Signaling Is Augmented in Systemic Sclerosis and Elicits Transforming Growth Factor β-Dependent Fibroblast Activation. Arthritis Rheumatol. 2016;68:1989–2002.

Trujillo G, Meneghin A, Flaherty KR, Sholl LM, Myers JL, Kazerooni EA, et al. TLR9 differentiates rapidly from slowly progressing forms of idiopathic pulmonary fibrosis. Sci Transl Med. 2010;2:57ra82.

Bauer S, Kirschning CJ, Häcker H, Redecke V, Hausmann S, Akira S, et al. Human TLR9 confers responsiveness to bacterial DNA via species-specific CpG motif recognition. Proc Natl Acad Sci USA. 2001;98:9237–42.

Zhang Q, Raoof M, Chen Y, Sumi Y, Sursal T, Junger W, et al. Circulating mitochondrial DAMPs cause inflammatory responses to injury. Nature. 2010;464:104–7.

Maatouk L, Compagnion AC, Sauvage MC, Bemelmans AP, Leclere-Turbant S, Cirotteau V, et al. TLR9 activation via microglial glucocorticoid receptors contributes to degeneration of midbrain dopamine neurons. Nat Commun. 2018;9:2450.

Roh YS, Seki E. Toll-like receptors in alcoholic liver disease, non-alcoholic steatohepatitis and carcinogenesis. J Gastroenterol Hepatol. 2013;28:38–42.

Tadeu AM, Horsley V. Epithelial stem cells in adult skin. Curr Top Dev Biol. 2014;107:109–31.

Adam RC, Yang H, Ge YJ, Infarinato NR, Gur-Cohen S, Miao YX, et al. NFI transcription factors provide chromatin access to maintain stem cell identity while preventing unintended lineage fate choices. Nat Cell Biol. 2020;22:640.

Hughes MW, Jiang T-X, Plikus MV, Guerrero-Juarez CF, Lin C-H, Schafer C, et al. Msx2 Supports Epidermal Competency during Wound-Induced Hair Follicle Neogenesis. J Investig Dermatol. 2018;138:2041–50.

Wang XS, Chen HY, Tian RY, Zhang YL, Drutskaya MS, Wang CM, et al. Macrophages induce AKT/beta-catenin-dependent Lgr5(+) stem cell activation and hair follicle regeneration through TNF. Nat Commun. 2017;8:14091.

Ge W, Tan S-J, Wang S-H, Li L, Sun X-F, Shen W, et al. Single-cell transcriptome profiling reveals dermal and epithelial cell fate decisions during embryonic hair follicle development. Theranostics. 2020;10:7581–98.

Joost S, Zeisel A, Jacob T, Sun XY, La Manno G, Lonnerberg P, et al. Single-cell transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity. Cell Syst. 2016;3:221.

Ge Y, Miao Y, Gur-Cohen S, Gomez N, Yang H, Nikolova M, et al. The aging skin microenvironment dictates stem cell behavior. Proc Natl Acad Sci USA. 2020;117:5339–50.

Joost S, Annusver K, Jacob T, Sun X, Dalessandri T, Sivan U, et al. The molecular anatomy of mouse skin during hair growth and rest. Cell Stem Cell. 2020;26:441–57.e7.

Mok KW, Saxena N, Heitman N, Grisanti L, Srivastava D, Muraro MJ, et al. Dermal condensate niche fate specification occurs prior to formation and is placode progenitor dependent. Dev Cell. 2019;48:32–48.e5.

Desert R, Chen W, Ge X, Viel R, Han H, Athavale D, et al. Hepatocellular carcinomas, exhibiting intratumor fibrosis, express cancer-specific extracellular matrix remodeling and WNT/TGFB signatures, associated with poor outcome. Hepatology. 2023;78:741–57.

Article   PubMed   Google Scholar  

Abbasi S, Sinha S, Labit E, Rosin NL, Yoon G, Rahmani W, et al. Distinct regulatory programs control the latent regenerative potential of dermal fibroblasts during wound healing. Cell Stem Cell. 2020;27:396–412.e6.

Telerman SB, Rognoni E, Sequeira I, Pisco AO, Lichtenberger BM, Culley OJ, et al. Dermal Blimp1 acts downstream of epidermal TGFβ and Wnt/β-catenin to regulate hair follicle formation and growth. J Investig Dermatol. 2017;137:2270–81.

Chu S-Y, Chou C-H, Huang H-D, Yen M-H, Hong H-C, Chao P-H, et al. Mechanical stretch induces hair regeneration through the alternative activation of macrophages. Nat Commun. 2019;10:1524.

Ode Boni BO, Lamboni L, Souho T, Gauthier M, Yang G. Immunomodulation and cellular response to biomaterials: the overriding role of neutrophils in healing. Mater Horiz. 2019;6:1122–37.

Skelly DA, Squiers GT, McLellan MA, Bolisetty MT, Robson P, Rosenthal NA, et al. Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep. 2018;22:600–10.

Li Z, Yang Q, Tang X, Chen Y, Wang S, Qi X, et al. Single-cell RNA-seq and chromatin accessibility profiling decipher the heterogeneity of mouse γδ T cells. Sci Bull. 2022;67:408–26.

Hu Y, Fang K, Wang Y, Lu N, Sun H, Zhang C. Single-cell analysis reveals the origins and intrahepatic development of liver-resident IFN-γ-producing γδ T cells. Cell Mol Immunol. 2021;18:954–68.

MacLeod AS, Hemmers S, Garijo O, Chabod M, Mowen K, Witherden DA, et al. Dendritic epidermal T cells regulate skin antimicrobial barrier function. J Clin Investig. 2013;123:4364–74.

McKenzie DR, Kara EE, Bastow CR, Tyllis TS, Fenix KA, Gregor CE, et al. IL-17-producing γδ T cells switch migratory patterns between resting and activated states. Nat Commun. 2017;8:15632.

Gay D, Kwon O, Zhang ZK, Spata M, Plikus MV, Holler PD, et al. Fgf9 from dermal gamma delta T cells induces hair follicle neogenesis after wounding. Nat Med. 2013;19:916.

Konieczny P, Xing Y, Sidhu I, Subudhi I, Mansfield KP, Hsieh B, et al. Interleukin-17 governs hypoxic adaptation of injured epithelium. Science. 2022;377:eabg9302.

Ekman AK, Bivik Eding C, Rundquist I, Enerbäck C. IL-17 and IL-22 promote keratinocyte stemness in the germinative compartment in psoriasis. J Investig Dermatol. 2019;139:1564–73.e8.

Archer NK, Jo JH, Lee SK, Kim D, Smith B, Ortines RV, et al. Injury, dysbiosis, and filaggrin deficiency drive skin inflammation through keratinocyte IL-1α release. J Allergy Clin Immunol. 2019;143:1426–43.e6.

Akiyama M, Smith LT, Holbrook KA. Growth factor and growth factor receptor localization in the hair follicle bulge and associated tissue in human fetus. J Investig Dermatol. 1996;106:391–6.

Zaiss DMW, Gause WC, Osborne LC, Artis D. Emerging functions of amphiregulin in orchestrating immunity, inflammation, and tissue repair. Immunity. 2015;42:216–26.

Kennedy-Crispin M, Billick E, Mitsui H, Gulati N, Fujita H, Gilleaudeau P, et al. Human keratinocytes’ response to injury upregulates CCL20 and other genes linking innate and adaptive immunity. J Investig Dermatol. 2012;132:105–13.

Yue Z, Lei M, Paus R, Chuong CM. The global regulatory logic of organ regeneration: circuitry lessons from skin and its appendages. Biol Rev Camb Philos Soc. 2021;96:2573–83.

Brockes JP, Kumar A, Velloso CP. Regeneration as an evolutionary variable. J Anat. 2001;199:3–11.

Wynn TA, Vannella KM. Macrophages in tissue repair, regeneration, and fibrosis. Immunity. 2016;44:450–62.

Takeuchi O, Akira S. Pattern recognition receptors and inflammation. Cell. 2010;140:805–20.

Pacini L, Ceraolo MG, Venuti A, Melita G, Hasan UA, Accardi R, et al. UV radiation activates Toll-like receptor 9 expression in primary human keratinocytes, an event inhibited by human papillomavirus 38 E6 and E7 oncoproteins. J Virol. 2017;91:e01123–17.

Selleri S, Arnaboldi F, Palazzo M, Gariboldi S, Zanobbio L, Opizzi E, et al. Toll-like receptor agonists regulate beta-defensin 2 release in hair follicle. Br J Dermatol. 2007;156:1172–7.

Wier E, Asada M, Wang G, Alphonse MP, Li A, Hintelmann C, et al. Neutrophil extracellular traps impair regeneration. J Cell Mol Med. 2021;25:10008–19.

Girardi M, Oppenheim DE, Steele CR, Lewis JM, Glusac E, Filler R, et al. Regulation of cutaneous malignancy by gammadelta T cells. Science. 2001;294:605–9.

Lee P, Gund R, Dutta A, Pincha N, Rana I, Ghosh S, et al. Stimulation of hair follicle stem cell proliferation through an IL-1 dependent activation of γδT-cells. eLife 2017;6:e28875.

Purvis JE, Lahav G. Encoding and decoding cellular information through signaling dynamics. Cell. 2013;152:945–56.

Richardson GD, Bazzi H, Fantauzzo KA, Waters JM, Crawford H, Hynd P, et al. KGF and EGF signalling block hair follicle induction and promote interfollicular epidermal fate in developing mouse skin. Development. 2009;136:2153–64.

Lu Q, Gao Y, Fan Z, Xiao X, Chen Y, Si Y, et al. Amphiregulin promotes hair regeneration of skin-derived precursors via the PI3K and MAPK pathways. Cell Prolif. 2021;54:e13106.

Harmon C, Zaborowski A, Moore H, St, Louis P, Slattery K, et al. γδ T cell dichotomy with opposing cytotoxic and wound healing functions in human solid tumors. Nat Cancer. 2023;4:1122–37.

Fischer MA, Golovchenko NB, Edelblum KL. γδ T cell migration: separating trafficking from surveillance behaviors at barrier surfaces. Immunol Rev. 2020;298:165–80.

Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–U251.

Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14:979–82.

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The study was funded in part by National Natural Science Foundation of China (No. 82201106); National Natural Science Foundation of China (No. 82271015); Sichuan Science and Technology Program (2023JDRC0107), Research and Develop Program, West China Hospital of Stomatology Sichuan University, and Research Funding from West China School/Hospital of Stomatology, Sichuan University (No. RCDWJS2023-8). We thank the National Clinical Research Center for Oral Diseases & State Key Laboratory of Oral Diseases for cell and animal experiments. We thank Zilin Zhong for his help with data analysis.

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State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China

Xinhui Li, Tiantian An, Yang Yang, Zhaoyu Xu, Shuaidong Chen, Zumu Yi, Chen Deng, Feng Zhou, Yi Man & Chen Hu

Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China

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Conceptualization: XHL, CH. Methodology: XHL, ZMY, SDC, TTA. Investigation: XHL, ZMY, SDC, TTA, ZYX, FZ, CD. Visualization: XHL. Supervision: CH, YM. Writing—original draft: XHL. Writing—review & editing: XHL, CH.

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Li, X., An, T., Yang, Y. et al. TLR9 activation in large wound induces tissue repair and hair follicle regeneration via γδT cells. Cell Death Dis 15 , 598 (2024). https://doi.org/10.1038/s41419-024-06994-y

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Received : 03 May 2024

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Published : 17 August 2024

DOI : https://doi.org/10.1038/s41419-024-06994-y

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Analysing near-miss incidents in construction: a systematic literature review.

1. Introduction

• Q 1 —Are near-miss events in construction industry the subject of scientific research?
• Q 2 —What methods have been employed thus far to obtain information on near misses and systems for recording incidents in construction companies?
• Q 3 —What methods have been used to analyse the information and figures obtained?
• Q 4 —What are the key aspects of near misses in the construction industry that have been of interest to the researchers?

2. Definition of Near-Miss Events

3. research methodology, 4.1. a statistical analysis of publications, 4.2. methods used to obtain information about near misses, 4.2.1. traditional methods.

• Traditional registration forms
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4.2.2. Real-Time Monitoring Systems

• Employee-tracking systems
• Video surveillance systems
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• Motion sensors

4.3. Methods Used to Analyse the Information and Figures That Have Been Obtained

4.3.1. quantitative and qualitative statistical methods, 4.3.2. analysis using artificial intelligence (ai), 4.3.3. building information modelling, 4.4. key aspects of near-miss investigations in the construction industry, 4.4.1. occupational risk assessment, 4.4.2. causes of hazards in construction, 4.4.3. time series of near misses, 4.4.4. material factors of construction processes, 4.5. a comprehensive overview of the research questions and references on near misses in the construction industry, 5. discussion, 5.1. interest of researchers in near misses in construction (question 1), 5.2. methods used to obtain near-miss information (question 2), 5.3. methods used to analyse the information and data sets (question 3), 5.4. key aspects of near-miss investigations in the construction industry (question 4), 6. conclusions.

• A quantitative analysis of the Q 1 question has revealed a positive trend, namely that there is a growing interest among researchers in studying near misses in construction. The greatest interest in NM topics is observed in the United States of America, China, the United Kingdom, Australia, Hong Kong, and Germany. Additionally, there has been a recent emergence of interest in Poland. The majority of articles are mainly published in journals such as Safety Science (10), Journal of Construction Engineering and Management (8), and Automation in Construction (5);
• The analysis of question Q 2 illustrates that traditional paper-based event registration systems are currently being superseded by advanced IT systems. However, both traditional and advanced systems are subject to the disadvantage of relying on employee-reported data, which introduces a significant degree of uncertainty regarding in the quality of the information provided. A substantial proportion of the data and findings presented in the studies was obtained through surveys and interviews. The implementation of real-time monitoring systems is becoming increasingly prevalent in construction sites. The objective of such systems is to provide immediate alerts in the event of potential hazards, thereby preventing a significant number of near misses. Real-time monitoring systems employ a range of technologies, including ultrasonic technology, radio frequency identification (RFID), inertial measurement units (IMUs), real-time location systems (RTLSs), industrial cameras, wearable technology, motion sensors, and advanced IT technologies, among others;
• The analysis of acquired near-miss data is primarily conducted through the utilisation of quantitative and qualitative statistical methods, as evidenced by the examination of the Q 3 question. In recent years, research utilising artificial intelligence (AI) has made significant advances. The most commonly employed artificial intelligence techniques include text mining, machine learning, and artificial neural networks. The growing deployment of Building Information Modelling (BIM) technology has precipitated a profound transformation in the safety management of construction sites, with the advent of sophisticated tools for the identification and management of hazardous occurrences;
• In response to question Q 4 , the study of near misses in the construction industry has identified several key aspects that have attracted the attention of researchers. These include the utilisation of both quantitative and qualitative methodologies for risk assessment, the analysis of the causes of hazards, the identification of accident precursors through the creation of time series, and the examination of material factors pertaining to construction processes. Researchers are focusing on the utilisation of both databases and advanced technologies, such as real-time location tracking, for the assessment and analysis of occupational risks. Techniques such as Analytic Hierarchy Process (AHP) and clustering facilitate a comprehensive assessment and categorisation of incidents, thereby enabling the identification of patterns and susceptibility to specific types of accidents. Moreover, the impact of a company’s safety climate and organisational culture on the frequency and characteristics of near misses represents a pivotal area of investigation. The findings of this research indicate that effective safety management requires a holistic approach that integrates technology, risk management and safety culture, with the objective of reducing accidents and enhancing overall working conditions on construction sites.

7. Gaps and Future Research Directions, Limitations

• Given the diversity and variability of construction sites and the changing conditions and circumstances of work, it is essential to create homogeneous clusters of near misses and to analyse the phenomena within these clusters. The formation of such clusters may be contingent upon the direct causes of the events in question;
• Given the inherently dynamic nature of construction, it is essential to analyse time series of events that indicate trends in development and safety levels. The numerical characteristics of these trends may be used to construct predictive models for future accidents and near misses;
• The authors have identified potential avenues for future research, which could involve the development of mathematical models using techniques such as linear regression, artificial intelligence, and machine learning. The objective of these models is to predict the probable timing of occupational accidents within defined incident categories, utilising data from near misses. Moreover, efforts are being made to gain access to the hazardous incident recording systems of different construction companies, with a view to facilitating comparison of the resulting data;
• One significant limitation of near-miss research is the lack of an integrated database that encompasses a diverse range of construction sites and construction work. A data resource of this nature would be of immense value for the purpose of conducting comprehensive analyses and formulating effective risk management strategies. This issue can be attributed to two factors: firstly, the reluctance of company managers to share their databases with researchers specialising in risk assessment, and secondly, the reluctance of employees to report near-miss incidents. Such actions may result in adverse consequences for employees, including disciplinary action or negative perceptions from managers. This consequently results in the recording of only a subset of incidents, thereby distorting the true picture of safety on the site.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Occupational Risk|Safety and Health at Work EU-OSHA. Available online: https://osha.europa.eu/en/tools-and-resources/eu-osha-thesaurus/term/70194i (accessed on 28 June 2023).
• Guo, S.; Zhou, X.; Tang, B.; Gong, P. Exploring the Behavioral Risk Chains of Accidents Using Complex Network Theory in the Construction Industry. Phys. A Stat. Mech. Its Appl. 2020 , 560 , 125012. [ Google Scholar ] [ CrossRef ]
• Woźniak, Z.; Hoła, B. The Structure of near Misses and Occupational Accidents in the Polish Construction Industry. Heliyon 2024 , 10 , e26410. [ Google Scholar ] [ CrossRef ]
• Li, X.; Sun, W.; Fu, H.; Bu, Q.; Zhang, Z.; Huang, J.; Zang, D.; Sun, Y.; Ma, Y.; Wang, R.; et al. Schedule Risk Model of Water Intake Tunnel Construction Considering Mood Factors and Its Application. Sci. Rep. 2024 , 14 , 3857. [ Google Scholar ] [ CrossRef ]
• Li, X.; Huang, J.; Li, C.; Luo, N.; Lei, W.; Fan, H.; Sun, Y.; Chen, W. Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network. Period. Polytech. Civ. Eng. 2022 , 66 , 335–343. [ Google Scholar ] [ CrossRef ]
• Central Statistical Office Central Statistical Office/Thematic Areas/Labor Market/Working Conditions/Accidents at Work/Accidents at Work in the 1st Quarter of 2024. Available online: https://stat.gov.pl/obszary-tematyczne/rynek-pracy/warunki-pracy-wypadki-przy-pracy/wypadki-przy-pracy-w-1-kwartale-2024-roku,3,55.html (accessed on 17 July 2024).
• Manzo, J. The $ 5 Billion Cost of Construction Fatalities in the United States: A 50 State Comparison ; The Midwest Economic Policy Institute (MEPI): Saint Paul, MN, USA, 2017. [ Google Scholar ]
• Sousa, V.; Almeida, N.M.; Dias, L.A. Risk-Based Management of Occupational Safety and Health in the Construction Industry—Part 1: Background Knowledge. Saf. Sci. 2014 , 66 , 75–86. [ Google Scholar ] [ CrossRef ]
• Amirah, N.A.; Him, N.F.N.; Rashid, A.; Rasheed, R.; Zaliha, T.N.; Afthanorhan, A. Fostering a Safety Culture in Manufacturing through Safety Behavior: A Structural Equation Modelling Approach. J. Saf. Sustain. 2024; in press . [ Google Scholar ] [ CrossRef ]
• Heinrich, H.W. Industrial Accident Prevention ; A Scientific Approach; McGraw-Hill: New York, NY, USA, 1931. [ Google Scholar ]
• Near Miss Definition Per OSHA—What Is a Near Miss? Available online: https://safetystage.com/osha-compliance/near-miss-definition-osha/ (accessed on 17 August 2024).
• Cambraia, F.B.; Saurin, T.A.; Formoso, C.T. Identification, Analysis and Dissemination of Information on near Misses: A Case Study in the Construction Industry. Saf. Sci. 2010 , 48 , 91–99. [ Google Scholar ] [ CrossRef ]
• Tan, J.; Li, M. How to Achieve Accurate Accountability under Current Administrative Accountability System for Work Safety Accidents in Chemical Industry in China: A Case Study on Major Work Safety Accidents during 2010–2020. J. Chin. Hum. Resour. Manag. 2022 , 13 , 26–40. [ Google Scholar ] [ CrossRef ]
• Wu, W.; Gibb, A.G.F.; Li, Q. Accident Precursors and near Misses on Construction Sites: An Investigative Tool to Derive Information from Accident Databases. Saf. Sci. 2010 , 48 , 845–858. [ Google Scholar ] [ CrossRef ]
• Janicak, C.A. Fall-Related Deaths in the Construction Industry. J. Saf. Res. 1998 , 29 , 35–42. [ Google Scholar ] [ CrossRef ]
• Li, H.; Yang, X.; Wang, F.; Rose, T.; Chan, G.; Dong, S. Stochastic State Sequence Model to Predict Construction Site Safety States through Real-Time Location Systems. Saf. Sci. 2016 , 84 , 78–87. [ Google Scholar ] [ CrossRef ]
• Yang, K.; Aria, S.; Ahn, C.R.; Stentz, T.L. Automated Detection of Near-Miss Fall Incidents in Iron Workers Using Inertial Measurement Units. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19–21 May 2014; pp. 935–944. [ Google Scholar ] [ CrossRef ]
• Raviv, G.; Fishbain, B.; Shapira, A. Analyzing Risk Factors in Crane-Related near-Miss and Accident Reports. Saf. Sci. 2017 , 91 , 192–205. [ Google Scholar ] [ CrossRef ]
• Zhao, X.; Zhang, M.; Cao, T. A Study of Using Smartphone to Detect and Identify Construction Workers’ near-Miss Falls Based on ANN. In Proceedings of the Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII, Denver, CO, USA, 4–8 March 2018; p. 80. [ Google Scholar ] [ CrossRef ]
• Santiago, K.; Yang, X.; Ruano-Herreria, E.C.; Chalmers, J.; Cavicchia, P.; Caban-Martinez, A.J. Characterising near Misses and Injuries in the Temporary Agency Construction Workforce: Qualitative Study Approach. Occup. Environ. Med. 2020 , 77 , 94–99. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• What Is OSHA’s Definition of a Near Miss. Available online: https://www.osha.com/blog/near-miss-definition (accessed on 4 August 2023).
• Martins, I. Investigation of Occupational Accidents and Diseases a Practical Guide for Labour Inspectors ; International Labour Office: Geneva, Switzerland, 2015. [ Google Scholar ]
• National Safety Council. Near Miss Reporting Systems ; National Safety Council: Singapore, 2013. [ Google Scholar ]
• PKN PN-ISO 45001:2018-06 ; Occupational Health and Safety Management Systems—Requirements with Guidance for Use. CRC Press: Boca Raton, FL, USA, 2019.
• PKN PN-N-18001:2004 ; Occupational Health and Safety Management Systems—Requirements. CRC Press: Boca Raton, FL, USA, 2004.
• World Health Organisation. WHO Draft GuiDelines for Adverse Event Reporting and Learning Systems ; World Health Organisation: Geneva, Switzerland, 2005. [ Google Scholar ]
• International Atomic Energy Agency IAEA Satety Glossary. Terminology Used in Nuclear Safety and Radiation Protection: 2007 Edition ; International Atomic Energy Agency: Vienna, Austria, 2007. [ Google Scholar ]
• Marks, E.; Teizer, J.; Hinze, J. Near Miss Reporting Program to Enhance Construction Worker Safety Performance. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19 May 2014; pp. 2315–2324. [ Google Scholar ] [ CrossRef ]
• Gnoni, M.G.; Saleh, J.H. Near-Miss Management Systems and Observability-in-Depth: Handling Safety Incidents and Accident Precursors in Light of Safety Principles. Saf. Sci. 2017 , 91 , 154–167. [ Google Scholar ] [ CrossRef ]
• Thoroman, B.; Goode, N.; Salmon, P. System Thinking Applied to near Misses: A Review of Industry-Wide near Miss Reporting Systems. Theor. Issues Ergon. Sci. 2018 , 19 , 712–737. [ Google Scholar ] [ CrossRef ]
• Gnoni, M.G.; Tornese, F.; Guglielmi, A.; Pellicci, M.; Campo, G.; De Merich, D. Near Miss Management Systems in the Industrial Sector: A Literature Review. Saf. Sci. 2022 , 150 , 105704. [ Google Scholar ] [ CrossRef ]
• Bird, F. Management Guide to Loss Control ; Loss Control Publications: Houston, TX, USA, 1975. [ Google Scholar ]
• Zimmermann. Bauer International Norms and Identity ; Zimmermann: Sydney, NSW, Australia, 2006; pp. 5–21. [ Google Scholar ]
• Arslan, M.; Cruz, C.; Ginhac, D. Semantic Trajectory Insights for Worker Safety in Dynamic Environments. Autom. Constr. 2019 , 106 , 102854. [ Google Scholar ] [ CrossRef ]
• Arslan, M.; Cruz, C.; Ginhac, D. Visualizing Intrusions in Dynamic Building Environments for Worker Safety. Saf. Sci. 2019 , 120 , 428–446. [ Google Scholar ] [ CrossRef ]
• Zhou, C.; Chen, R.; Jiang, S.; Zhou, Y.; Ding, L.; Skibniewski, M.J.; Lin, X. Human Dynamics in Near-Miss Accidents Resulting from Unsafe Behavior of Construction Workers. Phys. A Stat. Mech. Its Appl. 2019 , 530 , 121495. [ Google Scholar ] [ CrossRef ]
• Chen, F.; Wang, C.; Wang, J.; Zhi, Y.; Wang, Z. Risk Assessment of Chemical Process Considering Dynamic Probability of near Misses Based on Bayesian Theory and Event Tree Analysis. J. Loss Prev. Process Ind. 2020 , 68 , 104280. [ Google Scholar ] [ CrossRef ]
• Wright, L.; Van Der Schaaf, T. Accident versus near Miss Causation: A Critical Review of the Literature, an Empirical Test in the UK Railway Domain, and Their Implications for Other Sectors. J. Hazard. Mater. 2004 , 111 , 105–110. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Saleh, J.H.; Saltmarsh, E.A.; Favar, F.M.; Loı¨c Brevault, L. Accident Precursors, near Misses, and Warning Signs: Critical Review and Formal Definitions within the Framework of Discrete Event Systems. Reliab. Eng. Syst. Saf. 2013 , 114 , 148–154. [ Google Scholar ] [ CrossRef ]
• Fred, A. Manuele Reviewing Heinrich. Am. Soc. Saf. Prof. 2011 , 56 , 52–61. [ Google Scholar ]
• Love, P.E.D.; Tenekedjiev, K. Understanding Near-Miss Count Data on Construction Sites Using Greedy D-Vine Copula Marginal Regression: A Comment. Reliab. Eng. Syst. Saf. 2022 , 217 , 108021. [ Google Scholar ] [ CrossRef ]
• Jan van Eck, N.; Waltman, L. VOSviewer Manual ; Universiteit Leiden: Leiden, The Netherlands, 2015. [ Google Scholar ]
• Scopus. Content Coverage Guide ; Elsevier: Amsterdam, The Netherlands, 2023; pp. 1–24. [ Google Scholar ]
• Lukic, D.; Littlejohn, A.; Margaryan, A. A Framework for Learning from Incidents in the Workplace. Saf. Sci. 2012 , 50 , 950–957. [ Google Scholar ] [ CrossRef ]
• Teizer, J.; Cheng, T. Proximity Hazard Indicator for Workers-on-Foot near Miss Interactions with Construction Equipment and Geo-Referenced Hazard Area. Autom. Constr. 2015 , 60 , 58–73. [ Google Scholar ] [ CrossRef ]
• Zong, L.; Fu, G. A Study on Designing No-Penalty Reporting System about Enterprise Staff’s near Miss. Adv. Mater. Res. 2011 , 255–260 , 3846–3851. [ Google Scholar ] [ CrossRef ]
• Golovina, O.; Teizer, J.; Pradhananga, N. Heat Map Generation for Predictive Safety Planning: Preventing Struck-by and near Miss Interactions between Workers-on-Foot and Construction Equipment. Autom. Constr. 2016 , 71 , 99–115. [ Google Scholar ] [ CrossRef ]
• Zou, P.X.W.; Lun, P.; Cipolla, D.; Mohamed, S. Cloud-Based Safety Information and Communication System in Infrastructure Construction. Saf. Sci. 2017 , 98 , 50–69. [ Google Scholar ] [ CrossRef ]
• Hinze, J.; Godfrey, R. An Evaluation of Safety Performance Measures for Construction Projects. J. Constr. Res. 2011 , 4 , 5–15. [ Google Scholar ] [ CrossRef ]
• Construction Inspection Software|IAuditor by SafetyCulture. Available online: https://safetyculture.com/construction/ (accessed on 25 August 2023).
• Incident Reporting Made Easy|Safety Compliance|Mobile EHS Solutions. Available online: https://www.safety-reports.com/lp/safety/incident/ (accessed on 25 August 2023).
• Wu, F.; Wu, T.; Yuce, M.R. An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications. Sensors 2019 , 19 , 21. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fang, W.; Luo, H.; Xu, S.; Love, P.E.D.; Lu, Z.; Ye, C. Automated Text Classification of Near-Misses from Safety Reports: An Improved Deep Learning Approach. Adv. Eng. Inform. 2020 , 44 , 101060. [ Google Scholar ] [ CrossRef ]
• Gatti, U.C.; Lin, K.-Y.; Caldera, C.; Chiang, R. Exploring the Relationship between Chronic Sleep Deprivation and Safety on Construction Sites. In Proceedings of the Construction Research Congress 2014: Construction in a Global Network, Atlanta, GA, USA, 19–24 May 2014; pp. 1772–1781. [ Google Scholar ] [ CrossRef ]
• Hon, C.K.H.; Chan, A.P.C.; Yam, M.C.H. Relationships between Safety Climate and Safety Performance of Building Repair, Maintenance, Minor Alteration, and Addition (RMAA) Works. Saf. Sci. 2014 , 65 , 10–19. [ Google Scholar ] [ CrossRef ]
• Oni, O.; Olanrewaju, A.; Cheen, K.S. Accidents at construction sites and near-misses: A constant problem. Int. Struct. Eng. Constr. 2022 , 9 , 2022. [ Google Scholar ] [ CrossRef ]
• Wu, W.; Yang, H.; Chew, D.A.S.; Yang, S.-H.; Gibb, A.G.F.; Li, Q. Towards an Autonomous Real-Time Tracking System of near-Miss Accidents on Construction Sites. Autom. Constr. 2010 , 19 , 134–141. [ Google Scholar ] [ CrossRef ]
• Aria, S.S.; Yang, K.; Ahn, C.R.; Vuran, M.C. Near-Miss Accident Detection for Ironworkers Using Inertial Measurement Unit Sensors. In Proceedings of the International Symposium on Automation and Robotics in Construction, ISARC 2014, Sydney, Australia, 9–11 July 2014; Volume 31, pp. 854–859. [ Google Scholar ] [ CrossRef ]
• Hasanzadeh, S.; Garza, J.M. de la Productivity-Safety Model: Debunking the Myth of the Productivity-Safety Divide through a Mixed-Reality Residential Roofing Task. J. Constr. Eng. Manag. 2020 , 146 , 04020124. [ Google Scholar ] [ CrossRef ]
• Teizer, J. Magnetic Field Proximity Detection and Alert Technology for Safe Heavy Construction Equipment Operation. In Proceedings of the 32nd International Symposium on Automation and Robotics in Construction, Oulu, Finland, 15–18 June 2015. [ Google Scholar ] [ CrossRef ]
• Mohajeri, M.; Ardeshir, A.; Banki, M.T.; Malekitabar, H. Discovering Causality Patterns of Unsafe Behavior Leading to Fall Hazards on Construction Sites. Int. J. Constr. Manag. 2022 , 22 , 3034–3044. [ Google Scholar ] [ CrossRef ]
• Kisaezehra; Farooq, M.U.; Bhutto, M.A.; Kazi, A.K. Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites. Intell. Autom. Soft Comput. 2023 , 36 , 911–927. [ Google Scholar ] [ CrossRef ]
• Li, C.; Ding, L. Falling Objects Detection for near Miss Incidents Identification on Construction Site. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 138–145. [ Google Scholar ] [ CrossRef ]
• Jeelani, I.; Ramshankar, H.; Han, K.; Albert, A.; Asadi, K. Real-Time Hazard Proximity Detection—Localization of Workers Using Visual Data. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 281–289. [ Google Scholar ] [ CrossRef ]
• Lim, T.-K.; Park, S.-M.; Lee, H.-C.; Lee, D.-E. Artificial Neural Network–Based Slip-Trip Classifier Using Smart Sensor for Construction Workplace. J. Constr. Eng. Manag. 2015 , 142 , 04015065. [ Google Scholar ] [ CrossRef ]
• Yang, K.; Jebelli, H.; Ahn, C.R.; Vuran, M.C. Threshold-Based Approach to Detect Near-Miss Falls of Iron Workers Using Inertial Measurement Units. In Proceedings of the 2015 International Workshop on Computing in Civil Engineering, Austin, TX, USA, 21–23 June 2015; 2015; 2015, pp. 148–155. [ Google Scholar ] [ CrossRef ]
• Yang, K.; Ahn, C.R.; Vuran, M.C.; Aria, S.S. Semi-Supervised near-Miss Fall Detection for Ironworkers with a Wearable Inertial Measurement Unit. Autom. Constr. 2016 , 68 , 194–202. [ Google Scholar ] [ CrossRef ]
• Raviv, G.; Shapira, A.; Fishbain, B. AHP-Based Analysis of the Risk Potential of Safety Incidents: Case Study of Cranes in the Construction Industry. Saf. Sci. 2017 , 91 , 298–309. [ Google Scholar ] [ CrossRef ]
• Saurin, T.A.; Formoso, C.T.; Reck, R.; Beck da Silva Etges, B.M.; Ribeiro JL, D. Findings from the Analysis of Incident-Reporting Systems of Construction Companies. J. Constr. Eng. Manag. 2015 , 141 , 05015007. [ Google Scholar ] [ CrossRef ]
• Williams, E.; Sherratt, F.; Norton, E. Exploring the Value in near Miss Reporting for Construction Safety. In Proceedings of the 37th Annual Conference, Virtual Event, 6–10 December 2021; pp. 319–328. [ Google Scholar ]
• Baker, H.; Smith, S.; Masterton, G.; Hewlett, B. Data-Led Learning: Using Natural Language Processing (NLP) and Machine Learning to Learn from Construction Site Safety Failures. In Proceedings of the 36th Annual ARCOM Conference, Online, 7–8 September 2020; pp. 356–365. [ Google Scholar ]
• Jin, R.; Wang, F.; Liu, D. Dynamic Probabilistic Analysis of Accidents in Construction Projects by Combining Precursor Data and Expert Judgments. Adv. Eng. Inform. 2020 , 44 , 101062. [ Google Scholar ] [ CrossRef ]
• Zhou, Z.; Li, C.; Mi, C.; Qian, L. Exploring the Potential Use of Near-Miss Information to Improve Construction Safety Performance. Sustainability 2019 , 11 , 1264. [ Google Scholar ] [ CrossRef ]
• Boateng, E.B.; Pillay, M.; Davis, P. Predicting the Level of Safety Performance Using an Artificial Neural Network. Adv. Intell. Syst. Comput. 2019 , 876 , 705–710. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Cao, T.; Zhao, X. Using Smartphones to Detect and Identify Construction Workers’ Near-Miss Falls Based on ANN. J. Constr. Eng. Manag. 2018 , 145 , 04018120. [ Google Scholar ] [ CrossRef ]
• Gadekar, H.; Bugalia, N. Automatic Classification of Construction Safety Reports Using Semi-Supervised YAKE-Guided LDA Approach. Adv. Eng. Inform. 2023 , 56 , 101929. [ Google Scholar ] [ CrossRef ]
• Zhu, Y.; Liao, H.; Huang, D. Using Text Mining and Multilevel Association Rules to Process and Analyze Incident Reports in China. Accid. Anal. Prev. 2023 , 191 , 107224. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, M.; Lin, Q.; Jin, H. Research on Near-Miss Incidents Monitoring and Early Warning System for Building Construction Sites Based on Blockchain Technology. J. Constr. Eng. Manag. 2023 , 149 , 04023124. [ Google Scholar ] [ CrossRef ]
• Chung, W.W.S.; Tariq, S.; Mohandes, S.R.; Zayed, T. IoT-Based Application for Construction Site Safety Monitoring. Int. J. Constr. Manag. 2020 , 23 , 58–74. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Xu, F.; Zhang, Z.; Sun, K. Fall-Portent Detection for Construction Sites Based on Computer Vision and Machine Learning. Eng. Constr. Archit. Manag. 2023; ahead-of-print . [ Google Scholar ] [ CrossRef ]
• Abbasi, H.; Guerrieri, A.; Lee, J.; Yang, K. Mobile Device-Based Struck-By Hazard Recognition in Construction Using a High-Frequency Sound. Sensors 2022 , 22 , 3482. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, F.; Li, H.; Dong, C. Understanding Near-Miss Count Data on Construction Sites Using Greedy D-Vine Copula Marginal Regression. Reliab. Eng. Syst. Saf. 2021 , 213 , 107687. [ Google Scholar ] [ CrossRef ]
• Bugalia, N.; Tarani, V.; Student, G.; Kedia, J.; Gadekar, H. Machine Learning-Based Automated Classification Of Worker-Reported Safety Reports In Construction. J. Inf. Technol. Constr. 2022 , 27 , 926–950. [ Google Scholar ] [ CrossRef ]
• Chen, S.; Xi, J.; Chen, Y.; Zhao, J. Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification. Comput. Intell. Neurosci. 2022 , 2022 , 4851615. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tang, S.; Golparvar-Fard, M.; Naphade, M.; Gopalakrishna, M.M. Video-Based Activity Forecasting for Construction Safety Monitoring Use Cases. In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019; pp. 204–210. [ Google Scholar ] [ CrossRef ]
• Rashid, K.M.; Behzadan, A.H. Risk Behavior-Based Trajectory Prediction for Construction Site Safety Monitoring. J. Constr. Eng. Manag. 2018 , 144 , 04017106. [ Google Scholar ] [ CrossRef ]
• Shen, X.; Marks, E. Near-Miss Information Visualization Tool in BIM for Construction Safety. J. Constr. Eng. Manag. 2016 , 142 , 04015100. [ Google Scholar ] [ CrossRef ]
• Erusta, N.E.; Sertyesilisik, B. An Investigation into Improving Occupational Health and Safety Performance of Construction Projects through Usage of BIM for Lean Management. In Communications in Computer and Information Science (CCIS) ; Springer: Berlin/Heidelberg, Germany, 2020; Volume 1188, pp. 91–100. [ Google Scholar ] [ CrossRef ]
• Coffland, M.M.; Kim, A.; Sadatsafavi, H.; Uber, M.M. Improved Data Storage for Better Safety Analysis and Decision Making in Large Construction Management Firms. Available online: https://www.researchgate.net/publication/320474383_Improved_Data_Storage_for_Better_Safety_Analysis_and_Decision_Making_in_Large_Construction_Management_Firms (accessed on 12 June 2024).
• Zhou, Z.; Li, Q.; Wu, W. Developing a Versatile Subway Construction Incident Database for Safety Management. J. Constr. Eng. Manag. 2011 , 138 , 1169–1180. [ Google Scholar ] [ CrossRef ]
• Wu, W.; Yang, H.; Li, Q.; Chew, D. An Integrated Information Management Model for Proactive Prevention of Struck-by-Falling-Object Accidents on Construction Sites. Autom. Constr. 2013 , 34 , 67–74. [ Google Scholar ] [ CrossRef ]
• Hoła, B. Identification and Evaluation of Processes in a Construction Enterprise. Arch. Civ. Mech. Eng. 2015 , 15 , 419–426. [ Google Scholar ] [ CrossRef ]
• Zhou, C.; Ding, L.; Skibniewski, M.J.; Luo, H.; Jiang, S. Characterizing Time Series of Near-Miss Accidents in Metro Construction via Complex Network Theory. Saf. Sci. 2017 , 98 , 145–158. [ Google Scholar ] [ CrossRef ]
• Woźniak, Z.; Hoła, B. Time Series Analysis of Hazardous Events Based on Data Recorded in a Polish Construction Company. Arch. Civ. Eng. 2024; in process . [ Google Scholar ]
• Drozd, W. Characteristics of Construction Site in Terms of Occupational Safety. J. Civ. Eng. Environ. Archit. 2016 , 63 , 165–172. [ Google Scholar ]
• Meliá, J.L.; Mearns, K.; Silva, S.A.; Lima, M.L. Safety Climate Responses and the Perceived Risk of Accidents in the Construction Industry. Saf. Sci. 2008 , 46 , 949–958. [ Google Scholar ] [ CrossRef ]
• Bugalia, N.; Maemura, Y.; Ozawa, K. A System Dynamics Model for Near-Miss Reporting in Complex Systems. Saf. Sci. 2021 , 142 , 105368. [ Google Scholar ] [ CrossRef ]
• Gyi, D.E.; Gibb, A.G.F.; Haslam, R.A. The Quality of Accident and Health Data in the Construction Industry: Interviews with Senior Managers. Constr. Manag. Econ. 1999 , 17 , 197–204. [ Google Scholar ] [ CrossRef ]
• Menzies, J. Structural Safety: Learning and Warnings. Struct. Eng. 2002 , 80 , 15–16. [ Google Scholar ]
• Fullerton, C.E.; Allread, B.S.; Teizer, J. Pro-Active-Real-Time Personnel Warning System. In Proceedings of the Construction Research Congress 2009: Building a Sustainable Future, Seattle, WA, USA, 5–7 April 2009; pp. 31–40. [ Google Scholar ] [ CrossRef ]
• Marks, E.D.; Wetherford, J.E.; Teizer, J.; Yabuki, N. Potential of Leading Indicator Data Collection and Analysis for Proximity Detection and Alert Technology in Construction. In Proceedings of the 30th ISARC—International Symposium on Automation and Robotics in Construction Conference, Montreal, QC, Canada, 11–15 August 2013; pp. 1029–1036. [ Google Scholar ] [ CrossRef ]
• Martin, H.; Lewis, T.M. Pinpointing Safety Leadership Factors for Safe Construction Sites in Trinidad and Tobago. J. Constr. Eng. Manag. 2014 , 140 , 04013046. [ Google Scholar ] [ CrossRef ]
• Hobson, P.; Emery, D.; Brown, L.; Bashford, R.; Gill, J. People–Plant Interface Training: Targeting an Industry Fatal Risk. Proc. Inst. Civ. Eng. Civ. Eng. 2014 , 167 , 138–144. [ Google Scholar ] [ CrossRef ]
• Marks, E.; Mckay, B.; Awolusi, I. Using near Misses to Enhance Safety Performance in Construction. In Proceedings of the ASSE Professional Development Conference and Exposition, Dallas, TX, USA, 7–10 June 2015. [ Google Scholar ]
• Popp, J.D.; Scarborough, M.S. Investigations of near Miss Incidents—New Facility Construction and Commissioning Activities. IEEE Trans. Ind. Appl. 2016 , 53 , 615–621. [ Google Scholar ] [ CrossRef ]
• Nickel, P.; Lungfiel, A.; Trabold, R.J. Reconstruction of near Misses and Accidents for Analyses from Virtual Reality Usability Study. In Lecture Notes in Computer Science ; Springer: Berlin/Heidelberg, Germany, 2017; Volume 10700, pp. 182–191. [ Google Scholar ] [ CrossRef ]
• Gambatese, J.A.; Pestana, C.; Lee, H.W. Alignment between Lean Principles and Practices and Worker Safety Behavior. J. Constr. Eng. Manag. 2017 , 143 , 04016083. [ Google Scholar ] [ CrossRef ]
• Van Voorhis, S.; Korman, R. Reading Signs of Trouble. Eng. News-Rec. 2017 , 278 , 14–17. [ Google Scholar ]
• Doan, D.R. Investigation of a near-miss shock incident. IEEE Trans. Ind. Appl. 2016 , 52 , 560–561. [ Google Scholar ] [ CrossRef ]
• Oswald, D.; Sherratt, F.; Smith, S. Problems with safety observation reporting: A construction industry case study. Saf. Sci. 2018 , 107 , 35–45. [ Google Scholar ] [ CrossRef ]
• Raviv, G.; Shapira, A. Systematic approach to crane-related near-miss analysis in the construction industry. Int. J. Constr. Manag. 2018 , 18 , 310–320. [ Google Scholar ] [ CrossRef ]
• Whiteoak, J.; Appleby, J. Mate, that was bloody close! A case history of a nearmiss program in the Australian construction industry. J. Health Saf. Environ. 2019 , 35 , 31–43. [ Google Scholar ]
• Duryan, M.; Smyth, H.; Roberts, A.; Rowlinson, S.; Sherratt, F. Knowledge transfer for occupational health and safety: Cultivating health and safety learning culture in construction firms. Accid. Anal. Prev. 2020 , 139 , 105496. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shaikh, A.Y.; Osei-Kyei, R.; Hardie, M. A critical analysis of safety performance indicators in construction. Int. J. Build. Pathol. Adapt. 2020 , 39 , 547–580. [ Google Scholar ] [ CrossRef ]
• Martin, H.; Mohan, N.; Ellis, L.; Dunne, S. Exploring the Role of PPE Knowledge, Attitude, and Correct Practices in Safety Outcomes on Construction Sites. J. Archit. Eng. 2021 , 27 , 05021011. [ Google Scholar ] [ CrossRef ]
• Qin, Z.; Wu, S. A simulation model of engineering construction near-miss event disclosure strategy based on evolutionary game theory. In Proceedings of the 2021 4th International Conference on Information Systems and Computer Aided Education, Dalian, China, 24–26 September 2021; pp. 2572–2577. [ Google Scholar ] [ CrossRef ]
• Alamoudi, M. The Integration of NOSACQ-50 with Importance-Performance Analysis Technique to Evaluate and Analyze Safety Climate Dimensions in the Construction Sector in Saudi Arabia. Buildings 2022 , 12 , 1855. [ Google Scholar ] [ CrossRef ]
• Herrmann, A.W. Development of CROSS in the United States. In Proceedings of the Forensic Engineering 2022: Elevating Forensic Engineering—Selected Papers from the 9th Congress on Forensic Engineering, Denver, Colorado, 4–7 November 2022; Volume 2, pp. 40–43. [ Google Scholar ] [ CrossRef ]
• Al Shaaili, M.; Al Alawi, M.; Ekyalimpa, R.; Al Mawli, B.; Al-Mamun, A.; Al Shahri, M. Near-miss accidents data analysis and knowledge dissemination in water construction projects in Oman. Heliyon 2023 , 9 , e21607. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Agnusdei, G.P.; Gnoni, M.G.; Tornese, F.; De Merich, D.; Guglielmi, A.; Pellicci, M. Application of Near-Miss Management Systems: An Exploratory Field Analysis in the Italian Industrial Sector. Safety 2023 , 9 , 47. [ Google Scholar ] [ CrossRef ]
• Duan, P.; Zhou, J. A science mapping approach-based review of near-miss research in construction. Eng. Constr. Archit. Manag. 2023 , 30 , 2582–2601. [ Google Scholar ] [ CrossRef ]

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Woźniak, Z.; Hoła, B. Analysing Near-Miss Incidents in Construction: A Systematic Literature Review. Appl. Sci. 2024 , 14 , 7260. https://doi.org/10.3390/app14167260

Woźniak Z, Hoła B. Analysing Near-Miss Incidents in Construction: A Systematic Literature Review. Applied Sciences . 2024; 14(16):7260. https://doi.org/10.3390/app14167260

Woźniak, Zuzanna, and Bożena Hoła. 2024. "Analysing Near-Miss Incidents in Construction: A Systematic Literature Review" Applied Sciences 14, no. 16: 7260. https://doi.org/10.3390/app14167260

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A comprehensive survey of deep learning-based lightweight object detection models for edge devices

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• Published: 10 August 2024
• Volume 57 , article number  242 , ( 2024 )

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• Payal Mittal 1  

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This study concentrates on deep learning-based lightweight object detection models on edge devices. Designing such lightweight object recognition models is more difficult than ever due to the growing demand for accurate, quick, and low-latency models for various edge devices. The most recent deep learning-based lightweight object detection methods are comprehensively described in this work. Information on the lightweight backbone architectures used by these object detectors has been listed. The training and inference processes concerning to deep learning applications on edge devices is being discussed. To raise readers’ awareness of this developing domain, a variety of applications for deep learning-based lightweight object detectors and related utilities have been offered. Designing potent, lightweight object detectors based on deep learning has been suggested as a counter to such problems. On well-known datasets such as MS-COCO and PASCAL-VOC, we thoroughly examine the performance of certain conventional deep learning-based lightweight object detectors.

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1 Introduction

The advancement of effective deep learning-based object detectors has been influenced by Internet of Things (IoT)-based technologies. The majority of deep object models demand too much Central Processing Unit (CPU) power and cannot be used on edge devices, despite the fact that many object detectors attain outstanding accuracy and carry out inference in real-time (Wang et al. 2021a , 2021b , 2021c , 2022 ). Exciting outcomes have already been achieved using a variety of strategies. The brief study of strategies to deployment of deep learning-based applications into edge devices include (Wang et al. 2020a , 2020b , 2020c , 2021a , 2021b , 2021c ; Véstias et al. 2020 ; Li and Ye 2023 ; Subedi et al. 2021 ):

Using a partitioning technique, since various layers may execute at different times. For example, in a fully connected or convolutional layer, divide the processing graph into offloadable tasks so that the execution time of each composite task unit is the same.

Large-scale analytics platforms require intermediate resource standardisation for data manageability and low latency, as opposed to standalone applications on mobile devices. With the provisioning of intermediate resources, deep learning-based analytics platform can determine the proportion of local processing, provided that there is a mechanism to divide the load between buffering and memory loading. The offloaded execution through efficient partitioning can reduces costs, latency, or any other issue-related aim.

Moreover, a detailed study is provided in Sect. 4.6 of manuscript. In recent years, a new field of study i.e., lightweight object detectors have emerged with the goal of developing compact, effective networks for deployments of the IoT that frequently take place in low computing or resource-constrained settings. The research community has long worked to identify the best accuracy detection models through advanced architectural searches, as developing the deep learning-based lightweight network architecture is a difficult procedure. When using these models in edge devices, such as high-performance embedded processors, the question arises regarding usage of high-end innovative applications with fewer resources. It is still not entirely possible to perform detection using a smart phone or edge devices. Although existing models available today are capable of doing this task, but their precision level is just insufficient and undesirable in real-time instances.

Edge computing, according to Gartner, is a component of an architecture of distributed computing where data processing resides near the edge where devices or individuals generate or consume that data (Hua et al. 2023 ). Because of the constant growth in data created by the IoT, edge computing was first allocated to reduce bandwidth costs for data travelling long distances. On the other hand, the emergence of real-time applications that require processing at the edge is driving the current technological advancements. Among many other benefits, data minimization at the network level can prevent bottlenecks and significantly reduce energy, bandwidth, and storage expenses. A single device is able to send data across a network, problems occur when hundreds of devices send data at once. In addition to reducing quality due to delay, it also raises bandwidth expenses and creates bottlenecks that might result in cost spikes. By acting as a local source for these systems’ data processing and storage, edge computing services and offerings assist in fixing this problem. It also serves as an edge gateway, minimizing bandwidth requirements by processing data from an edge device and sending the pertinent data back through the cloud (Jin et al. 2021 ). A key element in modern integrated real-world Artificial Intelligence (AI) systems is edge devices. IoT devices could only gather data in the beginning and send it to the cloud for processing. By putting services closer to a network’s edge, edge computing expands the possibilities of cloud computing and enables a wider range of AI services and machine learning applications. IoT computing devices, mobile devices, embedded computers, smart TVs, and other connected gadgets can all be termed edge devices. Real-time application development and deployment can be accelerated by edge computing devices through high-speed networking technologies such 5G networking. Robotics, image and video processing, intelligent video analytics, self-driving cars, medical imaging, machine vision, industrial inspection, among examples of such applications (Véstias et al. 2020 ).

Edge computing can be applied to devices that are directly connected to sensors, routers or gateways that transfer data, or small servers installed locally in a closet. There are an increasing number of edge computing use cases as well as smart devices capable of doing various activities at the edge. The range of applications for edge computing is expanding in tandem with the development of AI capabilities. The applications spanning a wide range can be found utilising edge computing (Xu et al. 2020 ). Additionally, there is a good deal of overlap among the various use cases for edge computing. In particular, edge computing functionality in traffic management systems is closely related to that of autonomous vehicles as briefly discussed below:

Industrial infrastructure

Predictive maintenance and failure detection management in industries are supported by the edge computing. When a machine or component breaks down, the capability kicks in, enabling factory workers to fix the issue or replace the part in advance and save money by preventing lost output. The architecture of edge computing can handle large amounts of data from sensors and programmable logic controllers, as well as facilitate effective communications across extremely complicated supervisory control and data gathering systems.

Huge amounts of data are produced by retail applications from different point-of-sale systems, item stocking procedures, and other company operations. Edge computing can assist in analysing this vast quantity of data and locating problems that require quick resolution. Additionally, edge computing provides a way to handle consumer data locally, preventing it from leaving the client’s residence, a privacy regulation problem that is becoming more pressing.

In order to give medical practitioners precise, timely information about a patient’s status, the healthcare and medical industries gather patient data from sensors, monitors, wearable technology, and other devices. Edge computing solutions can provide dashboards with such data so users can see all the key indications in one convenient place. AI-enabled edge computing solutions can recognise anomalous data, allowing medical personnel to respond to patient requirements quickly and with the minimal possible false alarms. Furthermore, edge computing devices can aid in addressing concerns related to patient confidentiality and data privacy by processing data locally.

Global energy

Cities and smart grid systems can monitor public buildings and facilities for improved energy efficiency in areas like lighting, heating, and clean energy use by using edge computing devices. As an illustration: edge computing devices are utilised by intelligent lighting controls to regulate individual lights for optimal efficiency and public space safety; Embedded edge computer devices are used in solar fields to detect changes in the weather and modify their position; Edge computing is used by wind farms to send sensor data to substations and link to cell towers.

Public transit systems

Only the data necessary to support in-car activities and dispatcher insights in public transportation applications can be collected and transmitted by edge computing systems deployed in buses, passenger rail systems, and paratransit vehicles.

Travel transport utilities

In order to increase convenience and safety, edge computing can control when traffic signals turn on and off, open and close additional lanes of traffic, make sure that communications are maintained in the event of a public emergency, and do other real-time tasks. The adoption of autonomous vehicles will be significantly influenced by sophisticated traffic management systems, as was previously indicated.

Advanced industries

In advanced industries, vehicle sensors and cameras can provide data to edge computing devices, which make choices in milliseconds without any latency. This fast decision making is necessary in autonomous vehicles, for safety reasons. Self-parking apps and lane-departure warning are two examples of edge computing services that are currently readily accessible. Furthermore, as more cars are able to communicate with their surroundings, a quick and responsive network will be required. In order to assist predictive maintenance, electric vehicles require constant monitoring. Edge computing can be used to manage data in this regard. Data aggregation is supported by edge computing, which reports actionable data for maintenance and performance. These above-mentioned multitude of industries investing in implacability of edge devices. These industries include travel, transport and logistics, cross-vertical, retail, public sector utilities, global energy and materials, banking insurance, infrastructure and agriculture etc. Their share representation with respect to employability in various edge computing devices is shown in Fig. 1 a (Chabas et al. 2018 ). The travel, transport and logistics holds the maximum share of 24.2%, then 13.1% in global energy markets, 10.1% in retail and advanced industries followed by less shares by other industries. We have also represented hardware costs comparisons in terms of minimum and maximum cost in case of edge computing devices for mentioned industries. The hardware value includes opportunity across the tech stack on the basis of sensors, on-device firmware, storage and processor. By 2025, the edge computing-based devices depicts $175 to $215 billion potential hardware value. The industries such as travel, transport and logistics approximate $35 to $43, cross-vertical estimated to be $32 to $40 billion, $20 to $28 billion in retail sector, $16 to $24 billion in public sector utilities, $9 to $17 billion in global energy and materials, $4 to $11 billion in infrastructure and agriculture as depicted in Fig. 1 b (Chabas et al. 2018 ). There is a dire need to focus on advancing development of lightweight object detection models to boost their employability in heterogeneous edge devices. This survey study analyses the state-of-the-art deep learning-based lightweight object identification models in order to attain excellent performance on edge devices. With equivalent accuracy, powerful lightweight object detection models offer these advantages (Kim et al. 2023 ; Huang et al. 2022 ):

Lightweight object detection models based on deep learning require less communication between edge distributed training.

Less bandwidth will be needed to export a cutting-edge detection model from the cloud to a particular application.

Deploying lightweight detectors on Field Programmable Gate Arrays (FPGAs) and other hardware with limited resources is more practical.

a Share representation of various industries embedded in edge computing devices. b Comparison of hardware costs in case of edge computing devices

1.1 Motivation

Object detection is the core concept in deploying innovative edge devices-based applications such as face detection (Li et al. 2015 ), objects tracking (Nguyen et al. 2019 ), video surveillance (Yang and Rothkrantz 2011 ), pedestrian detection (Brunetti et al. 2018 ), etc. The powerful capabilities of deep learning boost the performance of object detection in these applications. The generic deep learning-based object detection models have computational complexities such as extensive use of platform resources, more bandwidth, and large data processing pipelines (Jiao et al. 2019 ; Zhao et al. 2019 ). However, a detection network might potentially use three orders of magnitude more Floating Point Operations (FLOPs) than a classification network due to the computational complexity, making its deployment on an edge device much more difficult (Ren et al. 2018 ). The generic deep object detectors often use more network layers which eventually require high parameter tuning. Deep models have more network layers, which makes it harder for the network to detect small targets because they lose position and feature information over time. The network parameters being too large could damage the model’s effectiveness and make it challenging to implement on smart mobile terminals, which brings us to our final possible concern.

For the development of lightweight object detection on edge devices, a comprehensive assessment of the research directions related to this topic is necessary, particularly for researchers who are interested in pursuing this line of inquiry. To assess the usefulness of deep learning-based lightweight object detection on edge devices, more research is required than just a basic review of the literature. Because the proposed research can offer a comprehensive examination of the literature, it can achieve each of these objectives. A deep learning-based lightweight detection evaluation hasn’t been written about recently in the literature. There are generic and application specific surveys dedicated to deep learning-based object detectors (Jiao et al. 2019 ; Zou et al. 2023 ; Liu et al. 2020a , 2020b , 2020c , 2020d ; Mittal et al. 2020 ; Han et al. 2018 ; Zhou et al. 2021a , 2021b ) but not have consolidated study specifically for lightweight detectors for edge devices as mentioned in Table  1 . To raise readers’ understanding of this developing subject, deep learning-based lightweight object detectors on edge devices have been investigated in this work. The research of deep learning-based lightweight object identification models with regard to various backbone architectures and diverse applications on edge devices will be advanced by the release of this study. The key objectives of the survey are as follows:

To provide taxonomy of deep learning-based lightweight object detection algorithms on edge devices

To provide an analysis of deep learning-based lightweight backbone architectures for edge devices

Literature findings of applications deployed through lightweight object detectors

Comparison of lightweight object detectors by analyzing results on leading detection datasets

The organization of research paper is as follows: Sect.  2 elaborates the work related to development of deep learning-based object detectors. The deep learning-based object detectors have further categorized into two, one and advanced stage. Section  3 describes materials and methods required for deep learning-based lightweight detection models on edge devices. The architectural details related to training and inference lightweight models have also been mentioned in this section. Further, detailed crucial properties and performance milestone of lightweight object detection methods have been mentioned in this section. Section  4 discusses commonly utilized backbone architectures in deep learning-based lightweight object detection models. Further, applications of lightweight object detection models have also been mentioned. The recommendations for designing powerful deep learning-based lightweight model are provided in Sect.  4 . The final section brings the entire study to a close and outlines some crucial implications for more research.

2 Background

Recent developments in the field of deep learning-based object detectors have mostly concentrated on raising the benchmark datasets’ state-of-the-art accuracy, which has caused an explosion in model size and parameters. The research, on the other hand, has demonstrated interest in suggesting lighter, smaller, and smarter networks that would minimise the parameters while keeping cutting-edge performance (Nguyen et al. 2023 ). In the next section, we will provide a brief summary regarding categorization of generic object detection models.

2.1 Taxonomy of deep learning-based object detectors

During the last years, there has been a rapid and successful expansion in lightweight object detection research domain. This domain has exploded from adopting and familiarizing the latest machine and deep methods through development of new representations. The generic deep learning-based object detection models have been classified into two, one and, advanced stage each having different concepts.

2.1.1 Two-stage object detection models

Two-stage algorithms, having two different stages of region proposal and detection head. The first stage was for the calculation of RoI proposals using anchors in external region proposal techniques such as Edge Box (Zitnick and Dollár 2014 ) or Selective Search (Uijlings et al. 2013 ). The second stage consists of processing extracted RoIs into final bounding boxes, coordinate values and class labels. The examples of two-stage algorithms include Faster RCNN (Ren et al. 2015 ), Cascade RCNN (Cai and Vasconcelos 2018 ), R-FCN (Dai et. al. 2016 ) etc. The advantages of two-stage object detectors include better analysis of objects through given stages, multi-stage architecture to regress the bounding box values efficiently and better handling of class imbalance in datasets. Two-stage detectors adopted a deep neural-based Region Proposal Network (RPN) and a detection head. Even if the existing Light-Head R-CNN (Li et al. 2017 ) used a lightweight detection head, the backbone and detection part become imbalanced when the detection part is combined with a small backbone. This mismatch increases the danger of overfitting and causes repetitive calculation.

2.1.2 One-stage object detection models

Two-stage detectors helped deep learning-based object detection get off to a good start, but these systems struggled with speed. Due to their flexibility in satisfying demands like fast speed and minimal memory needs, one-stage detectors were ultimately adopted by researchers. The region proposal stage of two-stage detectors was eliminated by the one-stage algorithms since they saw the object identification problem as a regression problem. Instead of sending portions of the image to a fixed grid-based CNN, the entire image is sent at once, and anchors assist in identifying specific region suggestions. For the purpose of detecting the given area in a picture, boundary box coordinates were included. The examples of one-stage detectors include YOLO (Redmon et al. 2016 ), SSD (Liu et al. 2016 ), RetinaNet (Lin et al. 2017a , 2017b ) etc. The YOLO series outperforms two-stage models in terms of efficiency and accuracy.

2.1.3 Advanced-stage object detection models

The recently emerged advanced-stage object detectors removed the anchors concept in one-stage detectors for detecting objects. The advanced detector, CornerNet (Law and Deng 2018 ) detected objects as paired key points and a new corner pooling layer was introduced to better localize corners. CenterNet (Duan et al. 2019 ) detected the object as a triplet, rather than a pair of key points. Foveabox (Kong et al. 2020a , 2020b ) predicted category-sensitive semantic maps and category-agnostic bounding box for the object. The advanced-stage detectors also found struggling in locating multiple targets having small-size, complex backgrounds and slow detection speed. The one-stage methods (Bochkovskiy et al. 2020 ; Qin et al. 2019 ) utilized predefined anchor boxes and anchor-free (Duan et al. 2019 ) concepts for predicting bounding boxes.

2.1.4 Light-weight object detection models

The low computation in terms of bandwidth and resource utilization are light-weight object detectors and few examples include ThunderNet (Qin et al. 2019 ), PP-YOLO (Long et al. 2020a , 2020b ), YOLObile (Cai et al. 2021 ), Trident-YOLO (Wang et al. 2022a , 2022b , 2022c , 2022d ), YOLOV4-tiny (Jiang et al. 2020 ), Trident FPN (Picron and Tuytelaars 2021 ) etc.

The deep learning-based object detection algorithms have been categorized into two, one, advanced-stage and light weight detectors are highlighted in Fig.  2 . The algorithms such as Faster RCNN (Ren et al. 2015 ), Mask RCNN (He et al. 2017 ), Cascade RCNN (Cai and Vasconcelos 2018 ), FPN (Lin et al. 2017a , 2017b ) and R-FCN (Dai et al. 2016 ) etc., fall under two-stage detectors whereas YOLO (Redmon and Farhadi 2018 ), SSD (Liu et al. 2016 ), RefineDet (Zhang et al. 2018a , 2018b ) and RetinaNet (Lin et al. 2017a , 2017b ) under one-stage detectors. The advanced object detectors such as CornerNet (Law and Deng 2018 ), Objects as points (Zhou et al. 2019a ) and Foveabox (Kong et al. 2020a , 2020b ) are listed in Fig.  2 . However, the algorithms listed above often include a large number of channels and convolutional layers, which demand a lot of computing power for deployment in edge devices. The deep learning-based lightweight object detectors presented in Fig.  2 are specifically designed for contexts with limited resources. Due to their efficiency and compactness, the one and advanced stage detectors’ pipeline is the industry standard for designing lightweight object detectors.

Taxonomy of recent deep learning-based object detection algorithms

3 Deep learning-based lightweight object detection models for edge devices

Numerous computer vision tasks, such as autonomous driving, robot vision, intelligent transportation, industrial quality inspection, object tracking, etc., have used deep learning-based object detection to a large extent. Deep models typically improve performance, but the deployment of real-world applications onto edge devices is constrained by their resource-intensive network. Lightweight mobile object detectors have drawn growing research interest as a solution to this issue, with the goal of creating extremely effective object detection. Deep learning-based lightweight object detectors have recently been developed for situations with limited computer resources, such as mobile devices.

The necessity to execute backbone designs on edge devices with constrained memory and processing power stimulates research and development of deep learning-based lightweight object identification models. A number of efficient lightweight backbone architectures have been proposed in recent years, for example, MobileNet (Howard et al. 2017 ), ShuffleNet (Zhang et al. 2018a , 2018b ), and DetNaS (Chen et al. 2019 ). However, all these architectures are heavily dependent on widely deployed depth-wise separable convolution-based methodologies (Ding et al. 2019 ). With regard to deep learning-based lightweight object identification models, we will describe methodology and each component in depth in the following sections. Our deep learning-based simple object detection models were heavily influenced by existing simple and complex object detection models. We give an architectural breakdown of deep learning-based lightweight object detection models in the following section.

3.1 Architecture methodology of lightweight object detection models

The different building blocks of deep learning-based lightweight object detection algorithms on edge devices consist of number of components consisting of input, backbone, neck and detector head. The definition and details of each component is tabulated in Table  2 . An input for the lightweight object detector is either an image, patch or pyramid, initially fed in the lightweight backbone architecture such as CSPDarkNet (Redmon and Farhadi 2018 ), ShuffleNet (Zhang et al. 2018a , 2018b ), MobileNet (Qian et al. 2021 ), PeleeNet (Wang et al. 2018 ) for the calculation of feature maps. The backbone is the part of deep learning-based lightweight object detection architecture which converts an image to feature maps, whereas the neck transforms the feature maps by connecting the backbone to detector head. The input image is passed to lightweight backbone architecture to calculate initial features vectors of objects. This backbone network may be a pre-trained network or a neural network built from scratch with the aim of feature extraction. The backbone architecture performs feature extraction and produces feature maps as an output. Then, neck component transforms this feature map to a required feature vector for handling various object detection challenges as per application. The lightweight detector head can be visualized as a deep neural network focusing on extraction of RoIs. Further, some pooling layer fixes the size of calculated RoIs to calculate final features of the detected objects. The final features are then passed onto classification and regression loss functions to assign class labels and regressing the coordinates values of bounding boxes. This whole process is repeated until the final regressed values of bounding boxes are obtained with the required class labels. The detailed methodology as presented in Fig.  3 , deep learning-based lightweight object detection consist of three parts i.e., backbone architecture, neck components, and lightweight head prediction. The input images are fed to the backbone and their architecture converts the input image into feature maps. In case of deep learning-based lightweight models, the backbone architecture should be deployed from given categories in Table  2 . The Conv2D + batch normalization + ReLU activation function is represented by a fundamental convolutional module that makes up the backbone architecture. By eliminating redundant gradient information from CNN’s optimization process and integrating gradient modifications into the feature map, it lowers input parameters and model size (Wang et al. 2020a , 2020b , 2020c ). In the bottleneck cross stage partial darknet model, for instance, a 640 × 640-pixel image is divided into four 320 × 320-pixel images, which are then combined to form a 320 × 320-pixel feature map. This 320 × 320x32 resulting feature map was produced using 32 convolutional kernels. Additionally, include the SPP module to add features of various sizes and increase the network’s receptive area. By enhancing the information flow between the backbone architecture and the detecting head, the neck alters the feature maps. The neck, PANet is built on a FPN topology utilized to provide strong semantic characteristics from top to bottom (Wang et al. 2019 ). FPN layers from bottom to top also express important positional features.

Methodology of deep learning-based lightweight object detection model

Furthermore, PANet encourages the transmission of low-level characteristics and the use of precise localization signals in the bottom layers. This improves the target object’s position accuracy. The prediction layer, sometimes referred to as the detection layer, creates many feature maps in order to accomplish multiscale prediction. However, at the prediction layer, the model is capable of classifying and detecting objects of various sizes. As a result, it is projected that each feature map will have various regression bounding boxes at each position, yielding various regression bounding boxes. The anticipated output of the model with bounding boxes is then shown as a detection result. The three steps mentioned above combine the training model for detection into the lightweight object detection model. After model training, the test data is passed to get fine-tuned lightweight model with modified features as shown in Fig.  3 . The parameters in context of deep learning-based light-weight models are discussed below:

To train an edge-cloud-based deep learning model, edge devices and cloud servers must share model parameters and other data. More data must be transferred between edge devices and cloud servers as the training model gets bigger. A number of methods have been put forth to lower the cost of communication during training, including Edge Stochastic Gradient Descent (eSGD), which can reduce a CNN model’s gradient size by up to 90% by communicating only the most important gradients, and intermediate edge aggregation prior to federated learning server aggregation. The two main components of training deep learning-based lightweight detection models are the ability to exit before the input data completes a full forward pass through each layer of a neural network distributed over heterogeneous nodes and the use of binarized neural networks to reduce memory and compute load on resource-constrained end devices (Koubaa et al. 2021 ; Dey and Mukherjee 2018 ).

Researchers have created a novel architecture known as Agile Condor that carries out real-time computer vision tasks using machine learning methods. At the network edge, close to the data sources, Agile Condor can be utilised for autonomous target detection (Isereau et al. 2017 ). Precog is a new method that lowers latency for mobile applications by prefetching and caching that anticipates the subsequent classification request and uses end-device caching to store essential portions of a trained classifier. As a result, fewer offloads to the cloud occur and edge servers calculate the likelihood that linked end devices may make a request in the future. These pre-fetched modules function as smaller models that minimise network traffic and cloud processing while accelerating inference on the end devices (Drolia et al. 2017 ). Another example include ECHO is a feature-rich, thoroughly tested framework for implementing data analytics in a distributed hybrid Edge-Fog-Cloud configuration. ECHO offers services such virtualized application status monitoring, resource discovery, deployment, and interfaces to data analytics runtime engines (Ogden and Guo 2019 ).

When feasible, distributed deep network designs enable the deployment on edge-cloud infrastructure to support local inference on edge devices. A distributed neural network model’s ability to function effectively on minimising inter-device communication costs. Inference on the end-edge-cloud architecture is a dynamic problem because of evolving network conditions (Subedi et al. 2021 ). Static methods like remote inference only or on-device inference only are also not the best. Ogden and Guo have created a distributed architecture that provides a flexible answer to this problem for mobile deep inference. A centralised model manager will house many deep learning models, and the inference environment (memory, bandwidth, and power) will be used to dynamically determine which model should run on which device. If resources are scarce in the inference environment, one of the compressed models may be employed; if not, an uncompressed model with higher accuracy is used. Edge servers handle remote inference when networks are sluggish.

Privacy and security

Edge devices can be used to filter personally identifiable information prior to data transfer in order to enhance user privacy and security when processing data remotely (Xu et al. 2020 ; Hu et al. 2023a , 2023b ). Since data generated by end devices is not available to a central location, training deep learning models across several edge devices in a distributed way leads to more privacy. Personally identifiable information in photographs and videos can be removed at the edge before being uploaded to an external server, enhancing user privacy. The privacy of critical training data becomes an issue when training is conducted remotely. To ensure local and global privacy techniques, it is imperative to keep an eye out for any decline in accuracy, ensure low computing overheads, and provide resilience to communication errors and delays (Abou et al. 2023 ; Makkar et al. 2021 ).

3.2 Comprehensive analysis of lightweight object detection models

The development of extremely effective object detection outcomes has garnered increasing scientific attention in the small, transportable object detectors. With the use of efficient components and compression techniques like pruning, quantization, hashing, and other techniques, the effectiveness of deep learning lightweight object identification models has grown. Distillation, which employs a large network that has been used to train smaller models, has produced some surprising results as well. A comprehensive list containing multiple details of deep learning-based lightweight object detection models in the recent years is presented in Tables 3 , 4 . The categorization of anchor-based and anchor-free detectors for lightweight object detectors have been identified. Anchor-based methods are the mechanism of extracting RoIs employed in object detection models, such as Fast R-CNN (Girshick 2015 ). The anchor boxes are of various scales, which can be viewed as RoIs, as a priori for performing bounding box regression for coordinates values. The detectors including YOLOv2 (Redmon and Farhadi 2017 ), YOLOv3 (Redmon and Farhadi 2018 ), YOLOv4 (Bochkovskiy et al. 2020 ), RetinaNet (Lin et al. 2017a , 2017b ), RefineDet (Zhang et al. 2018a , 2018b ), EfficientDet (Tan et al. 2020 ), Faster R-CNN (Ren et al. 2015 ), Cascade R-CNN (Cai and Vasconcelos 2018 ), Trident-Net (Li et al. 2019 ), belonging to one and two-stage detectors have anchor mechanism to elevate the performance of deep learning-based object detection. Besides, anchor-free detectors have recently received more attention in academia and research by witnessing a large number of new anchor-free methods have been proposed. Earlier works such as YOLOv1 (Redmon et al. 2016 ), DenseBox (Huang et al. 2015 ) and UnitBox (Yu et al. 2016 ) can be considered as early anchor-free detectors. In anchor-free methods, anchor and key points are utilized to perform detection. The former approach does object bounding box regression-based on anchor points instead of anchor boxes, including FCOS (Detector 2022 ), FoveaBox (Kong et al. 2020a , 2020b ), whereas latter approach reformulates the object detection as keypoints localization problem, including CornerNet (Law and Deng 2018 ; Law et al. 2019 ), CenterNet (Duan et al. 2019 ), ExtremeNet (Zhou et al. 2019b ) and RepPoint (Yang et al. 2019 ). By eliminating the handcraft anchors’ restrictions, anchor-free techniques have a lot of promise for working with extremely large and small objects. The anchor-based detectors shown in Table  3 can compete with some newly proposed anchor-free lightweight object detectors in terms of performance. Further, input image type, code link and published sources are also mentioned in Table  3 . While Table  4 reports crucial milestones such as AP, description, loss function etc. for individual deep learning-based light-weight detector.

Tiny-DSOD (Li et al. 2018 ) a lightweight object detector inspired by a thoroughly supervised object detection framework, has been proposed for resource-constrained applications. With only 0.95 M parameters and 1.06B FLOPs, it uses depth-wise dense block as a backbone architecture and depth-wise FPN in neck components, which is by far the most advanced result with such a small resource demand. The context enhancement module and the spatial attention module of ThunderNet (Qin et al. 2019 ), a lightweight two-stage detector, are used as the backbone architectural blocks to produce more discriminative feature representation representation. The effective RPN used in a portable detecting head. ThunderNet outperforms earlier lightweight one-stage detectors by operating at 24.1 frames per second with 19.2 AP on COCO on an ARM-based smartphone. One of the most recent, cutting-edge lightweight object detection algorithms, PP-YOLO (Long et al. 2020a , 2020b ) employs MobileNetV3 (Qian et al. 2021 ), a practical backbone architecture for edge devices. The depth-wise separable convolutions used by PPYOLOtiny’s detection head make it better suited for mobile devices. PPYOLOtiny adopts the optimisation techniques used by PPYOLO algorithms but does away with techniques that have a big impact on model size and performance. Block-punched pruning and a mobile acceleration unit with a mobile GPU-CPU collaboration approach are provided by YOLObile (Cai et al. 2021 ). Trident-YOLO (Wang et al. 2022a , 2022b , 2022c , 2022d ) is an upgrade to YOLOV4-tiny (Jiang et al. 2020 ), designed for mobile devices with limited computing power. In neck components, Trident FPN (Picron and Tuytelaars 2021 ) improves the recall and accuracy of basic object recognition methods by reorganising the network topology of neck components. Trident-YOLO proposes fewer cross-stage partial RFBs and smaller cross-stage partial SPPs, as well as enlarging the receptive field of the network with the fewest FLOPs. Conversely, Trident-FPN significantly enhances lightweight object detection performance by increasing the computational complexity through an increase in a limited number of FLOPs and producing a multi-scale model feature map. In order to simplify computation, YOLOV4-tiny (Jiang et al. 2020 ) uses two ResBlock-D modules in place of two CSPBlock modules in the ResNet-D network. In order to extract more feature information about the object, such as global features, channel, and spatial attention, it also creates an auxiliary residual network block with consecutive 3 × 3 convolutions that is utilized to obtain 5 × 5 receptive fields with the goal of reducing detection error. Optimizing the original YOLOv4 (Bochkovskiy et al. 2020 ), Slim YOLOv4 (Ding et al. 2022 ) changes the backbone architecture from CSPDarknet53 to MobileNetv2 (Sandler et al. 2018 ). Separable convolution and depth-wise over-parameterized convolutional layers were chosen to minimize computation and enhance the performance of the detection network. Based on YOLOv2 (Redmon and Farhadi 2017 ; Wang et al. 2022a ), YOLO-LITE (Huang et al. 2018 ; Wang et al. 2021a ) offers a quicker, more effective lightweight variant for mobile devices. On a PC without a GPU, YOLO-LITE works at roughly 21 frames per second and 10 frames per second when used on a website with only 7 layers and 482 million FLOPS. Object recognition using Fully Convolutional One‐Stage (FCOS) (Detector 2022 ) addresses the issue of label overlap within the ground-truth data. Unlike previous anchor-free detectors, there is no complex hyper-parameter adjustment. Large-scale server detectors constitute the majority of anchor-free detectors in general. The two small minority of anchor-free mobile device detectors are NanoDet (Li et al. 2020a , 2020b ) and YOLOX-Nano (Ge et al. 2021 ). The issue is that compact anchor-free detectors typically struggle to strike a good balance between efficiency and accuracy. In order to choose positive and negative samples, the FCOS method NanoDet employs Adaptive Training Sample Selection (ATSS) (Zhang et al. 2020a , 2020b , 2020c ) and uses generalised focal loss as the loss function for classification and bounding box regression. The centerness branch of FCOS and numerous convolutions on this branch are eliminated by the application of this loss function, which lowers the computational cost of the detection head. A lightweight detector dubbed L-DETR (Li et al. 2022a ) is created based on DETR and PP-LCNet to balance efficiency and accuracy. L-DETR has fewer parameters with the new backbone than the DETR. It is utilised to compute the overall data and arrive at the final prediction. Its normalisation and FFN are enhanced, and thus raises the precision of frame detection. In Table  5 , some well-known metrics in calculating performance of lightweight object detection models have been highlighted. The metrics termed FLOPs are frequently used to determine how computationally complex deep learning models are. They provide a quick and simple method of figuring out how many arithmetic operations are needed to complete a particular computation. It can offer extremely helpful insights on computational costs or requirements or energy consumption, which is particularly helpful for edge computing. It is useful when we have to estimate the total number of arithmetic operations needed, which is usually when computing efficiency is being measured. As highlighted, YOLOv7-x has highest FLOPs i.e., 189.9G among the mentioned detectors. One of the more important components of using a deep network architecture in deployment is the network latency/inference time. The majority of real-world applications need inference times that are quick—a few milliseconds to a second. It needs in-depth knowledge to measure a neural network’s inference time accurately. The time it takes for a deep learning algorithm to process fresh input and produce a prediction is known as the inference time in deep learning. The number of layers, the complexity of the network, and the number of neurons in each layer can all impact this time. Inference times typically rise with network complexity and scale. In our analysis, YOLOv3-Tiny has lowest inference time of 4.5 ms. The Frame Per Second (FPS) is a measure of how rapidly a deep learning model can handle frames. It also specifies how quickly your object detection model will process your photos and videos and produce the desired results. YOLOv4-Tiny has highest FPS among presented ones in Table  5 . Weight and bias are the model parameters in deep learning, which are characteristics of the training data that will be discovered throughout the learning process. The total number of parameters, which is a common indicator of a model’s performance, is the sum of all the weights and biases on the neural network. YOLO-X Nano has least learning parameters when compared with others. Moreover, with respect to each light-weight object detector, prediction regarding deployment of individual detector in real-time applications has been done on the basis of their AP values highlighted in Table  4 . MobileNet-SSD, MobileNetV2-SSDLite, Tiny-DSOD, Pelee, YOLO-Lite, MnasNet-A1 + SSDLite, YOLOv3-Tiny, NanoDet and Mini YOLO are not efficient when deployed.

Additionally, in latest years, one-stage YOLO-based lightweight object detectors have been developed which are mentioned in Table  6 . In 2024, DSP-YOLO (Zhang et al. 2024 ) and YOLO-NL (Zhou 2024 ) emerged but not ready to be deployed in real-life applications. On the contrary, EL-YOLO (Hu et al. 2023a , 2023b ), YOLO-S (Betti and Tucci 2023 ) GCL-YOLO (Cao et al. 2023 ), Light YOLO (Yin et al. 2023 ), Edge YOLO (Li and Ye 2023 ), GL-YOLO-Lite (Dai and Liu 2023 ) and LC-YOLO (Cui et al. 2023 ) can be merged in real-life applications of computing world. Further, we have added performance parameters in terms of FLOPs, Inference time, FPS and number of parameters with respect to each latest YOLO-based light-weight object detector. YOLO-S utilized least number of FLOPs i.e., 34.59B whereas Light YOLO has maximum FPS of 102.04 and GCL-YOLO has lease number of parameters as depicted in Table  6 .

3.3 Backbone architecture for deep learning-based lightweight object detection models

Deep learning-based models for image processing advanced and effectively outperformed more conventional methods in terms of object classification (Krizhevsky et al. 2012 ). The most effective deep learning object categorization architectures have been Convolutional Neural Networks (CNNs), which function similarly to human brains and include neurons that react to their surroundings in real time (Makantasis et al. 2015 ; Fernández-Delgado et al. 2014 ). Well-known CNN architectures based on deep learning have been used for object classification-based feature extractors to fine-tune the classifiers. Forward propagation is used to process the training with random seeds for the filters and parameters. However, due to severely resource-constrained conditions, notably in memory bandwidth, the development of specialised CNN architectures for lightweight object identification models has received less attention than expected. In this section, we summarized backbones i.e., feature extractors for deep learning-based lightweight object detection models. Backbone architectures are used to extract the features for conducting lightweight object identification tasks where an image is provided as an input and a feature map is produced as an output. The majority of backbone architectures for detection tasks are essentially networks for classification problems, which take into account the final fully linked layers. DetNaS convolutional neural network is shown in Fig.  4 to help understand how backbone architectures function in the context of lightweight object identification models. These architectures are shown block-by-block. ShuffleNetv2 5*5 and 7*7 blocks are what the blue and green blocks are made of. Kernel sizes for the blue blocks are 3. In comparison to pink 3*3 ShuffleNetv2 blocks, the peach colour blocks are Xception ShuffleNetv2 blocks (Ma et al. 2018 ). Each level has eight blocks and the total number of blocks is forty. Large-kernel blocks are found in low-level layers while deep blocks are found in high-level layers in the lightweight DetNAS architecture. Blocks of huge kernels (5*5, 7*7) are present in DetNAS’ stage 1 and stage 2’s low-level layers. Pink-colored blocks, on the other hand, have kernels that are 3*3. Stages 3 and 4 are comprised of peach and pink blocks, as shown in the centre of Fig.  4 . Six of these eight blocks—Xception and ShufflNetv2 blocks—are deeper than standard 3*3 ShufflNetv2 blocks. These results lead us to the conclusion that lightweight object detection networks differ visually from conventional detection and classification networks. In the next section, a brief introduction about deep learning-based lightweight backbone architectures have been given:

Architectural details of backbone architecture DetNaS (Chen et al. 2019 )

3.3.1 MobileNet (Howard et al. 2017 )

MobileNet created an efficient network architecture made up of 28 depth-wise separable convolutions to factorise a standard convolution into a depth-wise convolution and a 1 × 1 point-wise convolution. By applying different kernels, isolating the filtering, and merging the features using pointwise convolution in depth-wise convolution, the computing cost and model size were reduced. Two more model-shrinking hyperparameters, width and resolution multiplier, were added in order to improve performance and reduce the size of the model. The model’s oversimplification and linearity, which resulted in fewer channels for gradient flow, were corrected in later versions.

3.3.2 MobileNetV2 (Sandler et al. 2018 )

The inverted residual with linear bottleneck, a novel module, was added to MobileNetv2 in order to speed up calculations and improve accuracy. In the MobileNetv2 there were two convolutional layers followed by 19 bottleneck modules. The computationally efficient MobileNetv2 feature extractor was used by the SSD writers to detect objects. With respect to the original SSD, the new device, known as SSDLite, touted an 8 × reduction in parameters. It is simple to construct, generalises well to different datasets, and as a result, garnered positive feedback from the community.

3.3.3 MobileNetv3 (Qian et al. 2021 )

In MobileNetv3, the unneeded portions of the network were iteratively removed during an automated platform search in a factorised hierarchical search space. This model is then modified to increase the desired metrics after the design proposal has been prepared. Since the architecture’s filters regularly reflect one another, accuracy can be maintained even if half of them are discarded, which reduces the need for further processing. MobileNetv3 merged harsh Swish and RELU activation filters because the latter was computationally more efficient while preserving accuracy.

3.3.4 ShuffleNet (Zhang et al. 2018a , 2018b )

According to the authors, many effective networks lose their effectiveness as they scale down because of expensive 1 × 1 convolutions. ShuffleNet is a neural network design that was very computationally effective and created especially for mobile devices. To overcome the issue of restricted information flow, it was suggested to use group convolution along with channel shuffling. The ShuffleNet unit, like the ResNet block, substituted a pointwise group convolution for the 1 × 1 layer and a depth-wise convolution for the 3 × 3 layer.

3.3.5 ShuffleNetv2 (Ma et al. 2018 )

ShuffleNetv2 advocated in favour of using speed or latency as direct measures rather than FLOPs or other indirect metrics to determine how complex a computation is. Four guiding principles served as its foundation: equal channel width to lower memory access costs, group convolution selection based on target platform, multi-path ways to boost accuracy, and element-wise operations. The input was split in half by a channel split layer in this model, and the residual link was concatenated by three convolutional layers before being sent through a channel shuffle layer. ShuffleNetv2 outperformed other cutting-edge models of comparable complexity, outperforming its peers.

3.3.6 PeleeNet (Wang et al. 2018 )

PeleeNet is an inventive and effective architecture based on traditional convolution that was created using a number of computation-saving strategies. PeleeNet’s design comprised four iterations of modified dense and transition layers, followed by the classification layer. The two-way dense layer helps to obtain distinct receptive field scales, which makes it simpler to identify larger things. Using a stem block minimised the loss of information. While our model’s performance was not as good as modern object detectors on mobile and edge devices, it did demonstrate how even seemingly little design decisions can have a substantial impact on total performance.

3.3.7 mNASNet (Tan et al. 2019 )

Using NAS (Neural Architecture Search) automation, mNASNet was created. It conceptualised the search problem as a multi-object optimisation problem, with a dual focus on latency and accuracy. Unlike previous models that stacked identical blocks, this allowed for the design of individual blocks. By dividing the CNN into distinct blocks and then looking for operations and connections in each of those blocks separately, it factorised the search space. mNASNet was roughly twice as rapid as MobileNetv2 and more accurate.

3.3.8 Once for all (OFA) (Cai et al. 2019 )

In recent years, modern models have been constructed using NAS for architecture design; nonetheless, the sampled model training resulted in costly computations. This model just needs to be trained once, after which sub-networks can be constructed from it based on the requirements. Thanks to the OFA network, such sub-networks can be variable in the four key dimensions of a convolutional neural network: depth, width, kernel size, and dimension. They slowed down the training process and caused layering within the OFA network, which eventually resulted in gradual shrinkage.

3.3.9 MobileViT (Mehta and Rastegari 2021 )

Combining the benefits of CNNs and Vision Transformers (ViT), MobileViT is a transformer-based detector that is lightweight, portable, and compatible with edge devices. It was able to successfully identify both short- and long-range dependencies by utilising a unique MobileViT block. Alongside the MobileViT block, MobileNetv2 modules (Sandler et al. 2018 ) were made available in serial form. Unlike previous transformer-based networks, they used a transformer as a convolution, which automatically incorporated spatial bias, therefore location encoding was not necessary. MobileViT performed well on complex problems, supporting its claim to be a general-purpose backbone for various vision applications. Because of the constraints of transformers on mobile devices, it was able to attain better accuracy with a smaller parameter budget.

3.3.10 SqueezeNet (Iandola et al. 2016 )

SqueezeNet attempts to maintain the accuracy of the network by using techniques with fewer parameters. Smaller filters, 3 × 3 filters for the input channels, and later network placement of the down-sampling layers were the design strategies employed. SqueezeNet’s core module, the fire module, consisted of an extended layer and a squeeze layer, each containing a ReLU activation. Eight Fire modules were stacked and jammed in between the convolution layers to form the SqueezeNet architecture. Accuracy was increased over the basic model using SqueezeNet with residual connections, which was also developed and inspired by ResNet (He et al. 2016 ). SqueezeNet showed out as a serious contender for boosting the hardware efficiency of neural network topologies.

The year and initial usage in which backbone architectures have been utilized, the number of parameters, merits, and top-1 accuracy have been elaborated in the given Table  7 . According to research on deep learning-based backbone architectures, SqueezeNet (Iandola et al. 2016 ) and ShuffleNetv2 (Ma et al. 2018 ) are the most widely used lightweight backbone architectures used in edge devices today. The performance of the model built from depth-wise separable convolutions, inverted residual topologies with linear bottlenecks, and automatic complementary search structures is gradually enhanced by the MobileNet series (Howard et al. 2017 ; Qian et al. 2021 ; Sandler et al. 2018 ).

4 Performance analysis of deep learning-based lightweight object detectors

In this section, a comprehensive analysis has been made from above-discussed lightweight object detectors and related backbone architectures. It can be said that lightweight object detectors based on deep learning strike a balance between accuracy and efficiency. Although the above-mentioned lightweight detectors from the previous sections have a quick inference rate, accuracy isn’t always up to par for some jobs. As shown in Fig.  5 , performance evaluation of deep learning-based lightweight object detectors in terms of mAP on MS-COCO dataset, lightweight object detector YOLOv7-x performs best among mentioned detectors. The backbone architectures in deep learning-based lightweight object detectors play a vital role in determining the accuracy of models. The convolutional architectures specifically designed for edge devices in terms of limited bandwidth usage would be an ideal choice for embedding in detection models. The top-1 accuracy comparison of deep learning-based lightweight backbone architectures in detection models is presented in Fig.  6 . The backbone architecture ShuffleNetV2 attains 70.9 accuracy, a large jump from SqueezeNet (Iandola et al. 2016 ) results. A marginal accuracy increase can be seen in the architectures such as PeleeNet (Wang et al. 2018 ), DetNas (Chen et al. 2019 ), mNASNet (Tan et al. 2019 ), GhostNet (Han et al. 2020a , 2020b ) but the recently emerged transformers-based architecture, i.e., MobileViT (Mehta and Rastegari 2021 ) achieves best state-of-the-art results. Moreover, from year 2017 to 2023, we have shown literature summary in terms of number of publications for deep learning-based lightweight backbone architectures in Fig.  7 . The most popular architecture SqueezeNet has been utilized over years in lightweight detectors as shown in Fig.  7 . GhostNet (Paoletti et al. 2021 ) and MobileViT (Mehta and Rastegari 2021 ) backbone architectures have more literature in 2022 and 2023 year. As mentioned above, the state-of-the-art object detection works are either accuracy-oriented using a large model size (Ren et al. 2015 ; Liu et al. 2016 ; Bochkovskiy et al. 2020 ) or speed- oriented using a lightweight model but sacrificing accuracy (Wang et al. 2018 ; Sandler et al. 2018 ; Li et al. 2018 ; Liu and Huang 2018 ). It is difficult for any of existing lightweight detectors to meet the accuracy and latency requirements of real-world applications on mobile and edge devices at the same time. Therefore, we require a mobile device solution that can accomplish both high accuracy and low latency to deploy lightweight object detection models.

mAP Performance evaluation of major deep learning-based lightweight object detectors

Accuracy comparison of deep learning-based lightweight backbone architectures in detection models

Year-wise literature summary of backbone architectures in case of lightweight detection models

4.1 Benchmark detection databases for light-weight object detection models

In this section, most popular datasets have been discussed concerning to deep learning-based lightweight object detectors. Datasets are essential for lightweight object detection because they allow for standard comparisons of competing algorithms and the establishment of objectives for solutions.

4.1.1 PASCAL VOC (Everingham et al. 2010 )

The most well-known object detection dataset is this one. The PASCAL-VOC versions VOC2007 and VOC2012 are frequently used in papers. 2501 training, 2510 validation, and 5011 testing images make up VOC2007. VOC2012, on the other hand, comprises of 10,991 training, 5823 validation images, and 5717 testing images. The PASCAL VOC datasets include 11,000 images spread across 20 visual object classes. Animals, vehicles, people, and domestic things are the four broad categories into which these 20 classes can be divided. Additionally, classifications of objects with semantic similarities, such as trucks and buses, enhance the complexity levels for detection. Visit http://host.robots.ox.ac.uk/pascal/VOC/to get the dataset.

4.1.2 MS-COCO (Lin et al. 2014 )

A sizable image dataset called MS-COCO (Microsoft Common Objects in Context) contains 328,000 photographs of commonplace items and people. It is now one of the most well-liked and difficult object detection datasets. It has 897,000 tagged objects in 164,000 photos across 80 categories. For the training, validation, and testing sets, there are 118,287, 5000, and 40,670 photos, respectively. The distribution of objects in MS-COCO is more in line with real-world circumstances. There is no information available regarding the MS-COCO testing set’s annotations. The following categories of annotations are offered by MS-COCO, including those for captioning, keypoints, panoptic segmentation, dense pose, and object detection. The MS-COCO dataset provides a wide range of realistic images, showing disorganized scenes with various backgrounds, overlapping objects, etc. The URL of the dataset is http://cocodataset.org .

4.1.3 KITTI (Geiger et al. 2013 )

It is a well-known dataset for traffic scene analysis and includes 7518 photos for testing and 7481 for training that have been labelled. There are 100,000 pedestrian cases, 6000 IDs, and an average of one person per photograph. The pedestrian and cyclist are the two subclasses of the human class in KITTI. Based on how much the objects are obscured and shortened, the object labels are divided into easy, moderate, and hard levels. In this dataset, there are two subcategories for people: pedestrians and cyclists. Utilizing three criteria that differ in the minimum bounding box height and maximum occlusion level, the models trained on it are assessed. Visit http://www.cvlibs.net/datasets/kitti/index.php to download the dataset.

We have presented performance of deep learning-based lightweight detection models on above-discussed detection datasets in Fig.  8 . The lightweight object detector YOLOv4-dense achieves mAP value of 84.30 on KITTI dataset, 71.60 on PASCAL VOC dataset. L4Net detector attains mAP value of 71.68 on KITTI, 82.30 on PASCAL VOC and 42.90 on MSCOCO dataset. RefineDet-lite detector achieves mAP value of 26.80 on MSCOCO dataset. Further to compare performances, FE_YOLO performs best on KITTI dataset as presented in Fig.  8 whereas L4Net detector performs best on MSCOCO dataset and finally, lightweight YOLO-Compact detector outperforms other detectors on PASCAL VOC dataset.

Performance evaluation of deep learning-based lightweight models on leading datasets

4.2 Evaluation parameters

Lightweight object identification models based on deep learning use the same evaluation criteria as generic object detection models. Out of all predictions made, accuracy is the proportion of things that were successfully anticipated. When dealing with class unbalanced data, where the number of instances is not equal for each class, the accuracy result can be quite deceptive because it places more emphasis on learning the majority classes than the minority classes. Therefore, mean Average Precision (mAP), Frames Per Second, and the size of the model weight file serve as the primary evaluation indices for the effectiveness of the lightweight object identification model. The correct labelling data for each image provides the precise number of objects in each category in the image. Intersection Over Union (IoU) quantifies the similarity between the ground and predicted bounding box to evaluate how good the predicted bounding box is as represented in Eq. ( 1 ):

The calculation of IoU value takes place between each prediction box and ground data. Then consider the largest IoU value, and, based on the IoU threshold, we can calculate the number of True Positives (TP) and False Positives (FP) for each object category in an image. From this, the Precision of each category is calculated according to Eq. ( 2 ):

When the correct number of TP is obtained, the number of False Negatives (FN) are measured through Recall as in Eq. ( 3 ).

By figuring out various recall rates and associated accuracy rates for each category, PR curves for each can be plotted. The value of AP is identical to the region enclosed by the PR curve in the PASCAL VOC 2010 object detection competition evaluation criteria. Precision, recall rate, and average accuracy are three metrics that can be used to assess the model’s accuracy for detecting tasks. MS COCO averages mAP with a step of 0.05 over a range of IoU thresholds (0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, and 0.95). The main metric used to judge competitors is called “mAP,” which averages AP over all 80 COCO dataset categories and all 10 criteria. A higher AP score according to the COCO evaluation criteria denotes flawless bounding box localization of the discovered items. The typical COCO-style AP metric, which averages APs over IoU threshold ranges of 0.5 to 0.95 with 0.05 steps. The performance is measured using AP50, AP75 at various IoU thresholds and AP s , AP m , and AP l on objects that are small, medium, and large in size. By averaging over all 10 IoU thresholds across all categories with a uniform step size of 0:05, the primary metric, AP(IoU) = 0.50:0.05:0.95, is determined.

4.3 A summary of edge devices-based platforms for lightweight object detectors

In the upcoming years, a ton of data will be produced by mobile users and IoT devices. Data growth will bring new problems like latency. Additionally, traditional methods cannot be relied upon for very long if intelligence is to be derived from deep learning-based object detection and recognition algorithms in real-time. Edge computing devices have drawn a lot of interest as a result of prominent firms’ efforts to make supercomputers affordable. It is vital to enable developers to swiftly design and deploy edge applications from lightweight detection models as the IoT, 5G, and portable processing device eras approach. As a result of advancements in the field of deep learning, numerous enhancements to object identification models have been presented that are aimed at edge device applications. DeepSense, TinyML, DeepThings, and, DeepIoT are just a few of the frameworks that have been published in recent years with the intention of compressing deep models for IoT edge devices. To satisfy the processing demands of deep learning-based lightweight object detectors, the model must be able to overcome several constraints like a limited battery, high energy consumption, limited computational capabilities, and a constrained memory while maintaining a level of accuracy. The primary goal should be to create a framework that makes it possible for machine learning models to be quickly implemented in Internet of Things devices. The well-known TinyML frameworks TensorFlow Lite from Google, ELL from Microsoft, ARM-NN and CMSIS-NN from ARM, STM 32Cube-Al from STMicroelectronics, and Alfes from Fraunhofer IMS enable the use of deep learning at the peripheral. When combined with other microcontroller-based tasks, low-latency, low-power, and low-bandwidth AI algorithms can function as part of an intelligent system at a low cost thanks to TinyML on a microcontroller. The DeepIoT framework reduces neural network designs into less dense matrices while preserving the performance of sensing applications by figuring out how few non-redundant hidden components, including filters and dimensions, are needed by each layer. Another well-known framework that provides deep learning-based lightweight object recognition is TensorFlow. TensorFlow Lite is a cross-platform, quick, and lightweight mobile and IoT framework (TFLite) to scale down their massive models. The majority of lightweight models employ TensorFlow lite quantization, which is easy to deploy on edge devices.

4.3.1 Mobile phones

The limitations imposed by mobile devices may be the reason why less research is being done on the deployment of object detectors on mobile phones than on other embedded platforms. Smartphone complexity and capabilities are rising quickly, but their size and weight are also probably going to decrease. Few literature studies have tried to perform implementation on smartphones-based devices (Lan et al. 2019 ; Liu et al. 2020a , 2020b , 2020c , 2020d ; Liu et al. 2021a , 2021b , 2021c ; Li et al. 2021a , 2021b , 2021c ; Paluru et al. 2021 ). It can be seen that it puts a heavy burden on creating models that are small, light, and require a minimum number of computations. It is advised to test novel concepts for deep learning inference optimization on transportable models that are regularly utilized with cellphones (Xu et al. 2019 ). Either the spatial or temporal complexity of deep learning models can be reduced to the point where they can be fully implemented on mobile devices. But there may be a lot of security issues that need to be fixed Steimle et al. ( 2017 ). Although deep learning for smartphone item detection appears to be a promising field of study, success will need many more contributions (Wang et al. 2022a , 2022b , 2022c , 2022d ).

4.3.2 IoT edge devices

Another way to enable deep learning on IoT edge devices is to transfer model inference to a cloud server. Another way to boost the power of these inexpensive devices is to add an accelerator. The price of using these accelerators is a major drawback, though. Some edge devices, like the Raspberry Pi, may require an extra accelerator, but some, like the Coral Dev Board, already have edge TPU accelerators built in. Deep learning can be more easily enabled to run locally or remotely using a distributed design that links computationally inefficient front-end devices with more potent back-end devices, like a cloud server or accelerator (Ran et al. 2017 ).

4.3.3 Embedded boards

To provide the finest design options, processor-FPGA combinations, and FPGAs with hard processor cores embedded into its fabric are widely used. Lattice Semiconductor, Xilinx, Microchip, and Altera from Intel are the well-known manufacturers. The literature suggests that the Xilinx boards family is the one that is most frequently utilized for deep learning-based applications. An additional accelerator is often needed when employing FPGA devices (Saidi et al. 2021 ) to get acceptable performance. Due to Integrated Development Environment (IDE) and high-level language support, the Arduino and Spark-based boards at the top of the device family allow for greater software level programming (Kondaveeti et al. 2021 ).

4.4 Applications specific to deep learning-based lightweight object detectors

In the above sections, we have discussed architectural details, leading datasets of deep learning-based lightweight object detection models. These models offer a multitude of applications such as in remote-sensing (Xu and Wu 2021 ; Ma et al. 2023 ), and aerial images (Xu and Wu 2021 ; Zhou et al. 2022 ), traffic monitoring (Jiang et al. 2023 ; Zheng et al. 2023 ), fire detection (Chen et al. 2023 ), indoor robots (Jiang et al. 2022 ), pedestrian detection (Jian et al. 2023 ) etc. A summary of literature findings for supporting the applications of deep learning-based lightweight object detection models is listed in Table  6 . In (Zhou et al. 2019 ), for Range Doppler (RD) radar pictures, a lightweight detection network called YOLO-RD has been proposed. Additionally, a brand-new, lightweight mini-RD dataset has been created for effective network training. On the mini-RD dataset, YOLO-RD produced effective results with a smaller memory budget and a detection accuracy of 97.54%. Regarding both algorithm and hardware resource aspects in object detection, (Ding et al. 2019 ) introduced REQ-YOLO, a resource conscious, systematic weight quantization framework for object detection. For non-convex optimisation problems on FPGAs, it applied the block-circulant matrix approach and proposed a heterogeneous weight quantization. The outcomes demonstrated that the REQ-YOLO framework can greatly reduce the size of the YOLO model while just slightly reducing accuracy. It is suggested that autonomous vehicles use the L4Net Locating object suggestions from (Wu et al. 2021 ) which integrates a key point detection backbone with a co-attention strategy by attaining cheaper computation costs with improved detection accuracy under a variety of resource constraints. To generate more precise prediction boxes, the backbone capture context-wise information and co-attention method specifically combined the strength of both class-agnostic and semantic attention. With a 13.7 M model size and speeds of 149 FPS on an NVIDIA TX and 30.7 FPS on a Qualcomm-based device, respectively, L4Net achieved 71.68% mAP. The development of effective object detectors requires the rapid development of CPU-only hardware because to the huge data processing and resource-constrained scenarios on GPUs. With three orthogonal training strategies—IoU-guided loss, classes-aware weighting method, and balanced multi-task training approach, (Chen et al. 2020a , 2020b ) proposed a lightweight backbone and light-head detecting component. On a single-thread CPU, the suggested RefineDetLite obtained 26.8 mAP at a pace of 130 ms/pic. LiraNet, a compact CNN, was suggested by (Long et al. 2020a , 2020b ) for the recognition of marine ship objects in radar pictures. By creating Lira-YOLO, a compact model that is simple to set up on mobile devices, LiraNet was mounted on the already-existing detection framework Darknet. Additionally, a lightweight dataset of distant Doppler domain radar pictures known as mini-RD had been created to test the performance of the proposed model. Studies reveal that Lira-YOLO’s network complexity is minimal at 2.980 Bflops, and its parameter quantity is reduced at 4.3 MB thanks to its high detection accuracy of 83.21%. (Lu et al. 2020 ) developed a successful YOLO-compact network for real-time object detection in the single person category. The down sampling layer was separated in this network, which facilitated the modular design by enhancing the remaining bottleneck block. YOLO-compact’s AP result is 86.85% and its model size is 9 MB, making it smaller than tiny-yolov3, tiny-yolov2, and YOLOv3. By focusing on small targets and background complexity, (Xu and Wu 2021 ) presented FE-YOLO for deep learning-based target detection from remote sensing photos. The analyses on remote sensing datasets demonstrate that our suggested FE-YOLO outperformed existing cutting-edge target detection methods. A brand-new YOLOv4-dense model was put forth by (Jiang et al. 2023 ) for real-time object recognition on edge devices. To address the issue of losing small objects and further minimize the computing complexity, a dense block had been devised. With 20.3 M parameters, YOLOv4-dense obtained 84.3% mAP and 22.6 FPS. To improve the detection of small and medium-sized objects in aerial photos, (Zhou et al. 2022 ) developed Dense Feature Fusion Path Aggregation Network (DFF-PANet). The trials were conducted using the HRSC2016 dataset and the DOTA dataset, yielding 71.5% mAP and 9.2 M as a lightweight model. To help an indoor mobile robot solve the problem of object detection and recognition, (Jiang et al. 2022 ) presented ShuffleNet-SSD. Deep separable convolution, point-by-point grouping convolution, and channel rearrangement were all created using the suggested model. A dataset has been created for the mobility robot under the indoor scene. For the detection of dead trees, (Wang et al. 2022a , 2022b , 2022c , 2022d ) suggested a novel, lightweight architecture called LDS-YOLO based on the YOLO framework. These plants assisted in the timely regeneration of dead trees, allowing the ecosystem to remain stable and efficiently withstand catastrophic disasters. With the addition of the SoftPool approach in Spatial Pyramid Pooling (SPP), a unique feature extraction module is provided that makes use of the features from earlier layers in order to ensure that small targets are not ignored. On the basis of UAV-captured photos, the suggested approach is assessed, and the experimental findings show that the LDS-YOLO architecture works well when compared to AP of 89.11% and parameter size of 7.6 MB. The categorization of several applications concerning to lightweight object detectors as shown in Table  8 with respect to image type such as remote-sensing, aerial, medical and video streams and application type of healthcare, medical, military and industrial use.

4.5 Discussion and contributions

According to the above-mentioned analysis of deep learning-based light-weight object detectors, there is a need of focus to develop detectors for edge devices which can strike a good balance between speed and accuracy. Furthermore, real-time deployment of these detectors on edge devices is also needed while achieving accuracy of lightweight detectors without compromising precision. In 2022, lightweight backbone architectures ShuffleNet and SqueezeNet have highest publications with respect to lightweight object detectors. In 2023, transformers based MobileViT started getting attention of researchers as top-1 accuracy of 78.4 is also achieved and MobileNet backbone architectures were maximum employed when compared with others. As shown in Table  8 , according to input type, video streams have maximum employability in deep learning-based light-weight object detectors. With respect to diverse applications, traffic and pedestrians related detection problems, obstacles and driving assistance have highest studies whereas all other existing applications have limited light weight detectors on edge devices. As we witnessed, the majority of presented light-weight models are from the YOLO family, a number of deep network layers with increasing number of parameters needing to account for the improved accuracy. Therefore, the most important question to ask when a model migrates from a cloud device to an edge device is how to lower the parameters of a deep learning-based lightweight model. There are numerous approaches being used to address this which are described in the next section.

4.6 Recommendations for designing powerful deep learning-based lightweight models

Researchers have created new training methods that decrease the memory footprint in the edge device and speed up training on low-resource devices, in addition to specialized hardware for the deep learning model training process at the network edge. The techniques which we discussed in this section- pruning, quantification, knowledge distillation, and low-rank decomposition, are the four key categories used to compress pre-training networks (Kamath and Renuka 2023 ) and listed in the following (Koubaa et al. 2021 ; Makkar et al. 2021 ; Wang et al. 2020a , 2020b , 2020c ):

4.6.1 Pruning

Network pruning is a useful technique for reducing the size of the object detection model and speeding up model reasoning. By cutting out connections between neurons that are irrelevant to the application, this method lowers the amount of computations needed to analyse fresh input. In addition to eliminating connections, it can also eliminate neurons that are deemed irrelevant when the majority of their weights are low in relation to the deep neural network’s overall context. With the use of this method, a deep neural network with reduced size, greater speed, and improved memory efficiency can be used in low-resource devices, such as edge devices.

4.6.2 Weights quantization

The weight quantization approach, which trades precision for speed, shrinks the model’s storage capacity by reducing the number of floating-point parameters. In addition to eliminating pointless associations, every weight is kept as separate values. The weights quantization technique aims to compress these values to integers or numbers that occupy as few bits as possible by clustering related weight values into a single value. Consequently, there will be a re-adjustment of the weights, indicating a modification of the precision as well. This results in a cyclical implementation where the weights are quantified following each training.

4.6.3 Knowledge distillation

Dissection of knowledge presents itself as a new mode of transfer learning. This technique can extract knowledge from a big and well-trained deep neural network, dubbed teacher in this case, into a reduced deep network, called student. By doing this, the student network can learn to achieve the same outcomes as the teacher network while decreasing in size and increasing processing speed. Through the process of knowledge distillation, information is transferred from a large, thoroughly trained end-to-end detection network to numerous, quicker sub-models.

4.6.4 Training tiny networks

The deep neural network’s initial convolution kernel is mostly broken-down using matrix decomposition in the low-rank decomposition method, although the accuracy of the results will noticeably improve. Directly training tiny networks can drastically reduce network accuracy loss and speed up reasoning.

4.6.5 Federated learning and model partition

Distributed learning or federated learning are two possible training approaches for dealing with complicated tasks or a period of training including a lot of data. The data would be broken into smaller groups that would be distributed among the nodes of the edge network. As part of the final deep neural network, each node would train based on the data it received, enabling active learning capabilities at the network edge. Model partitioning is a strategy that may be applied in the inferring phase using the same methodology. To divide the burden, a separate node would compute each layer of the deep neural network in a model split. This approach would also make scaling simple.

Moreover, to boost the flow of information in a constrained amount of time, multi-scale feature learning in lightweight detection models that comprise single feature maps, pyramidical feature hierarchies, and integrated features may be used. The feature pyramid networks, as well as their variations such as feature fusion, feature pyramid generation, and multi-scaled fusion module, aid in overcoming object detection difficulties. Additionally, in order to boost the effectiveness of lightweight object identification models, researchers also work to encourage the development of activation functions and normalization in various applications. Above-mentioned techniques accelerate the usage of deep learning models into edge devices. The deep learning-based lightweight object detection models have not yet achieved comparable results when compared with generic object detection. Moreover, to mitigate these differences, a need for designing powerful and innovative lightweight detectors is a must. Some recommendations for designing powerful lightweight deep learning-based detectors are mentioned in this section.

Incorporation of FPNs - The bidirectional FPN can be utilized to improve the semantic information while incorporating feature fusion operations (Wang et al. 2023a , 2023b ). To successfully collect bottom-up and top-down features more than FPN, an effective feature-preserving and refining module can be introduced (Tang et al. 2020a , 2020b ). Deep learning-based lightweight detectors can be designed with the help of cross-layer connections and the extraction of features at various sizes while using depth-wise separable convolution. It is possible to take advantage of a multi-scale FPN architecture with a lightweight backbone to take out features from the input image.

Transformer-based Solutions - To increase the precision of the transformer-based lightweight detectors, group normalisation can be implemented in the encoder-and-decoder module and h-sigmoid activation function in the multi-layer perceptron (Li, Wang and Zhang 2022).

Receptive Fields Enlargement - The capacity of single-scale features to express themselves and to be detected on a single scale are both improved by the multi-branch block involving various receptive fields. The network width may increase and performance may be slightly enhanced with the use of several network branches (Liu et al. 2022).

Feature Fusion Operation - In order to combine several feature maps of the backbone and the collection of multi-scale features into a feature pyramid, the fusion operation offers a concatenation model (Mao et al. 2019 ). To improve the extraction of information from the suggested lightweight model, the feature maps’ weight of various channels can be reassigned. Furthermore, performance improvement may result from the integration of the attention module and data augmentation technique (Li et al. 2022a , 2022b ). The smooth fusion of semantic data from low-resolution scale to neighbourhood high-resolution scale is made possible by the implementation of FPN into the suggested lightweight detector architecture (Li et al. 2018 ).

Effect of Depth-wise Separable Convolution - The optimal design principle for lightweight object detection models consists of fewer channels with more convolutional layers (Kim et al. 2016 ). The approach to network scaling that modifies width, resolution, and network’s structure to reduce or balance the size of the feature map, keep the number of channels constant after convolution, and minimise convolutional input and output is where researchers can concentrate (Wang et al. 2021b ). The typical convolution in the network structure can be replaced with an over-parameterized depth-wise convolutional layer, which significantly reduces computation and boosts network performance. To increase the numerical resolution, ReLU6 can be used in place of the activation function known as Leaky ReLU (Ding et al. 2022 ).

Increase in Semantic Information - To keep semantic features and high-level feature maps in the deep lightweight object network, the proposal of smaller cross-stage partial SPPs and RFBs facilitates the integration of high-level semantic information with low-level feature maps (Wang et al. 2022a , 2022b , 2022c , 2022d ). The architectural additions of the context enhancement and spatial attention module can be employed to generate more discriminative feature representation (Qin et al. 2019 ).

Pruning Strategy - Block-punched pruning uses a fine-grained structured pruning method to maximise structural flexibility and minimise accuracy loss. High hardware parallelism can be achieved using the block-punched pruning strategy if the block size is suitable and compiler-level code generation is used (Cai et al. 2021 ).

Assignment Strategy- To improve the training of lightweight object detectors based on deep learning, use the SIMOTA dynamic label assignment method. When creating lightweight detection models, the combination of the regression method based on FCOS, dynamic and learnable sample assignment, and varifocal loss handling class imbalance works better (Yu et al. 2021 ). Designing lightweight object detectors using the anchor-free approach has been successful when combined with other cutting-edge detection methods using decoupled heads and the top label assignment strategy, known as SimOTA (Ge et al. 2021 ).

There are two ways of deployment of deep learning-based lightweight models on edge devices are when a light-weight model or compressed data are employed to match the compute capabilities of the limited edge devices. With regard to on-board object detection, this is true. The compromise between compression ratio and detection accuracy in this method is its drawback. Secondly, the model is distributed and data is exchanged when computations are spread over several devices and cloud server could be able to handle the computations in this situation. In this case, privacy and security seem to be the primary issues (Zhang et al. 2020a , 2020b , 2020c ). Consideration must be given when establishing device coordination in this scenario as it may also result in extra overhead in order to avoid the edge devices being overworked while conducting the collaborative learning algorithm. No matter the plan, all of these deployment methods rely on edge devices and have to deal with the problems with edge devices present. The primary causes of the issue are data disparity in real-world scenarios and the need to manage real-time sensor data while performing numerous deep learning tasks. The powerful processing units, the high computing requirements of deep learning models, and short battery life makes validity of light-weight models tough. In the future, we’ll strive to create such standards-compliant light-weight detection deployment models.

5 Conclusion

This study asserted that deep learning-based lightweight object detection models are a good candidate for improving the hardware efficiency of neural network architectures. This survey has examined and provided the most recent lightweight edge gadget models. The commonly utilized backbone architectures in deep learning-based lightweight object detection methods have also been stated in which ShuffleNet and MobileNetV2 employed majorly in these models. Some critical aspects after analyzing current state-of-the-art deep learning-based lightweight object detection models on edge devices have been discussed. The comparison has been drawn between emerging lightweight object detection models on the basis of COCO-based mAP scores. The presentation of a summary of heterogeneous applications for lightweight object identification models that take into account diverse types of photos and application categories. This study also gives information on edge platforms for using portable detector models. A few recommendations are also given for creating a potent deep learning-based lightweight model, including multi-scale and multi-branch FPNs, federated learning, partitioning strategy, pruning, knowledge distillation, and label assignment algorithms. The lightweight detectors still fall more than 50% short in delivering such outcomes, although having demonstrated significant potential by matching classification errors with the thorough models.

Abou El Houda Z, Brik B, Ksentini A, Khoukhi L (2023) A MEC-based architecture to secure IOT applications using federated deep learning. IEEE Internet Things Mag 6(1):60–63

Article   Google Scholar  

Agarwal S, Terrail JOD, Jurie F (2018) Recent advances in object detection in the age of deep convolutional neural networks. arXiv preprint arXiv:1809.03193

Alfasly S, Liu B, Hu Y, Wang Y, Li CT (2019) Auto-zooming CNN-based framework for real-time pedestrian detection in outdoor surveillance videos. IEEE Access 7:105816–105826

Bai X, Zhou J (2020) Efficient semantic segmentation using multi-path decoder. Appl Sci 10(18):6386

Betti A, Tucci M (2023) YOLO-S: a lightweight and accurate YOLO-like network for small target detection in aerial imagery. Sensors 23(4):1865

Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934

Brunetti A, Buongiorno D, Trotta GF, Bevilacqua V (2018) Computer vision and deep learning techniques for pedestrian detection and tracking: a survey. Neurocomputing 300:17–33

Cai Z, Vasconcelos N (2018) Cascade r-cnn: delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6154–6162)

Cai H, Gan C, Wang T, Zhang Z, Han S (2019) Once-for-all: Train one network and specialize it for efficient deployment. arXiv preprint arXiv:1908.09791

Cai Y, Li H, Yuan G, Niu W, Li Y, Tang X, Ren B, Wang Y (2021) Yolobile: real-time object detection on mobile devices via compression-compilation co-design. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 2, pp. 955–963)

Cao J, Bao W, Shang H, Yuan M, Cheng Q (2023) GCL-YOLO: a GhostConv-based lightweight yolo network for UAV small object detection. Remote Sens 15(20):4932

Chabas JM, Chandra G, Sanchi G, Mitra M (2018) New demand, new markets: What edge computing means for hardware companies. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/new-demand-new-markets-what-edge-computing-means-for-hardware-companies

Chang L, Zhang S, Du H, You Z, Wang S (2021) Position-aware lightweight object detectors with depthwise separable convolutions. J Real-Time Image Proc 18:857–871

Chen Y, Yang T, Zhang X, Meng G, Xiao X, Sun J (2019) Detnas: backbone search for object detection. Adv Neural Inf Process Syst. https://doi.org/10.48550/arXiv.1903.10979

Chen L, Ding Q, Zou Q, Chen Z, Li L (2020b) DenseLightNet: a light-weight vehicle detection network for autonomous driving. IEEE Trans Industr Electron 67(12):10600–10609

Chen C, Yu J, Lin Y, Lai F, Zheng G, Lin Y (2023) Fire detection based on improved PP-YOLO. SIViP 17(4):1061–1067

Chen C, Liu M, Meng X, Xiao W, Ju Q (2020) Refinedetlite: a lightweight one-stage object detection framework for cpu-only devices. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 700–701)

Cheng Y, Li G, Wong N, Chen HB, Yu H (2020) DEEPEYE: a deeply tensor-compressed neural network for video comprehension on terminal devices. ACM Trans Embed Comput Syst (TECS) 19(3):1–25

Cho C, Choi W, Kim T (2020) Leveraging uncertainties in Softmax decision-making models for low-power IoT devices. Sensors 20(16):4603

Cui B, Dong XM, Zhan Q, Peng J, Sun W (2021) LiteDepthwiseNet: a lightweight network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1–15

Google Scholar  

Cui M, Gong G, Chen G, Wang H, Jin M, Mao W, Lu H (2023) LC-YOLO: a lightweight model with efficient utilization of limited detail features for small object detection. Appl Sci 13(5):3174

Dai Y, Liu W (2023) GL-YOLO-Lite: a novel lightweight fallen person detection model. Entropy 25(4):587

Dai W, Li D, Tang D, Jiang Q, Wang D, Wang H, Peng Y (2021) Deep learning assisted vision inspection of resistance spot welds. J Manuf Process 62:262–274

Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. Advances in neural information processing systems, 29

Detector AFO (2022) Fcos: a simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4)

Dey S, Mukherjee A (2018) Implementing deep learning and inferencing on fog and edge computing systems. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 818–823). IEEE

Ding P, Qian H, Chu S (2022) Slimyolov4: lightweight object detector based on yolov4. J Real-Time Image Proc 19(3):487–498

Ding C, Wang S, Liu N, Xu K, Wang Y, Liang Y (2019) REQ-YOLO: a resource-aware, efficient quantization framework for object detection on FPGAs. In proceedings of the 2019 ACM/SIGDA international symposium on field-programmable gate arrays (pp. 33–42)

Drolia U, Guo K, Narasimhan P (2017) Precog: prefetching for image recognition applications at the edge. In Proceedings of the Second ACM/IEEE Symposium on Edge Computing (pp. 1–13)

Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) Centernet: keypoint triplets for object detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6569–6578)

Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput vis 88:303–338

Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181

MathSciNet   Google Scholar  

Gadosey PK, Li Y, Agyekum EA, Zhang T, Liu Z, Yamak PT, Essaf F (2020) SD-UNET: stripping down U-net for segmentation of biomedical images on platforms with low computational budgets. Diagnostics 10(2):110

Gagliardi A, de Gioia F, Saponara S (2021) A real-time video smoke detection algorithm based on Kalman filter and CNN. J Real-Time Image Proc 18(6):2085–2095

Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430

Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the kitti dataset. Int J Robot Res 32(11):1231–1237

Girshick R (2015) Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440–1448)

Guo W, Li W, Li Z, Gong W, Cui J, Wang X (2020) A slimmer network with polymorphic and group attention modules for more efficient object detection in aerial images. Remote Sens 12(22):3750

Han J, Zhang D, Cheng G, Liu N, Xu D (2018) Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Process Mag 35(1):84–100

Han S, Yoo J, Kwon S (2019) Real-time vehicle-detection method in bird-view unmanned-aerial-vehicle imagery. Sensors 19(18):3958

Han S, Liu X, Han X, Wang G, Wu S (2020b) Visual sorting of express parcels based on multi-task deep learning. Sensors 20(23):6785

Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: More features from cheap operations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1580–1589)

Haque WA, Arefin S, Shihavuddin ASM, Hasan MA (2021) DeepThin: a novel lightweight CNN architecture for traffic sign recognition without GPU requirements. Expert Syst Appl 168:114481

He W, Huang Y, Fu Z, Lin Y (2020) Iconet: a lightweight network with greater environmental adaptivity. Symmetry 12(12):2119

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778)

He K, Gkioxari G, Dollár P, and Girshick R (2017) Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961–2969)

Hou Y, Li Q, Han Q, Peng B, Wang L, Gu X, Wang D (2021) MobileCrack: object classification in asphalt pavements using an adaptive lightweight deep learning. J Trans Eng Part b: Pavements 147(1):04020092

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

Hu X, Yang W, Wen H, Liu Y, Peng Y (2021) A lightweight 1-D convolution augmented transformer with metric learning for hyperspectral image classification. Sensors 21(5):1751

Hu M, Li Z, Yu J, Wan X, Tan H, Lin Z (2023b) Efficient-lightweight yolo: improving small object detection in yolo for aerial images. Sensors 23(14):6423

Hu B, Wang Y, Cheng J, Zhao T, Xie Y, Guo X, Chen Y (2023) Secure and efficient mobile DNN using trusted execution environments. In Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security (pp. 274–285)

Hua H, Li Y, Wang T, Dong N, Li W, Cao J (2023) Edge computing with artificial intelligence: a machine learning perspective. ACM Comput Surv 55(9):1–35

Huang Z, Yang S, Zhou M, Gong Z, Abusorrah A, Lin C, Huang Z (2022) Making accurate object detection at the edge: review and new approach. Artif Intell Rev 55(3):2245–2274

Huang L, Yang Y, Deng Y, Yu Y (2015) Densebox: unifying landmark localization with end to end object detection. arXiv preprint arXiv:1509.04874

Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In 2018 IEEE international conference on big data (big data) (pp. 2503–2510). IEEE

Huang X, Wang X, Lv W, Bai X, Long X, Deng K, Dang Q, Han S, Liu Q, Hu X, Yu D (2021) PP-YOLOv2: a practical object detector. arXiv preprint arXiv:2104.10419

Huyan L, Bai Y, Li Y, Jiang D, Zhang Y, Zhou Q, Wei J, Liu J, Zhang Y, Cui T (2021) A lightweight object detection framework for remote sensing images. Remote Sens 13(4):683

Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360

Isereau D, Capraro C, Cote E, Barnell M, Raymond C (2017) Utilizing high-performance embedded computing, agile condor, for intelligent processing: An artificial intelligence platform for remotely piloted aircraft. In 2017 Intelligent Systems Conference (IntelliSys) (pp. 1155–1159). IEEE

Jain DK, Zhao X, González-Almagro G, Gan C, Kotecha K (2023) Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes. Inf Fus 95:401–414

Jeong M, Park M, Nam J, Ko BC (2020) Light-weight student LSTM for real-time wildfire smoke detection. Sensors 20(19):5508

Jiang S, Li H, Jin Z (2021) A visually interpretable deep learning framework for histopathological image-based skin cancer diagnosis. IEEE J Biomed Health Inform 25(5):1483–1494

Jiang L, Nie W, Zhu J, Gao X, Lei B (2022) Lightweight object detection network model suitable for indoor mobile robots. J Mech Sci Technol 36(2):907–920

Jiang Y, Li W, Zhang J, Li F, Wu Z (2023) YOLOv4-dense: a smaller and faster YOLOv4 for real-time edge-device based object detection in traffic scene. IET Image Proc 17(2):570–580

Jiang Z, Zhao L, Li S, Jia Y (2020) Real-time object detection method based on improved YOLOv4-tiny. arXiv preprint arXiv:2011.04244

Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R (2019) A survey of deep learning-based object detection. IEEE Access 7:128837–128868

Jin R, Lin D (2019) Adaptive anchor for fast object detection in aerial image. IEEE Geosci Remote Sens Lett 17(5):839–843

Jin Y, Cai J, Xu J, Huan Y, Yan Y, Huang B, Guo Y, Zheng L, Zou Z (2021) Self-aware distributed deep learning framework for heterogeneous IoT edge devices. Futur Gener Comput Syst 125:908–920

Kamal KC, Yin Z, Wu M, Wu Z (2019) Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric 165:104948

Kamath V, Renuka A (2023) Deep learning based object detection for resource constrained devices: systematic review, future trends and challenges ahead. Neurocomputing 531:34–60

Kang H, Zhou H, Wang X, Chen C (2020) Real-time fruit recognition and grasping estimation for robotic apple harvesting. Sensors 20(19):5670

Ke X, Lin X, Qin L (2021) Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios. Mach vis Appl 32:1–23

Kim W, Jung WS, Choi HK (2019) Lightweight driver monitoring system based on multi-task mobilenets. Sensors 19(14):3200

Kim K, Jang SJ, Park J, Lee E, Lee SS (2023) Lightweight and energy-efficient deep learning accelerator for real-time object detection on edge devices. Sensors 23(3):1185

Kim KH, Hong S, Roh B, Cheon Y, and Park M (2016) Pvanet: Deep but lightweight neural networks for real-time object detection. arXiv preprint arXiv:1608.08021 .

Kondaveeti HK, Kumaravelu NK, Vanambathina SD, Mathe SE, Vappangi S (2021) A systematic literature review on prototyping with Arduino: applications, challenges, advantages, and limitations. Comput Sci Rev 40:100364

Kong T, Sun F, Liu H, Jiang Y, Li L, Shi J (2020a) Foveabox: beyound anchor-based object detection. IEEE Trans Image Process 29:7389–7398

Kong Z, Xiong F, Zhang C, Fu Z, Zhang M, Weng J, Fan M (2020b) Automated maxillofacial segmentation in panoramic dental X-ray images using an efficient encoder-decoder network. IEEE Access 8:207822–207833

Koubaa A, Ammar A, Kanhouch A, AlHabashi Y (2021) Cloud versus edge deployment strategies of real-time face recognition inference. IEEE Trans Netw Sci Eng 9(1):143–160

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 25

Kyrkou C (2020) YOLOpeds: efficient real-time single-shot pedestrian detection for smart camera applications. IET Comput Vision 14(7):417–425

Kyrkou C (2021) C 3 Net: end-to-end deep learning for efficient real-time visual active camera control. J Real-Time Image Proc 18(4):1421–1433

Kyrkou C, Theocharides T (2020) EmergencyNet: efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE J Sel Top Appl Earth Observ Remote Sens 13:1687–1699

Lai CY, Wu BX, Shivanna VM, Guo JI (2021) MTSAN: multi-task semantic attention network for ADAS applications. IEEE Access 9:50700–50714

Lan H, Meng J, Hundt C, Schmidt B, Deng M, Wang X, Liu W, Qiao Y, Feng S (2019) FeatherCNN: fast inference computation with TensorGEMM on ARM architectures. IEEE Trans Parallel Distrib Syst 31(3):580–594

Law H, Deng J (2018) Cornernet: detecting objects as paired keypoints. In Proceedings of the European conference on computer vision (ECCV) (pp. 734–750)

Law H, Teng Y, Russakovsky O, Deng J (2019) Cornernet-lite: efficient keypoint based object detection. arXiv preprint arXiv:1904.08900

Li J, Ye J (2023) Edge-YOLO: lightweight infrared object detection method deployed on edge devices. Appl Sci 13(7):4402

Li X, Wang W, Wu L, Chen S, Hu X, Li J, Tang J, Yang J (2020a) Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection. Adv Neural Inf Process Syst 33:21002–21012

Li P, Han L, Tao X, Zhang X, Grecos C, Plaza A, Ren P (2020b) Hashing nets for hashing: a quantized deep learning to hash framework for remote sensing image retrieval. IEEE Trans Geosci Remote Sens 58(10):7331–7345

Li Y, Li M, Qi J, Zhou D, Zou Z, Liu K (2021a) Detection of typical obstacles in orchards based on deep convolutional neural network. Comput Electron Agric 181:105932

Li Z, Liu X, Zhao Y, Liu B, Huang Z, Hong R (2021b) A lightweight multi-scale aggregated model for detecting aerial images captured by UAVs. J vis Commun Image Represent 77:103058

Li C, Fan Y, Cai X (2021c) PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation. BMC Bioinf 22:1–11

Li T, Wang J, Zhang T (2022a) L-DETR: a light-weight detector for end-to-end object detection with transformers. IEEE Access 10:105685–105692

Li S, Yang Z, Nie H, Chen X (2022b) Corn disease detection based on an improved YOLOX-Tiny network model. Int J Cognit Inform Nat Intell (IJCINI) 16(1):1–8

Li H, Lin Z, Shen X, Brandt J, Hua G (2015) A convolutional neural network cascade for face detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5325–5334)

Li Z, Peng C, Yu G, Zhang X, Deng Y, Sun J (2017) Light-head r-cnn: In defense of two-stage object detector. arXiv preprint arXiv:1711.07264

Li Y, Li J, Lin W, Li J (2018) Tiny-DSOD: lightweight object detection for resource-restricted usages. arXiv preprint arXiv:1807.11013

Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6054–6063)

Liang L, Wang G (2021) Efficient recurrent attention network for remote sensing scene classification. IET Image Proc 15(8):1712–1721

Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part V 13 (pp. 740–755). Springer International Publishing

Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980–2988)

Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117–2125)

Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020a) Deep learning for generic object detection: a survey. Int J Comput Vision 128:261–318

Liu X, Liu B, Liu G, Chen F, Xing T (2020b) Mobileaid: a fast and effective cognitive aid system on mobile devices. IEEE Access 8:101923–101933

Liu J, Li Q, Cao R, Tang W, Qiu G (2020c) MiniNet: an extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation. ISPRS J Photogramm Remote Sens 166:255–267

Liu X, Li Y, Shuang F, Gao F, Zhou X, Chen X (2020d) ISSD: improved SSD for insulator and spacer online detection based on UAV system. Sensors 20(23):6961

Liu Y, Sun P, Wergeles N, Shang Y (2021a) A survey and performance evaluation of deep learning methods for small object detection. Expert Syst Appl 172:114602

Liu S, Guo B, Ma K, Yu Z, Du J (2021b) AdaSpring: context-adaptive and runtime-evolutionary deep model compression for mobile applications. Proc ACM Interact Mobile Wearable Ubiquitous Technol 5(1):1–22

Liu Z, Ma J, Weng J, Huang F, Wu Y, Wei L, Li Y (2021c) LPPTE: a lightweight privacy-preserving trust evaluation scheme for facilitating distributed data fusion in cooperative vehicular safety applications. Inf Fus 73:144–156

Liu Y, Zhang C, Wu W, Zhang B, Zhou F (2022a) MiniYOLO: a lightweight object detection algorithm that realizes the trade-off between model size and detection accuracy. Int J Intell Syst 37(12):12135–12151

Liu T, Wang J, Huang X, Lu Y, Bao J (2022b) 3DSMDA-Net: an improved 3DCNN with separable structure and multi-dimensional attention for welding status recognition. J Manuf Syst 62:811–822

Liu S, Huang D (2018) Receptive field block net for accurate and fast object detection. In Proceedings of the European conference on computer vision (ECCV) (pp. 385–400)

Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21–37). Springer International Publishing

Long F (2020) Microscopy cell nuclei segmentation with enhanced U-Net. BMC Bioinf 21(1):8

Long ZHOU, Suyuan W, Zhongma CUI, Jiaqi FANG, Xiaoting YANG, Wei D (2020b) Lira-YOLO: a lightweight model for ship detection in radar images. J Syst Eng Electron 31(5):950–956

Long X, Deng K, Wang G, Zhang Y, Dang Q, Gao Y, Shen H, Ren J, Han S, Ding E, Wen S (2020) PP-YOLO: An effective and efficient implementation of object detector. arXiv preprint arXiv:2007.12099

Lu Y, Zhang L, and Xie W (2020) YOLO-compact: an efficient YOLO network for single category real-time object detection. In 2020 Chinese control and decision conference (CCDC) (pp. 1931–1936). IEEE

Luo X, Zhu J, Yu Q (2019) Efficient convNets for fast traffic sign recognition. IET Intel Transport Syst 13(6):1011–1015

Ma N, Yu X, Peng Y, Wang S (2019) A lightweight hyperspectral image anomaly detector for real-time mission. Remote Sens 11(13):1622

Ma M, Ma W, Jiao L, Liu X, Li L, Feng Z, Yang S (2023) A multimodal hyper-fusion transformer for remote sensing image classification. Inf Fus 96:66–79

Ma N, Zhang X, Zheng HT, Sun J (2018) Shufflenet v2: practical guidelines for efficient CNN architecture design. In Proceedings of the European conference on computer vision (ECCV) (pp. 116–131)

Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In 2015 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 4959–4962). IEEE

Makkar A, Ghosh U, Rawat DB, Abawajy JH (2021) Fedlearnsp: preserving privacy and security using federated learning and edge computing. IEEE Consumer Electron Mag 11(2):21–27

Mansouri SS, Kanellakis C, Kominiak D, Nikolakopoulos G (2020) Deploying MAVs for autonomous navigation in dark underground mine environments. Robot Auton Syst 126:103472

Mao QC, Sun HM, Liu YB, Jia RS (2019) Mini-YOLOv3: real-time object detector for embedded applications. IEEE Access 7:133529–133538

Mehta S, Rastegari M (2021) Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:2110.02178

Mittal P, Singh R, Sharma A (2020) Deep learning-based object detection in low-altitude UAV datasets: a survey. Image vis Comput 104:104046

Muhammad K, Hussain T, Del Ser J, Palade V, De Albuquerque VHC (2019) DeepReS: a deep learning-based video summarization strategy for resource-constrained industrial surveillance scenarios. IEEE Trans Industr Inf 16(9):5938–5947

Nguyen HD, Na IS, Kim SH, Lee GS, Yang HJ, Choi JH (2019) Multiple human tracking in drone image. Multimedia Tools Appl 78:4563–4577

Nguyen TV, Tran AT, Dao NN, Moon H, Cho S (2023) Information fusion on delivery: a survey on the roles of mobile edge caching systems. Inf Fus 89:486–509

Ogden SS, Guo T (2019) Characterizing the deep neural networks inference performance of mobile applications. arXiv preprint arXiv:1909.04783

Ophoff T, Van Beeck K, Goedemé T (2019) Exploring RGB+ Depth fusion for real-time object detection. Sensors 19(4):866

Ouyang Z, Niu J, Liu Y, Guizani M (2019) Deep CNN-based real-time traffic light detector for self-driving vehicles. IEEE Trans Mob Comput 19(2):300–313

Paluru N, Dayal A, Jenssen HB, Sakinis T, Cenkeramaddi LR, Prakash J, Yalavarthy PK (2021) Anam-Net: anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 chest CT images. IEEE Trans Neural Netw Learn Syst 32(3):932–946

Panero Martinez R, Schiopu I, Cornelis B, Munteanu A (2021) Real-time instance segmentation of traffic videos for embedded devices. Sensors 21(1):275

Pang J, Li C, Shi J, Xu Z, and Feng H (2019) R2-CNN: fast tiny object detection in large-scale remote sensing images. arXiv 2019. arXiv preprint arXiv:1902.06042

Paoletti ME, Haut JM, Pereira NS, Plaza J, Plaza A (2021) Ghostnet for hyperspectral image classification. IEEE Trans Geosci Remote Sens 59(12):10378–10393

Picron C, Tuytelaars T (2021) Trident pyramid networks: the importance of processing at the feature pyramid level for better object detection. arXiv preprint arXiv:2110.04004

Ping P, Huang C, Ding W, Liu Y, Chiyomi M, Kazuya T (2023) Distracted driving detection based on the fusion of deep learning and causal reasoning. Inf Fus 89:121–142

Qian S, Ning C, Hu Y (2021) MobileNetV3 for image classification. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) (pp. 490–497). IEEE

Qin Z, Li Z, Zhang Z, Bao Y, Yu G, Peng Y, Sun J (2019) ThunderNet: towards real-time generic object detection on mobile devices. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6718–6727)

Qin S, Liu S (2020) Efficient and unified license plate recognition via lightweight deep neural network. IET Image Proc 14(16):4102–4109

Quang TN, Lee S, Song BC (2021) Object detection using improved bi-directional feature pyramid network. Electronics 10(6):746

Ran X, Chen H, Liu Z, Chen J (2017) Delivering deep learning to mobile devices via offloading. In Proceedings of the Workshop on Virtual Reality and Augmented Reality Network (pp. 42–47)

Rani E (2021) LittleYOLO-SPP: a delicate real-time vehicle detection algorithm. Optik 225:165818

Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263–7271)

Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767

Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788)

Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. https://doi.org/10.1109/TPAMI.2016.2577031

Ren J, Guo Y, Zhang D, Liu Q, Zhang Y (2018) Distributed and efficient object detection in edge computing: challenges and solutions. IEEE Netw 32(6):137–143

Rodriguez-Conde I, Campos C, Fdez-Riverola F (2021) On-device object detection for more efficient and privacy-compliant visual perception in context-aware systems. Appl Sci 11(19):9173

Rui Z, Zhaokui W, Yulin Z (2019) A person-following nanosatellite for in-cabin astronaut assistance: system design and deep-learning-based astronaut visual tracking implementation. Acta Astronaut 162:121–134

Saidi A, Othman SB, Dhouibi M, Saoud SB (2021) FPGA-based implementation of classification techniques: a survey. Integration 81:280–299

Samore A, Rusci M, Lazzaro D, Melpignano P, Benini L, Morigi S (2020) BrightNet: a deep CNN for OLED-based point of care immunofluorescent diagnostic systems. IEEE Trans Instrum Meas 69(9):6766–6775

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510–4520)

Sharma VK, Mir RN (2020) A comprehensive and systematic look up into deep learning based object detection techniques: a review. Comput Sci Rev 38:100301

Article   MathSciNet   Google Scholar  

Shi C, Wang T, Wang L (2020) Branch feature fusion convolution network for remote sensing scene classification. IEEE J Sel Top Appl Earth Observ Remote Sens 13:5194–5210

Shoeibi A, Khodatars M, Jafari M, Ghassemi N, Moridian P, Alizadehsani R, Ling SH, Khosravi A, Alinejad-Rokny H, Lam HK, Fuller-Tyszkiewicz M (2023) Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: a review. Inf Fus 93:85–117

Silva SH, Rad P, Beebe N, Choo KKR, Umapathy M (2019) Cooperative unmanned aerial vehicles with privacy preserving deep vision for real-time object identification and tracking. J Parallel Distrib Comput 131:147–160

Song S, Jing J, Huang Y, Shi M (2021) EfficientDet for fabric defect detection based on edge computing. J Eng Fibers Fabr 16:15589250211008346

Steimle F, Wieland M, Mitschang B, Wagner S, Leymann F (2017) Extended provisioning, security and analysis techniques for the ECHO health data management system. Computing 99:183–201

Subedi P, Hao J, Kim IK, Ramaswamy L (2021) AI multi-tenancy on edge: concurrent deep learning model executions and dynamic model placements on edge devices. In 2021 IEEE 14th International Conference on Cloud Computing (CLOUD) (pp. 31–42). IEEE

Sun Y, Pan B, Fu Y (2021) Lightweight deep neural network for real-time instrument semantic segmentation in robot assisted minimally invasive surgery. IEEE Robot Autom Lett 6(2):3870–3877

Tan M, Chen B, Pang R, Vasudevan V, Sandler M, Howard A, Le QV (2019) Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2820–2828)

Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10781–10790)

Tang Q, Li J, Shi Z, Hu Y (2020) Lightdet: a lightweight and accurate object detection network. In ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2243–2247). IEEE

Tang Z, Liu X, Shen G, and Yang B (2020) Penet: object detection using points estimation in aerial images. arXiv preprint arXiv:2001.08247 .

Tsai WC, Lai JS, Chen KC, Shivanna V, Guo JI (2021) A lightweight motional object behavior prediction system harnessing deep learning technology for embedded adas applications. Electronics 10(6):692

Tzelepi M, Tefas A (2020) Improving the performance of lightweight CNNs for binary classification using quadratic mutual information regularization. Pattern Recogn 106:107407

Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vision 104:154–171

Ullah A, Muhammad K, Ding W, Palade V, Haq IU, Baik SW (2021) Efficient activity recognition using lightweight CNN and DS-GRU network for surveillance applications. Appl Soft Comput 103:107102

Véstias MP, Duarte RP, de Sousa JT, Neto HC (2020) Moving deep learning to the edge. Algorithms 13(5):125

Wang RJ, Li X, Ling CX (2018) Pelee: a real-time object detection system on mobile devices. Adv Neural Inf Process Syst. https://doi.org/10.48550/arXiv.1804.06882

Wang X, Han Y, Leung VC, Niyato D, Yan X, Chen X (2020a) Convergence of edge computing and deep learning: a comprehensive survey. IEEE Commun Surv Tutor 22(2):869–904

Wang F, Xie F, Shen S, Huang L, Sun R, Le Yang J (2020c) A novel multiface recognition method with short training time and lightweight based on ABASNet and H-softmax. IEEE Access 8:175370–175384

Wang T, Wang P, Cai S, Zheng X, Ma Y, Jia W, Wang G (2021a) Mobile edge-enabled trust evaluation for the Internet of Things. Inf Fus 75:90–100

Wang J, Huang R, Guo S, Li L, Zhu M, Yang S, Jiao L (2021c) NAS-guided lightweight multiscale attention fusion network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 59(10):8754–8767

Wang D, Ren J, Wang Z, Zhang Y, Shen XS (2022a) PrivStream: a privacy-preserving inference framework on IoT streaming data at the edge. Inf Fus 80:282–294

Wang G, Ding H, Li B, Nie R, Zhao Y (2022b) Trident-YOLO: improving the precision and speed of mobile device object detection. IET Image Proc 16(1):145–157

Wang Y, Wang J, Zhang W, Zhan Y, Guo S, Zheng Q, Wang X (2022c) A survey on deploying mobile deep learning applications: a systemic and technical perspective. Digit Commun Netw 8(1):1–17

Wang X, Zhao Q, Jiang P, Zheng Y, Yuan L, Yuan P (2022d) LDS-YOLO: a lightweight small object detection method for dead trees from shelter forest. Comput Electron Agric 198:107035

Wang C, Wang Z, Li K, Gao R, Yan L (2023b) Lightweight object detection model fused with feature pyramid. Multimedia Tools Appl 82(1):601–618

Wang K, Liew JH, Zou Y, Zhou D, Feng J (2019) Panet: few-shot image semantic segmentation with prototype alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9197–9206)

Wang CY, Liao HYM, Wu YH, Chen PY, Hsieh JW, Yeh IH (2020) CSPNet: a new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 390–391).

Wang CY, Bochkovskiy A, Liao HYM (2021) Scaled-yolov4: scaling cross stage partial network. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13029–13038)

Wang CY, Bochkovskiy A, Liao HYM (2023) YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7464–7475)

Wu Q, Wang H, Liu Y, Zhang L, Gao X (2019) SAT: single-shot adversarial tracker. IEEE Trans Industr Electron 67(11):9882–9892

Wu X, Sahoo D, Hoi SC (2020) Recent advances in deep learning for object detection. Neurocomputing 396:39–64

Wu Y, Feng S, Huang X, Wu Z (2021) L4Net: an anchor-free generic object detector with attention mechanism for autonomous driving. IET Comput Vision 15(1):36–46

Xiao Y, Tian Z, Yu J, Zhang Y, Liu S, Du S, Lan X (2020) A review of object detection based on deep learning. Multimedia Tools Appl 79:23729–23791

Xu D, Wu Y (2021) FE-YOLO: a feature enhancement network for remote sensing target detection. Remote Sens 13(7):1311

Xu Z, Liu W, Huang J, Yang C, Lu J, Tan H (2020) Artificial intelligence for securing IoT services in edge computing: a survey. Secur Commun Netw 2020(1):8872586

Xu C, Zhu G, Shu J (2021) A lightweight and robust lie group-convolutional neural networks joint representation for remote sensing scene classification. IEEE Trans Geosci Remote Sens 60:1–15

Xu M, Liu J, Liu Y, Lin F X, Liu Y, Liu X (2019) A first look at deep learning apps on smartphones. In The World Wide Web Conference (pp. 2125–2136)

Xu S, Wang X, Lv W, Chang Q, Cui C, Deng K, Wang G, Dang Q, Wei S, Du Y, Lai B (2022) PP-YOLOE: an evolved version of YOLO. arXiv preprint arXiv:2203.16250

Yang Z, Rothkrantz, L (2011) Surveillance system using abandoned object detection. In Proceedings of the 12th international conference on computer systems and technologies (pp. 380–386)

Yang Z, Liu S, Hu H, Wang L, Lin S (2019) Reppoints: point set representation for object detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9657–9666)

Yi Z, Yongliang S, Jun Z (2019) An improved tiny-yolov3 pedestrian detection algorithm. Optik 183:17–23

Yin R, Zhao W, Fan X, Yin Y (2020) AF-SSD: an accurate and fast single shot detector for high spatial remote sensing imagery. Sensors 20(22):6530

Yin T, Chen W, Liu B, Li C, Du L (2023) Light “You Only Look Once”: an improved lightweight vehicle-detection model for intelligent vehicles under dark conditions. Mathematics 12(1):124

Yu J, Jiang Y, Wang Z, Cao Z, Huang T (2016) Unitbox: an advanced object detection network. In Proceedings of the 24th ACM International Conference on Multimedia (pp. 516–520)

Yu G, Chang Q, Lv W, Xu C, Cui C, Ji W, Dang Q, Deng K, Wang G, Du Y, Lai B, Ma Y (2021) PP-PicoDet: a better real-time object detector on mobile devices. arXiv preprint arXiv:2111.00902

Yuan F, Zhang L, Wan B, Xia X, Shi J (2019) Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition. Mach vis Appl 30:345–358

Zaidi S, Ansari SA, Aslam MS, Kanwal N, Asghar M, Lee B (2022) A survey of modern deep learning based object detection models. Digit Sig Process 126:103514

Zhang S, Wang X, Lei Z, Li SZ (2019a) Faceboxes: a CPU real-time and accurate unconstrained face detector. Neurocomputing 364:297–309

Zhang Y, Liu M, Chen Y, Zhang H, Guo Y (2019b) Real-time vision-based system of fault detection for freight trains. IEEE Trans Instrum Meas 69(7):5274–5284

Zhang X, Lin X, Zhang Z, Dong L, Sun X, Sun D, Yuan K (2020b) Artificial intelligence medical ultrasound equipment: application of breast lesions detection. Ultrason Imaging 42(4–5):191–202

Zhang S, Li Y, Liu X, Guo S, Wang W, Wang J, Ding B, Wu D (2020c) Towards real-time cooperative deep inference over the cloud and edge end devices. Proc ACM Interact Mobile Wearable Ubiquitous Technol 4(2):1–24

Zhang Y, Zhang H, Huang Q, Han Y, Zhao M (2024) DsP-YOLO: an anchor-free network with DsPAN for small object detection of multiscale defects. Expert Syst Appl 241:122669

Zhang S, Wen L, Bian X, Lei Z, Li SZ (2018) Single-shot refinement neural network for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4203–4212)

Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848–6856)

Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2020) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9759–9768)

Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232

Zhao H, Zhou Y, Zhang L, Peng Y, Hu X, Peng H, Cai X (2020a) Mixed YOLOv3-LITE: a lightweight real-time object detection method. Sensors 20(7):1861

Zhao Z, Zhang Z, Xu X, Xu Y, Yan H, Zhang L (2020b) A lightweight object detection network for real-time detection of driver handheld call on embedded devices. Comput Intell Neurosci 2020(1):6616584

Zhao Y, Yin Y, Gui G (2020c) Lightweight deep learning based intelligent edge surveillance techniques. IEEE Trans Cognit Commun Netw 6(4):1146–1154

Zheng G, Chai WK, Duanmu JL, Katos V (2023) Hybrid deep learning models for traffic prediction in large-scale road networks. Inf Fus 92:93–114

Zhou Y (2024) A YOLO-NL object detector for real-time detection. Expert Syst Appl 238:122256

Zhou T, Fan DP, Cheng MM, Shen J, Shao L (2021a) RGB-D salient object detection: a survey. Comput Visual Media 7:37–69

Zhou X, Li X, Hu K, Zhang Y, Chen Z, Gao X (2021b) ERV-Net: an efficient 3D residual neural network for brain tumor segmentation. Expert Syst Appl 170:114566

Zhou L, Rao X, Li Y, Zuo X, Qiao B, Lin Y (2022) A lightweight object detection method in aerial images based on dense feature fusion path aggregation network. ISPRS Int J Geo Inf 11(3):189

Zhou X, Wang D, Krähenbühl P (2019) Objects as points. arXiv preprint arXiv:1904.07850

Zhou X, Zhuo J, Krahenbuhl P (2019) Bottom-up object detection by grouping extreme and center points. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 850–859)

Zhou L, Wei S, Cui Z, Ding W (2019) YOLO-RD: a lightweight object detection network for range doppler radar images. In IOP Conference Series: Materials Science and Engineering (Vol. 563, No. 4, p. 042027). IOP Publishing

Zhu Z, He X, Qi G, Li Y, Cong B, Liu Y (2023) Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI. Inf Fus 91:376–387

Zitnick CL, Dollár P (2014) Edge boxes: locating object proposals from edges. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part V 13 (pp. 391–405). Springer International Publishing

Zou Z, Chen K, Shi Z, Guo Y, Ye J (2023) Object detection in 20 years: a survey. Proc IEEE 111(3):257–276

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Mittal, P. A comprehensive survey of deep learning-based lightweight object detection models for edge devices. Artif Intell Rev 57 , 242 (2024). https://doi.org/10.1007/s10462-024-10877-1

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Accepted : 25 July 2024

Published : 10 August 2024

DOI : https://doi.org/10.1007/s10462-024-10877-1

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