Dear Dr. Jin,

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Kind regards,

Fatih Uysal, Ph.D.

Academic Editor

PLOS ONE

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“the Ministry of Emergency Management of the People's Republic of China�Grant numbers:Jilin-0050-2018AQ.”

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Additional Editor Comments:

Please revise the paper according to my comments below in addition to the referee comments.

1. YOLO v5 was preferred for object detection operations in the study. Please explain in detail why v5 was used in particular, although it is much more up-to-date in the literature (such as YOLO v12).

2. The literature review part of the study is very limited. This part definitely needs to be detailed. In addition, a literature table consisting of columns such as "pros and cons, originality, results, used dataset" should be added.

3. Like many metrics in object detection, the choice of loss function is also very important. For this reason, please explain in detail how the two different SIOU-loss and EIOU-loss used in this study were determined and whether any customization was made at this point.

4. There are very serious deficiencies in the evaluation metrics section. For this reason, metrics such as Average Recall (AR), Optimal Localization Recall Precision (oLRP), Precision-Recall Curve and Count of predicted bounding box should also be obtained.

5. In order to compare the results and reveal the superiority of the proposed approach in the literature, it is very important to obtain and compare the results for this dataset with several state-of-the-art models.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Need some improvements. Please check file. Please include all this improvement in your paper. Acknowledge the limitations of the current study, such as potential biases in the dataset or overfitting due to the model complexity.

Reviewer #2: Clarity in Problem Statement

Comment: "Ensure that the problem statement is clearly defined. While the paper mentions hidden dangers, it would be helpful to specifically define what qualifies as a 'hidden danger' on construction sites (e.g., poorly stored equipment, unsafe scaffolding, electrical hazards)."

2. Data and Dataset Clarification

Comment: "Provide more details on the dataset used. Are the images annotated manually or are they sourced from existing databases? A brief explanation of how the data was collected and pre-processed could enhance the reader's understanding."

3. YOLO Implementation

Comment: "The paper mentions using YOLO for object detection. Please clarify which version of YOLO (YOLOv3, YOLOv4, YOLOv5, etc.) was used and why this version was chosen over others, especially in the context of construction site recognition."

4. Accuracy and Performance Metrics

Comment: "The paper could benefit from more detailed performance metrics, such as precision, recall, F1 score, or mAP (mean Average Precision). A comparison of YOLO's performance versus other potential detection algorithms could provide more context for its efficacy in this application."

5. Challenges and Limitations

Comment: "While the system seems promising, it would be good to address any limitations encountered during implementation. For example, how does the model handle occlusion or cluttered environments typical of construction sites? Are there any false positive or false negative challenges?"

6. Real-Time Application

Comment: "The real-time application of this system is mentioned but not elaborated upon. Can the system be deployed effectively in real-world scenarios with live video feeds from construction sites? A brief discussion of the computational efficiency or hardware requirements would be valuable."

7. Safety Considerations

Comment: "It would be useful to briefly discuss how the system could be integrated with existing safety protocols on construction sites. How will the system alert workers or managers to detected hidden dangers? Are there considerations for worker privacy or data security?"

8. Further Improvements

Comment: "Consider adding a section on potential future work. For instance, could this system be expanded to detect additional types of hazards (e.g., environmental conditions like weather or lighting changes)? What are the next steps for improving detection accuracy?"

9. Related Work

Comment: "Please consider including a brief comparison to related work or similar AI-based hazard detection systems. How does the proposed system stand out in terms of accuracy, scalability, or real-time detection?"

10. Conclusion

Comment: "The conclusion should not only summarize the findings but also emphasize the practical implications of the system for improving construction site safety. A brief mention of its potential impact on reducing accidents or improving safety audits could strengthen the conclusion."

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Reviewer #1: No

Reviewer #2: Yes:  DR. SHERIN ZAFAR

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Attachments

Attachment

Submitted filename: Rew_Document.docx

YOLO-based intelligent recognition system for hidden dangers at construction sites

PONE-D-25-09028R1

Dear Dr. Jin,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Fatih Uysal, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The article was accepted because the revisions satisfactorily addressed the referee comments, and the work shows strong potential to contribute to the literature.

Reviewers' comments: