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Academic Editor

PLOS ONE

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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: Partly

Reviewer #2: Partly

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

Reviewer #1: No

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: The Manuscript " Application of LHAT-YOLO Model in Engineering: Research on Intelligent Monitoring Algorithm for Helmets at Construction Sites" conducts a study on the helmets detection algorithm based on Yolo11. Through comparisons with different Yolo algorithms and experiments on different datasets, the advantages of the improved algorithm are verified but the advantages are not clearly. Generally speaking, this paper has a certain degree of academic value, but the innovation is insufficient. The paper needs to be significantly revised in the following aspects:

1. The title is too long and the first half of the title can be deleted. Generally, "study" should be used instead of "research" in the title.

2. The last paragraph of the introduction does not sufficiently summarize the innovation. The overall innovation of the manuscript is insufficient, and the manuscript does not reflect the issue of systematic real-time performance.

3. In Figure 5, the modification of YOLOV11 mainly lies in where it is located, but the representation in the figure is insufficient.

4. From the perspective of ablation experiments in Table 2, the improvement in algorithm performance is not obviously significant overall? And in the column of parameter quantity in Table 2, 6 should be an exponent.

5. In fact, there are many studies on safety helmet detection algorithms. This manuscript only compared with other Yolo algorithms in Table 3, and did not compare with other optimization algorithms based on Yolo. Therefore, it cannot fully reflect the innovation of the author's work.

6. The summary of the entire manuscript in the conclusion is not simple enough. The title is "Engineering Application ", but this point was not reflected at the end.

Reviewer #2: This manuscript proposes LHAT YOLO, a lightweight variant of YOLOv11 for detecting hardhat use on construction sites. The authors replace parts of the YOLOv11 backbone with GSConv and introduce a new Fast Convolutional Detection (FCD) head that includes “difference convolutions” to emphasize local gradients. On a self compiled dataset of 24,726 images labeled “helmet” and “person,” they report 11% lower GFLOPs and 9.5% fewer parameters than YOLOv11n, with precision 93.95%, recall 88.99%, mAP@50 94.92%, and mAP@50–95 65.28%. Qualitative examples suggest better robustness at night, under occlusion, and in clutter. However, this manuscript must be improved more for publication.

The manuscript can be enhanced by addressing the following comments:

1. Abstract is missing thorough review of applications of the most recent AI techniques and drones in safety helmet detection, for example, application of Transformer in M.Z. Shanti et al. Enhancing worker safety at heights: A deep learning model for detecting helmets and harness using DETR architecture, IEEE Access, 2025, pp151788-151802, and application of drones in M.Z. Shanti et al. Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning, Journal of Safety Research, 2022, 83, pp364-370.

2. The FCD description is conceptual, but reproducibility requires exact architectural details: in/out channels and strides per branch, kernel sizes, the composition and ordering of CDC/HDC/VDC/ADC, normalization types, activation function placement, and how outputs are fused (Fig. 4a–b; pages 13–14). Please provide a layer by layer specification or an open source config file. Also, cite the original “Central Difference Convolution” work and any sources for the horizontal/vertical/angular variants referenced on lines 134–136.

3. The manuscript repeatedly refers to adding a “complex object detection layer” to YOLOv11 (lines 140–146). Please specify whether you added an extra detection scale (e.g., P2 with stride 4 or 8) and how it interfaces with the neck. Include final detection strides, feature map sizes for 640×640 input, and the number of heads. Sentence to amend: “Due to the complex and changing environment at the construction site. The study chose to add a complex object detection layer…” Proposed: “To better detect small and occluded objects, we add an additional detection scale at [specify level, e.g., P2, stride 8], fed by [neck block(s)], yielding [K] detection heads at strides [s1, s2, s3,(s4)].”

4. On lines 72–75, GFLOPs is described as “the number of floating point operations performed per second by the model.” That defines FLOPS (rate), not FLOPs (count). Please correct to “theoretical number of floating point operations per forward pass (at 640×640).” Sentence to amend: “GFLOPs refers to the number of floating point operations performed per second by the model.” Proposed: “GFLOPs denotes the number of floating point operations per forward pass at the specified input size; we report theoretical complexity estimated from the architecture.”

5. The paper lists OS/GPU/CPU and two hyperparameters but omits batch size, optimizer (SGD/AdamW), scheduler (cosine, one cycle), momentum/β, label smoothing, augmentations (Mosaic/MixUp/HSV/flip), EMA, NMS IoU and confidence thresholds, loss composition, number of epochs, and random seeds (lines 91–95). Please add these details and provide a public repository with training scripts and model weights.

6. Provide the training performance of the model, e.g., the plots of loss vs. epochs.

7. Provide tests for various environmental and image conditions, i.e., foggy, blurred, etc.

8. Figures and text mention modules such as C2PSA, PSABlock, C3k2, SPPF in Fig. 5 without definitions in the main text. Please include short descriptions and citations for each non standard component. Also, unify “heat map/hot map” to “heatmap” throughout, e.g., lines 184–186 and lines 203–204. Sentence to amend: “Similarly, the hot map results also show…” Proposed: “Similarly, the heatmap results show…”.

9. There are several grammatical errors in methodological sentences. Example: “A ablation experiment was conducted…” (line 154). Sentence to amend: “A ablation experiment…” Proposed: “An ablation experiment…”. Also, “Due to the complex and changing environment at the construction site. The study chose to add…” (lines 140–142) should be one sentence with a clear subject. These occur in sections where precision matters for readers trying to reimplement the approach.

10. Add the dataset size and the baseline(s) used for the 11%/9.5% reductions, and replace “However,” before the metric list with “Overall,” to avoid the implication of a trade off that the results do not show (Abstract lines 14–21).

11. Provide examples where LHAT YOLO fails, not just YOLOv11 failures, to guide future work.

12. Use “mAP@50” and “mAP@50–95” or “mAP50 / mAP50–95” consistently.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

LHAT-YOLO : Study on Intelligent Monitoring Algorithm for Helmets at Construction Sites

PONE-D-25-44125R1

Dear Dr. Wang,

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,

Yile Chen, Ph.D. in Architecture

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: The author has revised and improved the paper according to the suggested corrections, and it is recommended for acceptance.

Reviewer #2: Authors addressed all the comments properly. Therefore, I recommend the acceptance of this manuscript.

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

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