PONE-D-24-31889

Enhanced Object Detection in Low-Visibility Haze Conditions with YOLOv9s PLOS ONE

Dear Dr. Zhou,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Xiongkuo Min

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?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: Partly

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

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: No

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this paper, authors introduce an innovative object detection framework designed to enhance

visibility and accuracy in low-visibility haze conditions. The approach begins by applying haze to the COCO dataset and then employs a dehazing module that leverages an improved GAN with an integrated patchwise contrastive loss function to generate dehazed images. These images are subsequently fed into an advanced YOLOv9s object detection model, which has been refined with the addition of the Efficient Multi-Scale Attention module and the Wise-IoU loss function to boost detection precision. Comprehensive experiments conducted on the hazed COCO dataset demonstrate the superiority of the proposed model in terms of detection accuracy. However, there are some major issues, which need to be handled in the text properly:

Authors compare the proposed technique with existing techniques in terms of detection accuracy. Authors should also compare the proposed technique with existing techniques in terms of computation times, training times and storage sizes.

The failure case of the proposed technique must be described and discussed in the text.

In this paper, authors propose a technique to alleviate the nonuniform illumination obtained with non-collimated light sources performing Reflectance Transformation Imaging (RTI) acquisitions. It’s shown that it is possible to obtain a per-pixel estimation of light-surface distance as well as light angular position knowing the real size of the surface as well as the light distance and angle at the center of the surface. It’s also shown that, using the estimated distances and the elevation angles, it is possible to obtain adjustment coefficients following the illumination model of a point light source. Authors show the efficiency of the proposed method on RTI acquisitions performed on cultural heritage objects and a manufactured surface. Authors show that the proposed method corrects the effects of non-uniform illumination and leads to improve the relighting commonly associated with RTI. However, there are some major issues, which need to be handled in the text properly:

Authors should compare the proposed technique with existing techniques both numerically and visually.

The failure case of the proposed technique must be described and discussed in the text.

The source code of the proposed technique must be shared to implement it easily.

Authors should cite the following recent object detection paper [Azadvatan2024arXiv].

At the end of Conclusion section, authors can also give some important future directions to the readers, researchers.

@article{Azadvatan2024arXiv,

author = {{Azadvatan}, Yashar and {Kurt}, Murat},

title = {MelNet: A Real-Time Deep Learning Algorithm for Object Detection},

journal = {arXiv preprint arXiv:2401.17972},

pages = {arXiv:2401.17972},

year = {2024},

month = jan,

eid = {arXiv:2401.17972},

doi = {10.48550/arXiv.2401.17972},

url = {https://doi.org/10.48550/arXiv.2401.17972},

archivePrefix = {arXiv},

eprint = {2401.17972},

primaryClass = {cs.CV},

keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning}}

Reviewer #2: The paper titled "Enhanced Object Detection in Low-Visibility Haze Conditions with YOLOv9s" presents a novel framework for object detection in challenging haze environments. The proposed model improves detection accuracy by integrating contrastive learning, multi-scale attention mechanisms, and dynamic focusing techniques, particularly enhancing the YOLOv9s architecture.

However, there are several issues that need to be addressed:

1-There is no comparison between the proposed method and other state-of-the-art methods specifically designed for haze conditions. The authors should include a more detailed discussion and comparative analysis, explaining why the proposed method outperforms others.

2-I suggest covering more benchmark metrics for comparison.

3-The conclusion of this paper lacks future directions or suggestions, which are necessary for guiding further research.

4-The manuscript could be strengthened by including experiments on additional datasets, particularly those that capture real-world haze conditions. Alternatively, the authors could discuss the potential limitations of the current approach when applied to real-world data and suggest future research directions to address these challenges.

Reviewer #3: This paper presents a new method for object detection in hazy scenes.

The proposed method seems reasonable, and the paper is well written, but the experimental validation needs some improvement.

In the introduction and related work sections, some of the relevant works are missing, especially the works about the evaluation of haze and dehazing, for example, HazDesNet: an end-to-end network for haze density prediction; Quality evaluation of image dehazing methods using synthetic hazy images; Objective quality evaluation of dehazed images.

“…This degradation in image quality can severely impact the accuracy of computer vision tasks, such as object detection…”

“…Eliminating haze from images not only improves image quality but also enhances information readability…”

Some related surveys are suggested to be given for better referring of the related topics, for example visual quality as its evaluation as discussed in the related surveys, e.g., ‘Perceptual image quality assessment: a survey’, ‘Perceptual video quality assessment: a survey’, and ‘Screen content quality assessment: Overview, benchmark, and beyond’.

The experimental validation needs to be strengthened. For example, the combinations of SOTA dehazing methods and SOTA detection methods are also suggested to be included into the comparison.

The formats of some references are suggested to be double-checked.

Reviewer #4: Strengths:

- The manuscript is clearly written, with a logical structure that aids in understanding.

- Complex ideas are conveyed in a manner that is accessible to a wide audience.

Weaknesses:

- The experimental design is not detailed enough to substantiate the theoretical claims, with inadequate information in the code and experiment sections.

- The paper lacks a focus on parameter fine-tuning, which is critical for evaluating the model's robustness.

- There is no discussion of the computational costs, especially during inference, which is an important aspect to consider.

- The literature review is not comprehensive enough, failing to provide sufficient comparisons with established methods.

- The absence of standard deviations in the reported results undermines their statistical validity.

- The manuscript does not address how the model generalizes to unseen data, which is crucial for its practical use.

- There is a lack of discussion on potential limitations and areas for future research, leaving the reader with an incomplete picture of the work's scope.

Additional Comment: What exactly sets your novel method apart? Is it akin to a hypothetical 'YOLOv9'? The paper claims to enhance object detection in low-visibility haze conditions with YOLOv9s, but it is essential to validate this claim by using more public datasets. Proving that the proposed model is truly the best requires thorough comparative experiments.

The most significant revisions will likely revolve around addressing the concerns outlined above. In general, I anticipate:

- A more thorough discussion and comparison with existing work;

- Additional experiments with detailed statistical analysis;

- Refinements to the discussion to better evaluate the strengths and limitations of the approach.

Summary of Reasons for Rejection:

Significant revisions are required, including a more thorough experimental design, detailed statistical reporting, and discussions on generalization, limitations, and future work. These improvements are necessary for the manuscript to be reconsidered.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: Yes: Teerapong Panboonyuen

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Attachments

Attachment

Submitted filename: reviewer_comments(14.08.2024).pdf

PONE-D-24-31889R1 Enhanced Object Detection in Low-Visibility Haze Conditions with YOLOv9s PLOS ONE

Dear Dr. Zhou,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jan 17 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Xiongkuo Min

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

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

Reviewer #1: No

Reviewer #3: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this paper, authors introduce an innovative object detection framework designed to enhance visibility and accuracy in low-visibility haze conditions. The approach begins by applying haze to the COCO dataset and then employs a dehazing module that leverages an improved GAN with an integrated patchwise contrastive loss function to generate dehazed images. These images are subsequently fed into an advanced YOLOv9s object detection model, which has been refined with the addition of the Efficient Multi-Scale Attention module and the Wise-IoU loss function to boost detection precision. Comprehensive experiments conducted on the hazed COCO dataset demonstrate the superiority of the proposed model in terms of detection accuracy. In the revised paper, it seems that authors handled most of the reviewer comments into account. However, there are some minor issues, which need to be handled in the text properly:

Authors compare the proposed technique with existing techniques numerically. Authors should also compare the proposed technique with existing techniques visually.

Reviewer #3: Most of the reviewer's previous concerns are addressed. The paper is now suggested to be published.

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7. PLOS authors have the option to publish the peer review history of their article (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 #3: No

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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.

Attachments

Attachment

Submitted filename: reviewer_comments(02.12.2024).pdf

Enhanced Object Detection in Low-Visibility Haze Conditions with YOLOv9s

PONE-D-24-31889R2

Dear Dr. Zhou,

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,

Xiongkuo Min

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: