-->PONE-D-25-55470-->-->YOLO-Net: A lightweight edge-enhanced detection model for small-object recognition in tennis match scenarios -->-->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: Yes

Reviewer #3: Partly

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

Reviewer #1: N/A

Reviewer #2: Yes

Reviewer #3: 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: No

Reviewer #3: No

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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: No

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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: Addresses a real and relevant application: small-object detection (tennis ball, racquet) in broadcast-style tennis videos.

Proposes a reasonably coherent set of improvements to YOLO11n.

There are some major issues:

1. Clarify the exact novelty and contribution. Right now, the work looks like a well-engineered combination rather than a fundamentally new detection architecture.

2. Experimental evaluation needs to be strengthened. Currently the main comparison is Table 2 (YOLOv5n, v8n, v10n, v11n, “YOLO12n”, YOLO-Net) with P/R/mAP@0.5 only.

3. The dataset is an important asset of the work but is described only briefly.

4. Some essential hyperparameters and design choices are missing or only briefly mentioned.

5. There are several confusing or inconsistent names:

The paper’s main method is called YOLO-Net, but:

Table 1 uses “YOLO-Net-N/S/M/L/X” although only a single variant is actually discussed.

In the Focaler-IoU subsection, “the proposed Soccer-YOLO model” is mentioned (likely a leftover from another paper).

In Figure 7 captions and text, the method appears as “Tennis-YOLO” rather than YOLO-Net.

Table 2 lists “YOLO12n”, which is not explained (YOLO12 doesn’t exist at the time of writing and is not discussed elsewhere).

6. The C3k_MSEIS module is central to the claimed edge enhancement, but readers currently rely mostly on equations and a high-level description.

7. Minor comments (language, notation, formatting).

Reviewer #2: Authors propose YOLO-Net, a lightweight detection framework tailored for tennis event analysis. Authors introduces three innovations: a C3k-MSEIS module that enhances multi-scale edge information and dual-domain feature selection to refine fi-ne-grained boundaries; an ECA channel attention mechanism inserted after C2PSA to strengthen inter-channel dependency modeling and improve feature discriminability; and a Focaler-IoU loss function that emphasizes hard and small samples while reducing lo-calization errors. The results are interesting. However, recent development on AI should be mentioned as IEEE Transactions on Intelligent Vehicles 10, 2642-2652, 2025;Engineering Analysis with Boundary Elements 180, 106457, 2025;Expert Systems With Applications 291, 128279, 2025;Physica D 481, 134689, 2025; Physica D 472, 134362, 2025;Nonlinear Dynamics 112, 1291, 2024;Physics of Fluids, 36, 104127, 2024;Physica D 468, 134284, 2024.

Reviewer #3: The paper proposes YOLO-Net, a lightweight detection framework built upon YOLO11n with the integration of C3k-MSEIS, ECA attention, and Focaler-IoU loss. The study is application-oriented, and the results demonstrate a reasonable balance between accuracy and model efficiency. However, several important issues remain in terms of manuscript quality, experimental completeness and novelty. Therefore, major revision is recommended.

1. There are noticeable word-breaking and formatting problems (e.g., “fi-ne”, “re-al-time”), which should be corrected for clarity and professionalism.

2. The paper contains several abbreviations that are introduced without their full names at first occurrence (e.g., YOLO, DETR, Faster R-CNN, SSD).

3. The comparison experiments in this paper are relatively limited. The proposed method is mainly compared with baseline models such as the original YOLO variants (e.g., YOLOv5, YOLOv8, YOLO11n), which are not specifically optimized for small-object detection. However, a large number of recent studies have proposed improved YOLO-based methods tailored for small-object scenarios. It is recommended that the authors include comparisons with these state-of-the-art improved YOLO models (especially recent variants, e.g., YOLO13, YOLO2026, or other small-object-oriented improvements) to better demonstrate the effectiveness and competitiveness of the proposed method.

4. The novelty of the proposed method requires further clarification. The ECA attention mechanism and Focaler-CIoU loss are existing techniques, and this work mainly integrates them into the YOLO11 framework. It is therefore unclear whether similar integrations have already been explored in prior studies, especially in small-object detection scenarios.

5. Please provide a more comprehensive review of related work on such module combinations and clearly position their contribution relative to existing approaches.

6. The ablation study is relatively limited and does not convincingly demonstrate the individual contributions of each proposed component. The current design follows a purely sequential accumulation, which makes it difficult to disentangle the effect of each module. In particular, key variants such as baseline+ECA, baseline+Focaler-CIoU, and other pairwise combinations are missing.

7. The qualitative results do not convincingly demonstrate the challenging scenarios claimed in the paper. Fig.5 mainly shows standard domain variations (e.g., court color and background), rather than truly difficult cases such as small targets, motion blur, or occlusion. Similarly, Fig.7 presents mostly clean and simple scenes, where both methods perform well, lacking hard examples or failure cases to highlight the advantages of the proposed method. It is recommended to include more representative challenging scenarios to better validate the effectiveness of the approach.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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YOLO-Net: A lightweight edge-enhanced detection model for small-object recognition in tennis match scenarios

PONE-D-25-55470R1

Dear Dr. Jia,

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.

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Additional Editor Comments (optional):

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: Partly

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

Reviewer #1: Yes

Reviewer #3: Yes

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-->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

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-->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: The manuscript presents a well-executed study on lightweight object detection for tennis match scenarios. The proposed YOLO-Net effectively integrates multi-scale edge enhancement, channel attention, and improved localization loss to address the challenges associated with small and fast-moving objects. The methodology is clearly described, and the experimental results, including ablation studies and comparative analyses, provide convincing evidence of the effectiveness of the proposed approach. The manuscript is well organized, the figures and tables are informative, and the discussion appropriately acknowledges the limitations and future research directions. Overall, the work makes a meaningful contribution to sports analytics and real-time object detection research. I commend the authors for their thorough revisions and believe the manuscript is suitable for publication in its current form.

Reviewer #3: The authors have addressed all my previous comments thoroughly, and the manuscript has been significantly improved in quality. I recommend acceptance.

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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.

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Reviewer #1: Yes: Dr. Vipal Kumar Sharma

Reviewer #3: No

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