PONE-D-24-53882LI-YOLOv8: Lightweight Small Target Detection Algorithm for Remote Sensing Images that Combines GSConv and PConvPLOS ONE

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Academic Editor

PLOS ONE

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Additional Editor Comments:

The reviewers have given very detailed revision suggestions, please refer to and improve them carefully, especially in the analysis of the interpretation of the algorithm results. The manuscript needs further improvement.

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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: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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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

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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: The paper titled "LI-YOLOv8: Lightweight Small Target Detection Algorithm for Remote Sensing Images that Combines GSConv and PConv" proposes a lightweight small target detection algorithm for remote sensing images based on YOLOv8n, which integrates GSConv and PConv, termed LI-YOLOv8. The algorithm improves recognition performance through modifications such as replacing the activation function in CBS, embedding an efficient multi-scale attention mechanism (EMA), designing a lightweight detection head (GP-Detect head), and refining the boundary fitting loss function of the algorithm.

While the structure of the paper is generally reasonable, the language lacks conciseness. The limitations of the LI-YOLOv8 model's application scenarios are not discussed, and the use of only two datasets to validate the model's usability is evidently unconvincing.

Suggestions for Improvement:

1. Language and Clarity: Simplify the language to make sentences more concise. Some sections lack clear structure, such as overlapping content in the "Methods" and "Algorithm Implementation" sections. Redundant sentences, especially in the background introduction, affect readability. Overly detailed explanations of terms and formulas may confuse non-specialist readers.

2. Use of Figures and Explanations: There is insufficient explanation and referencing of figures. For example, in figures such as Fig.11 and Fig.12, only the improvement in PR curves is mentioned without detailing the reasons for the changes and their impact on results. It is recommended to provide more analysis and interpretation of the figures in the text.

3. Description of GP-Detect Module: The explanation of how the GP-Detect module reduces redundant computations is somewhat superficial and lacks in-depth theoretical support.

4. Ablation Experiments: Provide more quantitative or qualitative discussions on the specific contributions of each module in the ablation experiments. For instance, what are the primary reasons behind the 1.2% improvement attributed to SPPF-R?

5. Broader Validation: Supplement the study by validating the proposed model and methodology on at least two additional datasets or datasets from related domains to demonstrate the model's generalizability and practical applicability.

Conclusion:

The paper holds certain practical value and is recommended for acceptance after revisions.

Reviewer #2: 1. LI-YOLOv8 shows significant improvements on remote sensing datasets like RSOD and NWPU VHR-10, how does it perform on more diverse and challenging datasets that are not specifically focused on small object detection? Could there be a risk of overfitting the small object characteristics of these datasets?

2. How does LI-YOLOv8 perform on real-world, noisy images with significant occlusions, varying lighting, or seasonal changes (detecting small objects in aerial imagery from different times of day or weather conditions)?

3. The reduction in parameters and GFLOPs is a notable advantage, but how sensitive is the model to changes in hyperparameters like learning rate, batch size, or anchor box configuration? What effect do these parameters have on performance, especially in terms of detection accuracy for small objects?

4. How does the training time and convergence behavior of LI-YOLOv8 compared to other algorithms like YOLOv8 and YOLOv5? Are there any challenges in training this model on large-scale remote sensing datasets?

5. Below Table-6 rephrase the sentence ("YOLOv8n's metrics are close to those of YOLOv5n" "YOLOv8n's metrics are similar to those of YOLOv5n.")

6. While the ablation studies demonstrate improvements when adding different innovations (SPPF-R, C2fE, GP-Detect, Inner-Wise), can you provide more insights into how each innovation specifically addresses challenges in small object detection? Which innovation contributes the most to improving recall or precision?

7. The experiments are mainly conducted on datasets like RSOD, NWPU VHR-10, and TinyPerson, which focus on specific types of remote sensing imagery. How does LI-YOLOv8 perform on datasets from other domains, such as medical imaging or urban surveillance, where small object detection might have different characteristics?

8. Does the model's emphasis on small object detection affect its ability to detect large objects, especially when these appear in similar scenes? Could there be a trade-off where performance on larger objects (e.g., buildings, vehicles) is compromised?

9. In the visual comparison with YOLOv8, LI-YOLOv8 shows fewer false detections in complex backgrounds. However, was there any trade-off between false positives and false negatives in the cases with small targets or occlusions? How does the model handle ambiguous cases?

10. While mAP@0.5 and mAP@0.5:0.95 are widely used, could there be additional evaluation metrics or domain-specific metrics (F1 score for small objects or intersection over union for very small objects) that would give a clearer picture of the model’s real-world performance?

Reviewer #3: 1. The manuscript may require some improvement to the English.

2. From the experimental results presented in Table 1, it can be seen that there is no significant improvement in mAP and other parametersbefore and after using Inner Wise IoU. Similarly, the improvements observed in Table 2 are also insignificant. Is the use of Inner-Wise IoU appropriate?

3. The manuscript mentioned that LI-YOLOv8 not only detects more small targets in complex backgrounds but also does not incur any false detections.However, the detection accuracy of the vehicle in Table 4 has decreased by 5.2%. Does this contradict the conclusion of the manuscript�

4. Tables 5 and 6 seem to be simple stacks of algorithms. Please explain the significance of comparing YOLO algorithms at different stages and versions?

5.Line502-Line504: The previous research compared LI-YOLOv8 with YOLO series algorithms, and this paragraph also compared it with R-cnn. What does the author want to express? The logic of the manuscript is a bit confusing.

It is suggested to revise the manuscript before submitting it

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Reviewer #1: Yes: Qingfang He

Reviewer #2: Yes: Muhammad Wahab Hanif

Reviewer #3: No

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LI-YOLOv8: Lightweight Small Target Detection Algorithm for Remote Sensing Images that Combines GSConv and PConv

PONE-D-24-53882R1

Dear Dr. Yan,

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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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

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: (No Response)

Reviewer #4: 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: (No Response)

Reviewer #4: Yes

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

Reviewer #1: (No Response)

Reviewer #4: No

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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: (No Response)

Reviewer #4: 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: (No Response)

Reviewer #4: 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: (No Response)

Reviewer #4: Authors have revised paper well, but I still have a suggestion: the inference time should be considered as a additional evaluation metric.

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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 #4: No

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