Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system

Huili Dou; Sirui Chen; Fangyuan Xu; Yuanyuan Liu; Hongyang Zhao

doi:10.1371/journal.pone.0314817

Peer Review History

Original SubmissionJuly 17, 2024
17 Jul 2024 Author Response https://doi.org/10.1371/journal.pone.0314817.r001
7 Aug 2024 Decision Letter - Khalil Abdelrazek Khalil, Editor PONE-D-24-29268Analysis of the effect of using the improved YOLOv8 model for vehicle and pedestrian detection in urban traffic monitoring systemPLOS ONE Dear Dr. Dou, 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 Sep 21 2024 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. 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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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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 ****** 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 ****** 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: 1. The methodology section provides detailed descriptions of the improvements made to the YOLOv8 model, including multi-scale feature fusion and the improved non-maximum suppression (NMS) algorithm. However, it would be beneficial to include a flowchart or diagram summarizing the steps of the proposed method to enhance clarity and ensure consistency in understanding the proposed improvements. 2. The manuscript includes several evaluation metrics such as precision, recall, F1-score, mAP0.5, and mAP0.5:0.95. While these metrics are comprehensive, the discussion could benefit from a deeper analysis of the trade-offs between these metrics, particularly in scenarios with dense traffic and occlusions. 3. The manuscript compares the improved YOLOv8 model with several state-of-the-art models. However, the comparison could be more robust by including additional recent models that have shown significant performance in similar tasks. 4. The ablation study effectively demonstrates the contributions of individual components of the improved YOLOv8 model. However, the manuscript would benefit from a more detailed discussion on the impact of each component on the overall model performance. 5. The manuscript claims that the improved YOLOv8 model provides reliable technical support for urban traffic monitoring systems. While the experimental results are promising, the discussion on real-world applicability and deployment challenges is limited. 6. he manuscript provides quantitative results but lacks sufficient visual examples or case studies that illustrate the improved detection capabilities of the YOLOv8 model. Reviewer #2: This paper presents an improved YOLOv8 model for vehicle and pedestrian detection in urban traffic monitoring systems. In order to improve the detection performance of the model, introduced a multi-scale feature fusion module and an improved non-maximum suppression (NMS) algorithm based on the YOLOv8 model. The multi-scale feature fusion module enhances the model’s detection ability for targets of different sizes by combining feature maps of different scales; the improved non-maximum suppression algorithm effectively reduces repeated detection and missed detection by optimizing the screening process of candidate boxes. Experimental results show that the improved YOLOv8 model exhibits excellent detection performance on the VisDrone2019 dataset, and outperforms other classic target detection models and the baseline YOLOv8 model in key indicators such as precision, recall, F1 score, and mean average precision (mAP). The manuscript needs improvement; therefore, I recommend minor revision. The detailed comments are given below. 1)The title of the paper is not impressive; it should be renamed. Additionally, for the experimental work, VisDrone 2019 dataset was used which means this paper is based on UAV technology. Therefore, authors should link this technology in the title for the reader's clear understanding. 2)There is no numbering with each heading and the sub-sections of each heading are not easily understandable. For example, at the end of the related work, there are other methods and methods sessions that do not give proper understanding to the new readers. Furthermore, the initial letters of each word in the sub-session in related work start from small letters, while in the other headings, the first letter of each word in the sub-session starts from a capital letter. It is necessary to set the whole paper format. 3)How the proposed algorithms play an important role in other issues such as occlusion, low-quality images, multispectral images, and joint issues for target detection in traffic congestion using UAVs and AVs as mentioned in the following papers: �Target Detection and Recognition for Traffic Congestion in Smart Cities Using Deep Learning-Enabled UAVs: A Review and Analysis �Deep Learning-Based Pedestrian Detection in Autonomous Vehicles: Substantial Issues and Challenges � Advance generalization technique through 3D CNN to overcome the false positives pedestrian in autonomous vehicles Please justify the positive aspects of the proposed algorithm on other issues for target detection in traffic congestion and also include the mentioned references in it. 4)The author considers the VisDrone2019 dataset rather than the other datasets such as UAVDT, VisioDECT, and Synthetic Drone datasets. Why are the other datasets not considerable for this work? Please elaborate on it. 5)In the results and analysis section, the authors compare the results of the proposed model with other versions but the heading of comparison doesn’t make sense such as it is “Comparison results with other versions of yolov8”, it should be renamed such as “Comparison of the results with other versions of yolov8’ or “Comparing results with other versions of yolov8”. 6)In the introduction and experiment section, pages number 74, 390, 423, 425, and 428; the authors mentioned that we used two public datasets in our experimental work but this paper discusses only VisDrone dataset. What are the brief introduction and experimental results of the second public dataset? If this paper uses only one dataset on the entire paper, then it should be corrected on page number 74, 390, 423, 425, and 428. 7)The VisDrone dataset covers different weather conditions (such as sunny, cloudy, rainy) and time periods (such as day, dusk, and night). The authors need to justify the model results on the weather conditions and time periods. Is the model performing best in the sunny, cloudy, rainy, and daytime or either at nighttime? In which condition is the result very effective? ****** 6. 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 #2: Yes: Muhammad Asim ******** [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. https://doi.org/10.1371/journal.pone.0314817.r002
Revision 1
26 Aug 2024 Author Response Reviewer #1: 1. The methodology section provides detailed descriptions of the improvements made to the YOLOv8 model, including multi-scale feature fusion and the improved non-maximum suppression (NMS) algorithm. However, it would be beneficial to include a flowchart or diagram summarizing the steps of the proposed method to enhance clarity and ensure consistency in understanding the proposed improvements. Response: Thank you for your comments, which made us realize the shortcomings of our work. We have described your comments in the article. For example, the network structure shown in Figure 2 is the detailed process of the module, which we hope can improve the understanding of the article. 2. The manuscript includes several evaluation metrics such as precision, recall, F1-score, mAP0.5, and mAP0.5:0.95. While these metrics are comprehensive, the discussion could benefit from a deeper analysis of the trade-offs between these metrics, particularly in scenarios with dense traffic and occlusions. Response: Thank you for your comments, which made us realize the shortcomings of our work. We have described your comments in the article. In the results and analysis of the experiment section, we described in detail the results of the method on various indicators and the relationship between them. For details, please see the red marked content in the results and analysis. 3. The manuscript compares the improved YOLOv8 model with several state-of-the-art models. However, the comparison could be more robust by including additional recent models that have shown significant performance in similar tasks. Response: Thank you for your comments, which made us aware of the shortcomings of our work. We have added some experimental results of recent articles for comparison in the experimental section, see Table 2 for details. 4. The ablation study effectively demonstrates the contributions of individual components of the improved YOLOv8 model. However, the manuscript would benefit from a more detailed discussion on the impact of each component on the overall model performance. Response: Thank you for your comments, which made us aware of the shortcomings of our work. We have conducted relevant experiments in ablation experiments to investigate the impact of model components on model performance. For details, please refer to the ablation experiment section. 5. The manuscript claims that the improved YOLOv8 model provides reliable technical support for urban traffic monitoring systems. While the experimental results are promising, the discussion on real-world applicability and deployment challenges is limited. Response: Thank you for your comments, which made us realize the shortcomings of our work. Your comments are the limitations of our model, which we have described in the conclusion section and the improvement plan for future work. Please see the red marked content in the conclusion section for details. 6. The manuscript provides quantitative results but lacks sufficient visual examples or case studies that illustrate the improved detection capabilities of the YOLOv8 model. Response: Thank you for your comments, which have helped us to further improve our work. In response to your comments, we have shown the detection results of our method in the visualization results of the experimental part, hoping to better reflect the effectiveness and practicality of our method. Reviewer #2: This paper presents an improved YOLOv8 model for vehicle and pedestrian detection in urban traffic monitoring systems. In order to improve the detection performance of the model, introduced a multi-scale feature fusion module and an improved non-maximum suppression (NMS) algorithm based on the YOLOv8 model. The multi-scale feature fusion module enhances the model’s detection ability for targets of different sizes by combining feature maps of different scales; the improved non-maximum suppression algorithm effectively reduces repeated detection and missed detection by optimizing the screening process of candidate boxes. Experimental results show that the improved YOLOv8 model exhibits excellent detection performance on the VisDrone2019 dataset, and outperforms other classic target detection models and the baseline YOLOv8 model in key indicators such as precision, recall, F1 score, and mean average precision (mAP). The manuscript needs improvement; therefore, I recommend minor revision. The detailed comments are given below. 1)The title of the paper is not impressive; it should be renamed. Additionally, for the experimental work, VisDrone 2019 dataset was used which means this paper is based on UAV technology. Therefore, authors should link this technology in the title for the reader's clear understanding. Response: Thank you for your comments, which have helped us further improve our work. We have made serious revisions based on your comments, modified the title of the article, and added drone content to make it more in line with the content of the article. 2)There is no numbering with each heading and the sub-sections of each heading are not easily understandable. For example, at the end of the related work, there are other methods and methods sessions that do not give proper understanding to the new readers. Furthermore, the initial letters of each word in the sub-session in related work start from small letters, while in the other headings, the first letter of each word in the sub-session starts from a capital letter. It is necessary to set the whole paper format. Response: Thank you for your comments, which have helped us to further improve our work. According to your comments, we have revised the title of the article as a whole to make it easier to understand. 3)How the proposed algorithms play an important role in other issues such as occlusion, low-quality images, multispectral images, and joint issues for target detection in traffic congestion using UAVs and AVs as mentioned in the following papers: •Target Detection and Recognition for Traffic Congestion in Smart Cities Using Deep Learning-Enabled UAVs: A Review and Analysis •Deep Learning-Based Pedestrian Detection in Autonomous Vehicles: Substantial Issues and Challenges • Advance generalization technique through 3D CNN to overcome the false positives pedestrian in autonomous vehicles Please justify the positive aspects of the proposed algorithm on other issues for target detection in traffic congestion and also include the mentioned references in it. Response: Thank you for your comments, which have helped us further improve our work. By comparing with relevant literature, we further elaborate on the active handling of occluded and masked objects by our model. For details, please see the red marked part in Section 2.3. 4)The author considers the VisDrone2019 dataset rather than the other datasets such as UAVDT, VisioDECT, and Synthetic Drone datasets. Why are the other datasets not considerable for this work? Please elaborate on it. Response: We understand your confusion about this issue. Compared with datasets such as UAVDT, VisioDECT, and Synthetic Drone, VisDrone2019 is a more challenging dataset. Its complex surrounding environment and the occlusion and cover between objects virtually increase the difficulty of object detection and also put higher requirements on the performance of the model. Therefore, we choose VisDrone2019 dataset as our first choice. 5)In the results and analysis section, the authors compare the results of the proposed model with other versions but the heading of comparison doesn’t make sense such as it is “Comparison results with other versions of yolov8”, it should be renamed such as “Comparison of the results with other versions of yolov8’ or “Comparing results with other versions of yolov8”. Response: Thank you for your comments, which have helped us improve our work. We have modified the title of the experimental results based on your comments. 6)In the introduction and experiment section, pages number 74, 390, 423, 425, and 428; the authors mentioned that we used two public datasets in our experimental work but this paper discusses only VisDrone dataset. What are the brief introduction and experimental results of the second public dataset? If this paper uses only one dataset on the entire paper, then it should be corrected on page number 74, 390, 423, 425, and 428. Response: Thank you for your comments, which have helped us improve our work. In this work, we only used one dataset to validate the model, and we have corrected the errors in the paper. 7)The VisDrone dataset covers different weather conditions (such as sunny, cloudy, rainy) and time periods (such as day, dusk, and night). The authors need to justify the model results on the weather conditions and time periods. Is the model performing best in the sunny, cloudy, rainy, and daytime or either at nighttime? In which condition is the result very effective? Response: Thank you for your comments, which have helped us improve our work. We have also conducted an additional experimental comparison between daytime and nighttime. The results show that the detection effect at night is somewhat reduced compared to that during the daytime due to external factors such as lighting. Attachments Attachment Submitted filename: Response to reviewers.docx https://doi.org/10.1371/journal.pone.0314817.r003
18 Nov 2024 Decision Letter - Khalil Abdelrazek Khalil, Editor Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system PONE-D-24-29268R1 Dear Dr. Dou, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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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 #2: All comments have been addressed ******** 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 #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 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 ****** 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 #2: 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: Authors revised the manuscript based on my comments and I am satisfied with the manuscript and its in publishable form Reviewer #2: Authors have addressed all my concerns in the revised version of the manuscript. It can be accepted for possible publication. ****** 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 #2: Yes: Muhammad Asim ******** https://doi.org/10.1371/journal.pone.0314817.r004
Formally Accepted
Acceptance Letter - Khalil Abdelrazek Khalil, Editor PONE-D-24-29268R1 PLOS ONE Dear Dr. Dou, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Khalil Abdelrazek Khalil Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0314817.r005

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