Smart City Gnosys
Smart city article details
| Title | Pvswin-Yolov8S: Uav-Based Pedestrian And Vehicle Detection For Traffic Management In Smart Cities Using Improved Yolov8 |
|---|---|
| ID_Doc | 43782 |
| Authors | Tahir N.U.A.; Long Z.; Zhang Z.; Asim M.; ELAffendi M. |
| Year | 2024 |
| Published | Drones, 8, 3 |
| DOI | http://dx.doi.org/10.3390/drones8030084 |
| Abstract | In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions (for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset. © 2024 by the authors. |
| Author Keywords | CBAM; pedestrian and vehicle detection; soft-NMS; swin transformer; UAVs; YOLOv8 |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
23647  | 0.918 | Bhonde T.; Temare H.; Dadwhal Y.S. | Enhanced Object Detection Using Yolov8: Identifying Vehicles And Pedestrians In Urban Environments | 2024 IEEE Pune Section International Conference, PuneCon 2024 (2024) |
| 62128 | 0.911 | Sivaraman G.; Sophiya E.; Diviya M. | Yolov8 For Pedestrian Detection: A Comparative Study For Pedestrian Detection | 2024 3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024 (2024) |
| 43780 | 0.902 | Tahir N.U.A.; Zhang Z.; Asim M.; Iftikhar S.; A. Abd El-Latif A. | Pvdm-Yolov8L: A Solution For Reliable Pedestrian And Vehicle Detection In Autonomous Vehicles Under Adverse Weather Conditions | Multimedia Tools and Applications, 84, 23 (2025) |
| 33073 | 0.893 | Bakirci M. | Internet Of Things-Enabled Unmanned Aerial Vehicles For Real-Time Traffic Mobility Analysis In Smart Cities | Computers and Electrical Engineering, 123 (2025) |
| 50560 | 0.891 | Baiat Z.E.; Baydere S. | Smart City Traffic Monitoring:Yolov7 Transfer Learning Approach For Real-Time Vehicle Detection | 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023 (2023) |
| 54411 | 0.889 | Iftikhar S.; Asim M.; Zhang Z.; Muthanna A.; Chen J.; El-Affendi M.; Sedik A.; Abd El-Latif A.A. | Target Detection And Recognition For Traffic Congestion In Smart Cities Using Deep Learning-Enabled Uavs: A Review And Analysis | Applied Sciences (Switzerland), 13, 6 (2023) |
| 62125 | 0.889 | Song F.; Li P. | Yolov5-Ms: Real-Time Multi-Surveillance Pedestrian Target Detection Model For Smart Cities | Biomimetics, 8, 6 (2023) |
| 50361 | 0.886 | Wang C.; Song Z.; Shen X.; Miao S. | Smart City Management System Based On Unmanned Aerial Vehicle Real Time Target Detection | Lecture Notes in Electrical Engineering, 1416 LNEE (2025) |
| 44268 | 0.884 | Murthy C.B.; Hashmi M.F. | Real Time Pedestrian Detection Using Robust Enhanced Yolov3+ | Proceedings - 2020 21st International Arab Conference on Information Technology, ACIT 2020 (2020) |
| 36118 | 0.879 | Ouyang J.; Zeng L. | Magc-Yolo:Small Object Detection In Remote Sensing Images Based On Multi-Scale Attention And Graph Convolution | Proceedings of the International Joint Conference on Neural Networks (2024) |