RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing Images
Vehicle detection in remote sensing images faces significant challenges due to small object sizes, scale variation, and cluttered backgrounds. To address these issues, we propose RSW-YOLO, an enhanced detection model built upon the YOLOv8n framework, designed to improve feature extraction and robust...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4335 |
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| author | Hao Wang Jiapeng Shang Xinbo Wang Qingqi Zhang Xiaoli Wang Jie Li Yan Wang |
| author_facet | Hao Wang Jiapeng Shang Xinbo Wang Qingqi Zhang Xiaoli Wang Jie Li Yan Wang |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | Vehicle detection in remote sensing images faces significant challenges due to small object sizes, scale variation, and cluttered backgrounds. To address these issues, we propose RSW-YOLO, an enhanced detection model built upon the YOLOv8n framework, designed to improve feature extraction and robustness against environmental noise. A Restormer module is incorporated into the backbone to model long-range dependencies via self-attention, enabling better handling of multi-scale features and complex scenes. A dedicated detection head is introduced for small objects, focusing on critical channels while suppressing irrelevant information. Additionally, the original CIoU loss is replaced with WIoU, which dynamically reweights predicted boxes based on their quality, enhancing localization accuracy and stability. Experimental results on the DJCAR dataset show mAP@0.5 and mAP@0.5:0.95 improvements of 5.4% and 6.2%, respectively, and corresponding gains of 4.3% and 2.6% on the VisDrone dataset. These results demonstrate that RSW-YOLO offers a robust and accurate solution for UAV-based vehicle detection, particularly in urban scenes with dense or small targets. |
| format | Article |
| id | doaj-art-d0c56d3a431f4061a7b956c0c01b77fc |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d0c56d3a431f4061a7b956c0c01b77fc2025-08-20T03:32:33ZengMDPI AGSensors1424-82202025-07-012514433510.3390/s25144335RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing ImagesHao Wang0Jiapeng Shang1Xinbo Wang2Qingqi Zhang3Xiaoli Wang4Jie Li5Yan Wang6Electronic Information Engineering College, Changchun University, Changchun 130022, ChinaElectronic Information Engineering College, Changchun University, Changchun 130022, ChinaElectronic Information Engineering College, Changchun University, Changchun 130022, ChinaElectronic Information Engineering College, Changchun University, Changchun 130022, ChinaElectronic Information Engineering College, Changchun University, Changchun 130022, ChinaElectronic Information Engineering College, Changchun University, Changchun 130022, ChinaElectronic Information Engineering College, Changchun University, Changchun 130022, ChinaVehicle detection in remote sensing images faces significant challenges due to small object sizes, scale variation, and cluttered backgrounds. To address these issues, we propose RSW-YOLO, an enhanced detection model built upon the YOLOv8n framework, designed to improve feature extraction and robustness against environmental noise. A Restormer module is incorporated into the backbone to model long-range dependencies via self-attention, enabling better handling of multi-scale features and complex scenes. A dedicated detection head is introduced for small objects, focusing on critical channels while suppressing irrelevant information. Additionally, the original CIoU loss is replaced with WIoU, which dynamically reweights predicted boxes based on their quality, enhancing localization accuracy and stability. Experimental results on the DJCAR dataset show mAP@0.5 and mAP@0.5:0.95 improvements of 5.4% and 6.2%, respectively, and corresponding gains of 4.3% and 2.6% on the VisDrone dataset. These results demonstrate that RSW-YOLO offers a robust and accurate solution for UAV-based vehicle detection, particularly in urban scenes with dense or small targets.https://www.mdpi.com/1424-8220/25/14/4335deep learningremote sensing imagesobject detectionYOLO |
| spellingShingle | Hao Wang Jiapeng Shang Xinbo Wang Qingqi Zhang Xiaoli Wang Jie Li Yan Wang RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing Images Sensors deep learning remote sensing images object detection YOLO |
| title | RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing Images |
| title_full | RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing Images |
| title_fullStr | RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing Images |
| title_full_unstemmed | RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing Images |
| title_short | RSW-YOLO: A Vehicle Detection Model for Urban UAV Remote Sensing Images |
| title_sort | rsw yolo a vehicle detection model for urban uav remote sensing images |
| topic | deep learning remote sensing images object detection YOLO |
| url | https://www.mdpi.com/1424-8220/25/14/4335 |
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