DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module Optimization
This paper proposes an optimized algorithm based on YOLOv11s to address the problem of insufficient detection accuracy of vehicle targets from a drone perspective due to certain scenes involving complex backgrounds, dense vehicle targets, and/or large variations in vehicle target scales due to obliq...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3413 |
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| author | Qingqi Zhang Hao Wang Xinbo Wang Jiapeng Shang Xiaoli Wang Jie Li Yan Wang |
| author_facet | Qingqi Zhang Hao Wang Xinbo Wang Jiapeng Shang Xiaoli Wang Jie Li Yan Wang |
| author_sort | Qingqi Zhang |
| collection | DOAJ |
| description | This paper proposes an optimized algorithm based on YOLOv11s to address the problem of insufficient detection accuracy of vehicle targets from a drone perspective due to certain scenes involving complex backgrounds, dense vehicle targets, and/or large variations in vehicle target scales due to oblique imaging. The proposed algorithm enhances the model’s local feature extraction capability through a module collaboration optimization strategy, integrates coordinate convolution to strengthen spatial perception, and introduces a small object detection head to address target size variations caused by altitude changes. Additionally, we construct a dedicated dataset for urban vehicle detection that is characterized by high-resolution images, a large sample size, and low training resource requirements. Experimental results show that the proposed algorithm achieves gains of 1.9% in precision, 6.0% in recall, 4.2% in mAP@0.5, and 3.3% in mAP@0.5:0.95 compared to the baseline network. The improved model also achieves the highest F1-score, indicating an optimal balance between precision and recall. |
| format | Article |
| id | doaj-art-bfda5d98463942e39dfe22b2f1dee704 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-bfda5d98463942e39dfe22b2f1dee7042025-08-20T02:23:45ZengMDPI AGSensors1424-82202025-05-012511341310.3390/s25113413DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module OptimizationQingqi Zhang0Hao Wang1Xinbo Wang2Jiapeng Shang3Xiaoli 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, ChinaThis paper proposes an optimized algorithm based on YOLOv11s to address the problem of insufficient detection accuracy of vehicle targets from a drone perspective due to certain scenes involving complex backgrounds, dense vehicle targets, and/or large variations in vehicle target scales due to oblique imaging. The proposed algorithm enhances the model’s local feature extraction capability through a module collaboration optimization strategy, integrates coordinate convolution to strengthen spatial perception, and introduces a small object detection head to address target size variations caused by altitude changes. Additionally, we construct a dedicated dataset for urban vehicle detection that is characterized by high-resolution images, a large sample size, and low training resource requirements. Experimental results show that the proposed algorithm achieves gains of 1.9% in precision, 6.0% in recall, 4.2% in mAP@0.5, and 3.3% in mAP@0.5:0.95 compared to the baseline network. The improved model also achieves the highest F1-score, indicating an optimal balance between precision and recall.https://www.mdpi.com/1424-8220/25/11/3413deep learningUAVobject detectionYOLO |
| spellingShingle | Qingqi Zhang Hao Wang Xinbo Wang Jiapeng Shang Xiaoli Wang Jie Li Yan Wang DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module Optimization Sensors deep learning UAV object detection YOLO |
| title | DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module Optimization |
| title_full | DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module Optimization |
| title_fullStr | DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module Optimization |
| title_full_unstemmed | DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module Optimization |
| title_short | DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module Optimization |
| title_sort | dscw yolo vehicle detection from low altitude uav perspective via coordinate awareness and collaborative module optimization |
| topic | deep learning UAV object detection YOLO |
| url | https://www.mdpi.com/1424-8220/25/11/3413 |
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