Vehicle detection and classification for traffic management and autonomous systems using YOLOv10
With the continuous development of Intelligent Transportation Systems (ITS), real-time vehicle detection and classification have become critical tasks for urban traffic management and autonomous driving. However, existing detection methods face challenges such as small target detection, severe occlu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825007999 |
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| author | Anning Ji Xintao Ma |
| author_facet | Anning Ji Xintao Ma |
| author_sort | Anning Ji |
| collection | DOAJ |
| description | With the continuous development of Intelligent Transportation Systems (ITS), real-time vehicle detection and classification have become critical tasks for urban traffic management and autonomous driving. However, existing detection methods face challenges such as small target detection, severe occlusion, and changing traffic conditions. The main purpose of this study is to address these challenges and improve vehicle detection in ITS environments. We propose a novel detection framework, YDFNet, that integrates the YOLOv10 algorithm for fast feature extraction, the BiFPN (Bidirectional Feature Pyramid Network) for multi-scale feature fusion, and the DETR (Detection Transformer) architecture for global feature modeling. Our approach leverages the advantages of each method to enhance detection accuracy and efficiency, especially in complex traffic scenarios. Experimental results on the UA-DETRAC and COCO datasets demonstrate that YDFNet outperforms existing methods in terms of detection accuracy, small target detection, and inference speed. The novelty of our work lies in the effective combination of YOLOv10, BiFPN, and DETR, which improves the model’s robustness to dynamic environments while maintaining real-time performance. This research provides a new solution for efficient and accurate vehicle detection in ITS and lays the foundation for future work on multimodal data fusion and model optimization. |
| format | Article |
| id | doaj-art-2f1a8deaf37d41d18669b15d30214c8f |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-2f1a8deaf37d41d18669b15d30214c8f2025-08-22T04:55:36ZengElsevierAlexandria Engineering Journal1110-01682025-08-0112780481610.1016/j.aej.2025.06.049Vehicle detection and classification for traffic management and autonomous systems using YOLOv10Anning Ji0Xintao Ma1Department of traffic management, Jilin Police College, 130117, Changchun, ChinaSchool of Management Science and Information Engineering, JiLin University of Finance and Economics, 130000, Changchun, China; Corresponding author.With the continuous development of Intelligent Transportation Systems (ITS), real-time vehicle detection and classification have become critical tasks for urban traffic management and autonomous driving. However, existing detection methods face challenges such as small target detection, severe occlusion, and changing traffic conditions. The main purpose of this study is to address these challenges and improve vehicle detection in ITS environments. We propose a novel detection framework, YDFNet, that integrates the YOLOv10 algorithm for fast feature extraction, the BiFPN (Bidirectional Feature Pyramid Network) for multi-scale feature fusion, and the DETR (Detection Transformer) architecture for global feature modeling. Our approach leverages the advantages of each method to enhance detection accuracy and efficiency, especially in complex traffic scenarios. Experimental results on the UA-DETRAC and COCO datasets demonstrate that YDFNet outperforms existing methods in terms of detection accuracy, small target detection, and inference speed. The novelty of our work lies in the effective combination of YOLOv10, BiFPN, and DETR, which improves the model’s robustness to dynamic environments while maintaining real-time performance. This research provides a new solution for efficient and accurate vehicle detection in ITS and lays the foundation for future work on multimodal data fusion and model optimization.http://www.sciencedirect.com/science/article/pii/S1110016825007999Real-time vehicle detectionIntelligent transportation systemMulti-scale feature fusionYOLOv10 algorithmDetection transformer architectureBidirectional feature pyramid network |
| spellingShingle | Anning Ji Xintao Ma Vehicle detection and classification for traffic management and autonomous systems using YOLOv10 Alexandria Engineering Journal Real-time vehicle detection Intelligent transportation system Multi-scale feature fusion YOLOv10 algorithm Detection transformer architecture Bidirectional feature pyramid network |
| title | Vehicle detection and classification for traffic management and autonomous systems using YOLOv10 |
| title_full | Vehicle detection and classification for traffic management and autonomous systems using YOLOv10 |
| title_fullStr | Vehicle detection and classification for traffic management and autonomous systems using YOLOv10 |
| title_full_unstemmed | Vehicle detection and classification for traffic management and autonomous systems using YOLOv10 |
| title_short | Vehicle detection and classification for traffic management and autonomous systems using YOLOv10 |
| title_sort | vehicle detection and classification for traffic management and autonomous systems using yolov10 |
| topic | Real-time vehicle detection Intelligent transportation system Multi-scale feature fusion YOLOv10 algorithm Detection transformer architecture Bidirectional feature pyramid network |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825007999 |
| work_keys_str_mv | AT anningji vehicledetectionandclassificationfortrafficmanagementandautonomoussystemsusingyolov10 AT xintaoma vehicledetectionandclassificationfortrafficmanagementandautonomoussystemsusingyolov10 |