Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm
To investigate whether the skid resistance of the ramp meets the requirements of vehicle driving safety and stability, the simulation using the ideal driver model is inaccurate. Therefore, considering the driver’s driving habits, this paper proposes the use of Unmanned aerial vehicles (UAVs) for the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2788 |
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| author | Binshuang Zheng Jing Zhou Zhengqiang Hong Junyao Tang Xiaoming Huang |
| author_facet | Binshuang Zheng Jing Zhou Zhengqiang Hong Junyao Tang Xiaoming Huang |
| author_sort | Binshuang Zheng |
| collection | DOAJ |
| description | To investigate whether the skid resistance of the ramp meets the requirements of vehicle driving safety and stability, the simulation using the ideal driver model is inaccurate. Therefore, considering the driver’s driving habits, this paper proposes the use of Unmanned aerial vehicles (UAVs) for the collection and extraction of vehicle driving information. To process the collected UAV video, the Google Collaboration platform is used to modify and compile the “You Only Look Once” version 5 (YOLOv5) algorithm with Python 3.7.12, and YOLOv5 is retrained with the captured video. The results show that the precision rate P and recall rate R have satisfactory results with an F1 value of 0.86, reflecting a good P-R relationship. The loss function also stabilized at a very low level after 70 training epochs. Then, the trained YOLOv5 is used to replace the Faster R-CNN detector in the DeepSORT algorithm to improve the detection accuracy and speed and extract the vehicle driving information from the perspective of UAV. By coding, the coordinate information of the vehicle trajectory is extracted, the trajectory is smoothed, and the frame difference method is used to calculate the real-time speed information, which is convenient for the establishment of a real driver model. |
| format | Article |
| id | doaj-art-794859ea07294c9ab9c1a192ecc9090c |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-794859ea07294c9ab9c1a192ecc9090c2025-08-20T01:50:46ZengMDPI AGSensors1424-82202025-04-01259278810.3390/s25092788Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT AlgorithmBinshuang Zheng0Jing Zhou1Zhengqiang Hong2Junyao Tang3Xiaoming Huang4Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology, Zhejiang Scientific Research Institute of Transport, No. 705 Dalongjuwu Rd., Hangzhou 311305, ChinaSchool of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaChina State Construction Engineering (Hong Kong) Limited, Hong Kong 999077, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaTo investigate whether the skid resistance of the ramp meets the requirements of vehicle driving safety and stability, the simulation using the ideal driver model is inaccurate. Therefore, considering the driver’s driving habits, this paper proposes the use of Unmanned aerial vehicles (UAVs) for the collection and extraction of vehicle driving information. To process the collected UAV video, the Google Collaboration platform is used to modify and compile the “You Only Look Once” version 5 (YOLOv5) algorithm with Python 3.7.12, and YOLOv5 is retrained with the captured video. The results show that the precision rate P and recall rate R have satisfactory results with an F1 value of 0.86, reflecting a good P-R relationship. The loss function also stabilized at a very low level after 70 training epochs. Then, the trained YOLOv5 is used to replace the Faster R-CNN detector in the DeepSORT algorithm to improve the detection accuracy and speed and extract the vehicle driving information from the perspective of UAV. By coding, the coordinate information of the vehicle trajectory is extracted, the trajectory is smoothed, and the frame difference method is used to calculate the real-time speed information, which is convenient for the establishment of a real driver model.https://www.mdpi.com/1424-8220/25/9/2788rampUAV videovehicle recognitionYOLOv5 algorithmvehicle track information |
| spellingShingle | Binshuang Zheng Jing Zhou Zhengqiang Hong Junyao Tang Xiaoming Huang Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm Sensors ramp UAV video vehicle recognition YOLOv5 algorithm vehicle track information |
| title | Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm |
| title_full | Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm |
| title_fullStr | Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm |
| title_full_unstemmed | Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm |
| title_short | Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm |
| title_sort | vehicle recognition and driving information detection with uav video based on improved yolov5 deepsort algorithm |
| topic | ramp UAV video vehicle recognition YOLOv5 algorithm vehicle track information |
| url | https://www.mdpi.com/1424-8220/25/9/2788 |
| work_keys_str_mv | AT binshuangzheng vehiclerecognitionanddrivinginformationdetectionwithuavvideobasedonimprovedyolov5deepsortalgorithm AT jingzhou vehiclerecognitionanddrivinginformationdetectionwithuavvideobasedonimprovedyolov5deepsortalgorithm AT zhengqianghong vehiclerecognitionanddrivinginformationdetectionwithuavvideobasedonimprovedyolov5deepsortalgorithm AT junyaotang vehiclerecognitionanddrivinginformationdetectionwithuavvideobasedonimprovedyolov5deepsortalgorithm AT xiaominghuang vehiclerecognitionanddrivinginformationdetectionwithuavvideobasedonimprovedyolov5deepsortalgorithm |