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- Title
Monocular vehicle speed detection based on improved YOLOX and DeepSORT.
- Authors
Zhang, Kaiyu; Wu, Fei; Sun, Haojun; Cai, Meiyu
- Abstract
A monocular vehicle speed detection method based on improved YOLOX and DeepSORT is proposed for the simple scene of fixed shooting angle without high precision but requiring control cost. For continuous video frames collected from a monocular fixed perspective, the vehicle is first identified by using the YOLOX object detection network improved by ELAN module and the CAENet attention mechanism constructed by CA attention and ECANet. Then, the DeepSORT target tracking algorithm is used to match the recognition results of the object detection network output in the before and after frames to find the same target in different frames. Finally, a coordinate system transformation algorithm is used to convert the position distance of the target moving in different frame images into the actual ground plane distance and divide it by the detection interval time to obtain the vehicle speed. The experimental results show that our improved object detection model can increase mAP by 2% to 4% compared with YOLOX in different versions. Compared with the original model, the target tracking using the improved YOLOX is improved by 4.3% on MOTA. The speed limiting precision of speed detection is 75% in the corresponding speed range in experimental testing site 1 and the mean error of the effective velocity value measured by our speed measurement method is 2.10 km/h in experimental testing site 2, which is better than the mean error of 5.46 km/h obtained by the radar pistol velocimeter. This detection method enables economical and efficient vehicle speed detection in simple scenes.
- Subjects
OBJECT recognition (Computer vision); MONOCULARS; SPEED; TRACKING algorithms; TRACKING radar; COORDINATE transformations
- Publication
Neural Computing & Applications, 2024, Vol 36, Issue 17, p9643
- ISSN
0941-0643
- Publication type
Article
- DOI
10.1007/s00521-023-08963-6