基于深度学习的精细化车型分类与速度检测研究OA

Traditional vehicle speed extraction methods suffer low accuracy.This paper employs object detection and tracking algorithms based on deep learning to realize real-time vehicle speed detection.According to national and industrial vehicle classification standards,the correlation of various classification methods was reviewed.With vehicle model image feature information,a refined vehicle classification system was developed.The YOLOv10 network framework was selected and the CA attention mechanism was introduced.Wise-IOU v3 was employed to optimize bounding box regression and add a P2 detection layer.CAW-YOLO vehicle detection model was proposed to enhance detection accuracy for similar vehicle models.Image distortion was corrected and a PTP speed detection model was developed by ByteTrack object tracking algorithm with perspective transformation.Results indicate that,CAW-YOLO model improves mAP0.5 by 3.8％compared to that of YOLOv10 and the relative error value for speed detection in the PTP model is under 9％.Four vehicle classification systems are assessed and more accurate speed data are obtained by a refined vehicle classification system.