Vehicle detection and monitoring are gaining impor- tance in traffic m anagement. H owever, d etection i s s till a n issue as vehicles vary in size, which directly affects vehicle counting accuracy. The proposed vehicle detection and counting method first extracts the road surface of the expressway in the image and divides it into distant regions. The newly developed segmentation strategy in the proposed vehicle identification a nd counting system first e xtracts T he t rail's s tate i n t he p icture furthermore separates it as far as the near areas. This method is important for improving vehicle detection. The above location is then sent to his YOLOv5m network to determine the vehicle type and location. Finally, we validate the proposed methodology using multiple traffic m onitoring r ecordings f rom d ifferent e nvironments. Also, the vehicle detection performance has increased to 99.39% of m ap compared to Yolov5 Basic. The research has practical consequences for the management and control of vehicle objects in traffic situations.
Research on Multi-Target Vehicle Detection and Tracking Based on Yolo
2023-07-21
1149032 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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