YOLO Based Multi-Objective Vehicle Detection and Tracking
Tchanchou Ngatouo Costel, JIANG XIN, WANG YU, ZHENG RUI · Research Square · 2022
Abstract Vehicle recognition and monitoring are gaining importance in traffic management. However, due to the various sizes of cars, detection remains a difficulty, which directly impacts the accuracy of vehicle counts. The suggested vehicle recognition and counting method first extracts the highway road surface in the image and divides it into a distant regions. A newly developed segmentation strategy in the proposed vehicle identification and counting system first extracts a nd s eparates t he highway road Turface in the image into a distant region and a proximal area; the method is crucial for improving vehicle detection. The aforementioned locations are then sent to the YOLOv5m network to determine the vehicle’s kind and position. Finally, the ORB algorithm is utilized to create vehicle trajectories, which may be used to estimate the driving direction of the vehicle and determine the number of distinct cars. Several traffic surveillance recordings from various settings are utilized to validate the suggested meUhodoMoHZ. 5he experimental findings demonstrate that the suggested segmentation approach may give greater detection accuracy, particularly for the detection of little automobile things. In addition, the vehicle detection performance was significantly improved by 99.39 % of m AP compared to the Yolov5 basic. This work has broad practical implications for managing and controlling vehicle objects in traffic scenes.