The Traffic Violation Detection System using YoloV7

In recent time surveys, the deaths and injuries due to traffic violations have increased chiefly in Indian roads. So, this needed the assistance of an automated computer vision-based object detection model, as manually identifying the vehicles violating traffic is hectic. The principle of this paper is to detect multiple violations using single video frames. The input video stream obtained from the surveillance camera is processed and annotated to carry out multiple processes. The dataset used for red-light jumping is COCO and the dataset for over boarding is created by annotating the images obtained from google. The model is trained and the output is visualized using tensorboard. The parameters used are Precision, Recall, F-measure and P-measure. The accuracy for red light skipping is 93% and the mAP value for over boarding is 0.5:0.95. This system utilizes the video stream at its maximum to detect various violations.