Abstract
This research introduces a novel vehicle division mechanism intended for dynamic metropolitan settings. Modern technology, including deep learning, computer vision, and real-time warning systems, reduces traffic, enhances road safety, and effectively manages unanticipated situations. A strong vehicle instance segmentation model that can recognize cars in real-time from many sources is first developed using the YOLO technique. In the next round, the cars are tallied and a predetermined threshold is established. If there are more cars in the video or image than a preset threshold, the system will identify this and assume that traffic is heavy. During the last stage, users receive information on congestion analysis and anomaly identification through user-friendly interfaces. This article aims to modernize traffic control by merging cutting-edge technologies with all-encompassing techniques to deliver a responsive, safe, and efficient urban transportation experience. KEYWORDS: YOLOv7, DeepSORT, OpenCV, Object Detection, Multiclass Segmentation, Object Counting.
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