Car_Detection

This car detection project utilizes the YOLOv3 object detection model and OpenCV to identify and label cars in real-time from a video input. The YOLOv3 model, trained on the COCO dataset, processes each video frame by converting it into a blob and detecting objects with a confidence score above a set threshold. The detected objects are highlighted using bounding boxes and labeled with their class name (e.g., car) and confidence score, with unique colors assigned to each class. Non-Maximum Suppression (NMS) is applied to eliminate redundant boxes, ensuring accurate detection. The video is displayed with real-time detection, and the process can be terminated by pressing the Escape key.