Car Detection System

About This Project

The Car Detection System is a Python-based computer vision project that uses a custom-trained YOLOv8 object detection model to identify cars across multiple input sources. Users can upload images, process video files, or run live detection through a webcam. The system uses OpenCV to draw bounding boxes, labels, and confidence scores in real time. The backend logic is handled through a simple Python interface (main.py), which allows easy switching between input modes and YOLO model files. The project also supports YOLOv5 fallback and GPU acceleration for optimized inference speed. This system is ideal for beginners learning object detection, as well as developers looking to quickly test datasets, validate trained models, or build traffic-related prototypes. This project showcases strong skills in AI/ML, deep learning inference, OpenCV image processing, and efficient Python automation, delivering a clear and practical demonstration of real-world computer vision capabilities.