This project involves the development of an AI-powered system designed for real-time vehicle detection, license plate recognition, and tracking from video footage. It utilizes a combination of state-of-the-art computer vision techniques and deep learning models for accurate identification and extraction of vehicle and license plate information.

This project aims to develop a real-time license plate recognition system to detect stolen or wanted cars. Using a YOLOv8 model deployed on a Raspberry Pi with a camera, the system captures live video feeds and accurately recognizes license plates. It supports both Arabic and English characters, making it suitable for Saudi plates.

YOLOv8-based vehicle detection and license plate recognition project using PyTorch. Detects cars, trucks, and license plates from static images or live video streams with GPU acceleration (WSL2 + CUDA). Includes automatic bounding boxes, counting, and OCR (EasyOCR) for reading vehicle license plates.

License Plate Recognition with YOLOv8 and EasyOCR Detects cars and extracts license plate text from images using YOLOv8 and EasyOCR. Processes datasets in adverse weather, outputs annotated images, cropped plates, and a text file. Runs in Google Colab with GPU, using direct uploads. Ideal for computer vision and autonomous vehicle research.

Uses YOLO models (YOLOv5/YOLOv8) for object detection Detects vehicles (cars, trucks, buses) Includes license plate text recognition using EasyOCR Supports both video file processing and real-time webcam detection Has configurable settings and performance monitoring