UrbanEye: Deep Learning Enhanced Two-Wheeler Traffic Rule Violation Detection using YOLO and OCR

The deep learning enhanced two-wheeler traffic rule violation detection system takes advantage of computer vision, opencv, and deep learning techniques to automatically detect traffic violations involving two-wheelers. It identifies rule violations such as helmet-less riding, improper lane usage, triple riding, and license plate violations by processing images. The system utilizes object detection models like YOLO (you only look once) to detect vehicles, helmets, and license plates, while deep learning models are trained on large datasets to recognize patterns and predict traffic violations. Opencv-based image processing techniques further enhance visual data to improve detection accuracy. The system has the ability of reading number plates of the bikes with the help of optical character recognition (OCR). Immediate alerts along with the reports will help law enforcement agencies to act and take action promptly which will minimize the need of manual monitoring. Designed for seamless integration with existing traffic management infrastructures, this system enhances traffic rule enforcement, promoting road safety and reducing accidents. By automating violation detection, it offers a scalable solution for urban environments, minimizing human intervention and improving overall traffic management efficiency.