Helmet Detection with Number Plate Extraction System Using AI And OCR

Road traffic accidents involving two-wheelers remain one of the leading causes of fatalities and serious injuries, particularly in developing countries. A major contributing factor to these accidents is the non-compliance with helmet usage laws. Although traffic regulations mandate helmet use, manual enforcement mechanisms suffer from limitations such as human error, restricted coverage, and high operational costs. To address these challenges, this thesis presents a fully automated Helmet Detection and Fine Generation System using YOLO (You Only Look Once) for object detection and Optical Character Recognition (OCR) for vehicle identification. The proposed system integrates deep learning–based computer vision, text recognition, database management, and automated notification mechanisms into a unified pipeline. The system captures video input from traffic surveillance cameras, detects two-wheeler riders, determines helmet compliance, extracts vehicle number plates using YOLO-based plate detection combined with PaddleOCR, and automatically generates fines for violators. Fine details are securely stored in an SQLite database, and automated email notifications are sent to registered vehicle owners.The system is designed for real-time or near-real-time operation, ensuring scalability, efficiency, and reliability. By reducing dependency on human intervention, the proposed solution enhances enforcement accuracy, promotes road safety, and aligns with smart city initiatives. This thesis details the system architecture, methodology, implementation, and technical specifications, demonstrating the effectiveness of combining YOLO and OCR for intelligent traffic law enforcement.