Detection and Recognition of Helmet and Number Plates

Many people, particularly motorcycle riders, often fail to follow traffic regulations, with one of the most common violations being the failure to wear helmets. Ensuring compliance with helmet laws is crucial for road safety, but manually identifying and penalizing offenders is challenging due to the high volume of vehicles on the road. Traditional methods, such as manual monitoring by traffic police or reviewing CCTV footage, are inefficient and time-consuming. This project proposes an intelligent system that leverages image processing and machine learning techniques to automate helmet detection and number plate recognition. The system captures real-time images or video frames of motorcycle riders, processes them to identify helmet usage, and detects the vehicle’s license plate using Optical Character Recognition (OCR). When a violation is detected—where the rider is found without a helmet—the extracted license plate number is stored in a database and can be used for issuing fines or taking necessary actions. The proposed system enhances traffic law enforcement by minimizing manual intervention and increasing efficiency in identifying violations. It can be integrated with existing traffic surveillance infrastructure to provide real-time monitoring, ensuring improved compliance with helmet regulations. By automating the detection and reporting process, this system aims to reduce road accidents and fatalities caused by non-compliance with safety measures.