AI-Powered Helmet and Vehicle Number Plate Recognition System with Automatic Traffic Violation Detection and Chalan Generation

Abstract

Road safety violations such as riding without helmets and triple-seating on motorcycles are major causes of road accidents in developing countries. Manual checking by traffic officers is time-consuming and often inaccurate. To address this problem, this study presents an automated system that detects helmet violations, triple riding, and captures vehicle number plates using computer vision. The system applies the YOLOv8 object detection model for recognizing riders, helmets, and motorcycles in real time, along with Optical Character Recognition (OCR) for reading vehicle numbers. Once a violation is confirmed, an automatic e-challan is generated and sent to the registered vehicle owner through email. The system delivers high detection accuracy in different lighting and weather conditions while reducing manual effort and improving road safety enforcement. Road safety violations such as riding without helmets and triple-seating on motorcycles are major causes of road accidents in developing countries. Manual checking by traffic officers is time-consuming and often inaccurate. To address this problem, this study presents an automated system that detects helmet violations, triple riding, and captures vehicle number plates using computer vision. The system applies the YOLOv8 object detection model for recognizing riders, helmets, and motorcycles in real time, along with Optical Character Recognition (OCR) for reading vehicle numbers. Once a violation is confirmed, an automatic e-challan is generated and sent to the registered vehicle owner through email. The system delivers high detection accuracy in different lighting and weather conditions while reducing manual effort and improving road safety enforcement.