Automated Helmet Violation Detection and E-Challan Generation using Computer Vision and Optical Character Recognition

Abstract

Road safety has become a major concern due to the increasing number of traffic accidents caused by riders not wearing helmets. Traditional traffic monitoring systems mainly depend on manual supervision by traffic police, which is time-consuming, labour-intensive, and unable to provide continuous monitoring. As a result, many helmet violations remain undetected, reducing the effectiveness of traffic enforcement systems. This paper presents an intelligent Smart Surveillance System for Helmet Detection and Automatic Vehicle Fine Generation using Artificial Intelligence, Computer Vision, and Deep Learning techniques. The proposed system processes real-time video streams captured through surveillance cameras to detect two-wheelers and identify whether riders are wearing helmets using advanced object detection algorithms such as YOLO and Convolutional Neural Networks. When a helmet violation is detected, The system capturesss the vehicle number plate and applies Optical Character Recognition techniques to extract the registration number accurately. The extracted vehicle details are stored in a centralized database, and the system automatically generates fines based on violation history. First-time offenders receive a fine of along with a helmet incentive after payment, while repeated offenders receive a higher fine of ₹5000 without additional benefits. The proposed system ensures continuous monitoring, reduces human intervention, improves enforcement efficiency, and promotes road safety. Experimental analysis demonstrates that the system achieves high accuracy in helmet detection and performs efficiently in real-time traffic environments. The proposed framework provides a scalable and intelligent solution for smart city traffic management systems.