Helmet Detection in Two Wheeler Using Image Processing

Road safety is a significant concern worldwide, with a high number of fatalities occurring due to the negligence of helmet usage by two-wheeler riders. According to statistics, thousands of lives are lost annually due to non-compliance with helmet regulations. This paper proposes an automated helmet detection system using image processing techniques to enforce safety measures. The system captures real-time images of the rider using a camera and processes them with a pretrained machine learning model on a Raspberry Pi. If a helmet is detected, the vehicle's ignition is enabled; otherwise, it remains disabled. This method ensures compliance with safety regulations and contributes to reducing road accidents. The model achieves 95% accuracy in helmet detection, making it a reliable and practical solution for real-world deployment. Experimental results validate the effectiveness of the proposed system, highlighting its potential in improving road safety and reducing fatalities.