Next-Generation Helmet Detection: A Real-Time Approach with Cutting-Edge Vision Technique

Abstract

This research work addresses the critical need for improved road safety by developing a real-time motorcycle helmet detection system using advanced machine learning techniques. The system processes video feeds from surveillance cameras to detect helmet compliance and identify violators by detecting number plate information. Utilizing Convolutional Neural Networks (CNNs) like YOLO, the system ensures accurate detection. It is designed for real-time processing and scalability, seamlessly integrating with existing traffic monitoring infrastructures. Extensive testing on diverse datasets confirms the system’s high accuracy and reliability, marking a significant step toward enhancing road safety through automated helmet detection. Future improvements will focus on increasing availability and expanding applicability to other safety gear.