Real Time Helmet Violation Detection Using Deep Learning

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology for addressing real-world societal challenges by enabling machines to perform tasks that traditionally require human intelligence. Deep learning has gained significant importance in computer vision, supporting automatic recognition, classification, and analysis of visual data. These capabilities have been effectively utilized in traffic surveillance systems to enhance monitoring, decision-making, and law enforcement for improved road safety. Existing research proposes an AI-based multitier framework with a lightweight classifier for detecting helmetless motorbike riders. This two-stage approach initially identifies riders from surveillance footage and subsequently classifies helmet usage, achieving reduced computational complexity and faster processing with acceptable accuracy. However, such approaches are limited in scope, focusing primarily on helmet detection and lacking robustness under challenging environmental conditions. To address these limitations, the proposed system employs the YOLOv12 algorithm for comprehensive and real-time traffic violation detection. The system extends its capabilities to identify multiple violations, including triple riding and mobile phone usage, while maintaining performance under diverse conditions such as low light and adverse weather. Additionally, it incorporates an automated alert mechanism to notify authorities and warn riders. This integrated approach enhances detection accuracy, enables real-time enforcement, and contributes to the development of safer and more intelligent traffic management systems.