Enhancing Traffic Monitoring with a Python-Based Helmet and Triple Seat Detection System

Abstract

As traffic congestion escalates, the need for effective monitoring of motorcycle safety practices becomes increasingly urgent, especially in addressing the risks associated with triple seating and helmet non-compliance. This study presents an innovative detection framework that uses advanced deep learning techniques, specifically YOLOv8, to identify non-helmeted bike riders traveling with additional passengers. By utilizing real-time video analytics, our system effectively monitors traffic conditions, highlights critical patterns in violations, and provides valuable insights into rider behavior. The findings underscore the need for enhanced traffic enforcement mechanisms and advocate for targeted safety initiatives to reduce motorcycle-related accidents. This research contributes to the discourse on traffic safety and emphasizes the role of technology in promoting public health and safety on the roads.