Automated Monitoring of Motorcycle Helmet Usage for Road Safety

Abstract

Motorcycle safety is a major concern, with helmet usage being a critical factor in reducing severe injuries during accidents. Continuous monitoring of helmet compliance in hightraffic areas is challenging due to the limitations of manual observation. This paper presents an automated helmet detection and monitoring system that combines the YOLOv8 object detection model with BoT-SORT tracking to process CCTV footage in real time. Two detection models are employed: one for motorcycles and pedestrians within a Region of Interest (ROI), and another for helmets and non-helmeted heads within a counting zone. BoTSORT ensures consistent object identities across frames, reducing overcounting. The system integrates with Google Sheets and Drive APIs for automated logging and reporting of compliance data. Experimental evaluation demonstrates high precision and recall, with challenges primarily arising from lighting variations and object occlusion. Overall, the proposed system provides a reliable framework for large-scale helmet compliance monitoring and road safety enforcement.