Automated Monitoring of Motorcycle Helmet Usage for Road Safety
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105032747180
Journal Title
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025
Rights Holder(s)
SCOPUS
Bibliographic Citation
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025)
Suggested Citation
Rodthanong P., Ploypicha P., Pleangnoi S., Sivaraksa M. Automated Monitoring of Motorcycle Helmet Usage for Road Safety. 2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025). doi:10.1109/iSAI-NLP66160.2025.11320789 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115786
Title
Automated Monitoring of Motorcycle Helmet Usage for Road Safety
Author(s)
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
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.