Helmet compliance is an essential need yet the monitoring of helmet remains a challenge, due to which various accidents take place and people loss their precious lives due to various head injuries. This paper proposes a YOLO-v8 based biker helmet detection system to enforce helmet wearing by riders. The system uses the advanced YOLO-v8 system that is trained on a self-prepared & labelled data set consisting of images captured from various roads & angles, this architecture ensures prompt communication with traffic regulations by achieving an accurate and exact detection and generating a report. The model achieved a detection accuracy of 97% demonstrating its reliability and robustness. The system works by processing input videos provided via dataset and analyzes each frame to detect helmet violations and non-compliance. This provides the authorities and government formalize a plan for traffic solutions that helps in reduce accidents foster awareness. By addressing the in-efficiencies of traditional methods the system offers high accuracy, adaptability in various conditions like lowlight, weather, etc. making it suitable for smart cities and also supports integration with other systems like traffic management. This model can be optimized with edge devices, integration with vehicle detection to further enhance enforcement strategies. This strategy can be easily implemented into current traffic management systems because to its scalability and practicality, utilizing AI tools such as YOLOv8 to solve problems efficiently and improve road safety.
Khalil et al. (Sat,) studied this question.
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