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Abstract In India, helmets symbolize safety and civic responsibility, bearing cultural significance. However, a 22% increase in accidents and a 17.5% rise in fatalities in 2022-23 underscore the critical importance of helmet compliance beyond legal mandates. Non-compliance not only elevates the risk of injuries and fatalities but also entails legal consequences. Notably, 47,000 Indians died in 2021 due to not wearing helmets, emphasizing the pivotal role of helmet usage in road safety. This research focuses on improving motorcycle helmet detection to ensure compliance and reduce the risk of fatal head injuries for riders, extending its impact beyond geographical limits. While our dataset predominantly draws from Sivasagar, a district in Assam, India, the scope of our research is universally applicable. We employed a comprehensive methodology, comprising data collection, preprocessing, and YOLOv5 model training using the Darknet framework, testing, and evaluation. Analysis of the original YOLOv5 algorithm's performance using Precision-Recall (PR) curves resulted in mAP values of 85.9% for helmets, 88.1% for human heads, and an average of 87%. Subsequently, the proposed YOLOv5 algorithm, achieving mAP values of 93% for helmets, 96.8% for human heads, and a remarkable 94.9% average mAP, demonstrated significant improvements. Comparison revealed a consistent 7–8.5% higher mAP for helmet and human head detection, underscoring the efficacy of the proposed approach in improving detection capabilities. This research contributes to the broader field of computer vision and its practical applications, particularly in enhancing road safety and averting head injuries among riders, irrespective of their location.
Sarmah et al. (Thu,) studied this question.