Wearing Helmet is a critical safety measure not only for riders but also for passengers. However, people often tend to skip wearing these protective headgears, thereby leading to, increased risk of injury or death in the event of an accident. There is a growing necessity to develop innovative methods that automatically monitor and prevent unsafe driving. To address this issue, we have developed a computer vision-based helmet detection system that can detect if a rider has his helmet on in real-time.
We use state-of-the-art computer vision-based techniques for helmet detection. This paper covers various aspects of helmet detection, including image pre-processing, feature extraction, and classification. The system is evaluated on performance metrics such as accuracy, precision, and recall. Further enhancement of the system is proposed in the potential directions for future research. The results demonstrate that computer vision-based helmet detection systems hold significant potential to reduce the risk of accidents and improve safety for riders.
