"Helmet Detection At Traffic Signals Using Convolutional Neural Networks: A Deep Learning Approach"

This paper presents an advanced approach for helmet detection at traffic signals using deep learning techniques, addressing the critical issue of motorcycle safety in urban environments. With the rising number of road accidents involving two-wheeled vehicles, effective monitoring of helmet usage is essential for enhancing compliance with safety regulations. We propose a convolutional neural network (CNN)-based model that analyses video feeds from traffic signals to detect and classify helmeted riders. Our methodology involves the collection and annotation of a diverse dataset that includes various riding scenarios, helmets, and lighting conditions to ensure robust model training. Leveraging transfer learning, we fine-tuned pre- trained models such as YOLOv5 and EfficientDet, achieving high accuracy in real-time detection. We also implemented data augmentation techniques to counteract potential overfitting and improve the model's generalization capabilities. The experimental results demonstrated that our helmet detection system achieves an impressive mean average precision (mAP) score, outperforming existing solutions in both speed and accuracy. Furthermore, we integrated this detection system into a prototype traffic monitoring framework, allowing for immediate feedback regarding helmet compliance at traffic signals. Our system not only enhances road safety by encouraging helmet use among riders but also contributes valuable data for traffic management authorities. By automating helmet detection, we facilitate proactive intervention strategies and support public safety initiatives. This work underscores the significance of applying deep learning in transportation safety measures and sets a foundation for further research in the realm of intelligent traffic systems. The implementation implications extend beyond just helmet monitoring, offering a scalable solution for various vehicular safety assessments that can potentially inform policy- making and enhance public health outcomes