Intelligent Transportation Safety Using AI-Based Helmet Detection

Abstract

Helmet detection is a crucial task in ensuring rider safety and enforcing traffic regulations. This project focuses on Automatic Helmet Detection using deep learning techniques to identify whether a person riding a two-wheeler is wearing a helmet. The system leverages Convolutional Neural Networks (CNNs), particularly the YOLOv8 object detection model, to achieve real-time and accurate detection. The model is trained on a dataset containing images of riders with and without helmets, allowing it to generalize well in real-world scenarios. The primary objective of this project is to enhance road safety by assisting traffic authorities in monitoring helmet compliance automatically. Traditional image processing techniques such as HOG and SVM have been used in the past, but deep learning methods like Fast R-CNN and YOLO offer superior performance in terms of speed and accuracy. Our literature survey highlights advancements in object detection, from Viola-Jones to YOLOv8, demonstrating the evolution of real-time detection methods. The proposed system can be integrated into traffic surveillance cameras to detect violations in real-time, reducing manual monitoring efforts. The expected outcome is a high-accuracy, real-time helmet detection system capable of operating efficiently in diverse lighting and environmental conditions. Future improvements could include multi-angle detection and integration with license plate recognition for automated fine generation. This project contributes to the field of computer vision and AI-driven traffic management, promoting safer road practices through intelligent automation.