A Swin Transformer-Based Approach for Motorcycle Helmet Detection

Video surveillance-based automated detection of helmet use among motorcyclists has the potential to improve road safety by aiding in the implementation of enforcement initiatives. Despite that, the current detection approaches have many limitations. For instance, they are unable to detect multiple passengers or to function effectively in complex conditions. In this paper, we address the challenging problem of automated monitoring of helmet use using computer vision and machine learning. We propose a method based on deep neural network models known as transformers. We apply the base version of the Swin transformer as a backbone for feature extraction, and then combine a Feature Pyramid Network (FPN) neck with the Cascade Region-based Convolutional Neural Networks (RCNN) framework for final detection. The effectiveness of our proposed method is demonstrated through extensive experiments and is compared to existing approaches. Our method achieves a mean Average Precision (mAP) of 30.4, thus outperforming state-of-the-art detection methods.