Motorcyclist Helmet Violation Detection Framework by Leveraging Robust Ensemble and Augmentation Methods

Thien Van Luong, Huu Si Phuc Nguyen, Duy Khanh Dinh, Viet Hung Duong, Duy Hong Sam Vo, Huan Vu, Minh Tuan Hoang, Tien Cuong Nguyen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7027-7036

Traffic Monitoring Systems play a crucial role in real-life scenarios by improving traffic flow and reducing violations. Among these violations helmet non-compliance is particularly common in countries where motorcycles are the primary mode of transportation. However deploying an automatic violation capturing for helmet non-compliance in the real world presents challenges due to diverse traffic situations environmental conditions varying object sizes and severely imbalanced datasets. In order to address these challenges we propose a novel deep learning framework for helmet violation detection which consists of four different modules namely object detection object association post-processing for tracking and score correction. In particular we develop a robust ensemble method to take advantage of various state-of-the-art object detection models such as YOLOv7 YOLOv8 Co-DETR and EfficientDet. Furthermore to address the issue of data imbalance we propose two copy and paste data augmentation techniques for enriching data samples of rare classes. As a result our approach yields a substantial 7.43% mAP enhancement over the baseline Co-DETR model achieving a final score of 0.4792 in the 2024 AI City Challenge Track 5 test set and ranking 3rd among the competing teams.