Robust Motorcycle Helmet Detection in Real-World Scenarios: Using Co-DETR and Minority Class Enhancement

Hao Vo, Sieu Tran, Duc Minh Nguyen, Thua Nguyen, Tien Do, Duy-Dinh Le, Thanh Duc Ngo; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7163-7171

Abstract

Motorcycle helmet detection is a crucial task in intelligent traffic systems (ITS) as it enhances traffic safety consciousness and guides individuals towards legal compliance. Numerous challenges are tied to this problem particularly regarding data from the real world. In addition to requiring resilience to environmental fluctuations such as diverse camera angles and lighting conditions the solution must also address the problem of unbalanced data distribution across object classes. This study presents a system that utilizes Co-DETR to address the difficulties of dealing with changing perspectives on real-world data. Additionally we propose to use the Minority Optimizer and the Virtual Expander to enhance the accuracy of rare classes in imbalanced data. With a mean average precision (mAP) of 0.4860 our method achieved Rank 1 in the AI City Challenge 2024 Track 5 competition demonstrating its high effectiveness.

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[bibtex]
@InProceedings{Vo_2024_CVPR, author = {Vo, Hao and Tran, Sieu and Nguyen, Duc Minh and Nguyen, Thua and Do, Tien and Le, Duy-Dinh and Ngo, Thanh Duc}, title = {Robust Motorcycle Helmet Detection in Real-World Scenarios: Using Co-DETR and Minority Class Enhancement}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {7163-7171} }