Traffic safety is a major global concern. Helmet usage is a key factor in
preventing head injuries and fatalities caused by motorcycle accidents.
However, helmet usage violations continue to be a significant problem. To
identify such violations, automatic helmet detection systems have been proposed
and implemented using computer vision techniques. Real-time implementation of
such systems is crucial for traffic surveillance and enforcement, however, most
of these systems are not real-time. This study proposes a robust real-time
helmet violation detection system. The proposed system utilizes a unique data
processing strategy, referred to as few-shot data sampling, to develop a robust
model with fewer annotations, and a single-stage object detection model, YOLOv8
(You Only Look Once Version 8), for detecting helmet violations in real-time
from video frames. Our proposed method won 7th place in the 2023 AI City
Challenge, Track 5, with an mAP score of 0.5861 on experimental validation
data. The experimental results demonstrate the effectiveness, efficiency, and
robustness of the proposed system.

本文提出了一种利用 YOLOv8 单阶段对象检测模型和 few-shot 数据采样策略开发的鲁棒的实时头盔违规检测系统，实验结果表明该系统有效、高效、稳健，可以用于交通监控和执法。