The proper enforcement of motorcycle helmet regulations is crucial for
ensuring the safety of motorbike passengers and riders, as roadway cyclists and
passengers are not likely to abide by these regulations if no proper
enforcement systems are instituted. This paper presents the development and
evaluation of a real-time YOLOv5 Deep Learning (DL) model for detecting riders
and passengers on motorbikes, identifying whether the detected person is
wearing a helmet. We trained the model on 100 videos recorded at 10 fps, each
for 20 seconds. Our study demonstrated the applicability of DL models to
accurately detect helmet regulation violators even in challenging lighting and
weather conditions. We employed several data augmentation techniques in the
study to ensure the training data is diverse enough to help build a robust
model. The proposed model was tested on 100 test videos and produced an mAP
score of 0.5267, ranking 11th on the AI City Track 5 public leaderboard. The
use of deep learning techniques for image classification tasks, such as
identifying helmet-wearing riders, has enormous potential for improving road
safety. The study shows the potential of deep learning models for application
in smart cities and enforcing traffic regulations and can be deployed in
real-time for city-wide monitoring.

本研究基于 100 个视频，开发了实时的 YOLOv5 深度学习模型，用于检测骑摩托车的骑手和乘客是否佩戴头盔，从而提高路上安全和交通法规的执行力。