The present study addresses the issue of non-compliance with helmet laws and
the potential danger to both motorcycle riders and passengers. Despite the
well-established advantages of helmet usage, compliance remains a formidable
challenge in many regions of the world, with various factors contributing to
the issue. To mitigate this concern, real-time monitoring and enforcement of
helmet laws have been advocated as a plausible solution. However, previous
attempts at real-time helmet violation detection have been limited by their
inability to operate in real-time. To remedy this issue, the current paper
proposes a real-time helmet violation detection system utilizing a single-stage
object detection model called YOLOv5. The model was trained on the 2023 NVIDIA
AI City Challenge Track 5 dataset and employed genetic algorithms in selecting
the optimal hyperparameters for training the model. Furthermore, data
augmentation techniques such as flip, and rotation were implemented to improve
model performance. The efficacy of the model was assessed using mean average
precision (mAP). Our developed model achieved an mAP score of 0.5377 on the
experimental test data which won 10th place on the public leaderboard. The
proposed approach represents a noteworthy breakthrough in the field and holds
the potential to significantly improve motorcycle safety.

本文提出了一种利用 YOLOv5 目标检测模型实现的实时头盔违规检测系统，该系统用于监督执行头盔法律，有助于提高摩托车安全性。