Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle accidents is that motorcycle riders or passengers do not wear helmets. In this paper, an automatic helmet detection of motorcyclists method...

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Veröffentlicht in:IET image processing Jg. 15; H. 14; S. 3623 - 3637
Hauptverfasser: Jia, Wei, Xu, Shiquan, Liang, Zhen, Zhao, Yang, Min, Hai, Li, Shujie, Yu, Ye
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Wiley 01.12.2021
Schlagworte:
Computer vision and image processing techniques
Image recognition
Optical, image and video signal processing
Other topics in statistics
Traffic engineering computing
Video signal processing
ISSN:1751-9659, 1751-9667
Online-Zugang:Volltext
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Zusammenfassung:In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle accidents is that motorcycle riders or passengers do not wear helmets. In this paper, an automatic helmet detection of motorcyclists method based on deep learning is presented. The method consists of two steps. The first step uses the improved YOLOv5 detector to detect motorcycles (including motorcyclists) from video surveillance. The second step takes the motorcycles detected in the previous step as input and continues to use the improved YOLOv5 detector to detect whether the motorcyclists wear helmets. The improvement of the YOLOv5 detector includes the fusion of triplet attention and the use of soft‐NMS instead of NMS. A new motorcycle helmet dataset (HFUT‐MH) is being proposed, which is larger and more comprehensive than the existing dataset derived from multiple traffic monitoring in Chinese cities. Finally, the proposed method is verified by experiments and compared with other state‐of‐the‐art methods. Our method achieves mAP of 97.7%, F1‐score of 92.7% and frames per second (FPS) of 63, which outperforms other state‐of‐the‐art detection methods.
ISSN:1751-9659
1751-9667
DOI:10.1049/ipr2.12295