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

Abstract 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 motorcyclist...

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Autores principales:	Wei Jia, Shiquan Xu, Zhen Liang, Yang Zhao, Hai Min, Shujie Li, Ye Yu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Photography TR1-1050 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/84583987ba25418294e7c6e6a7a3013d
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spelling	oai:doaj.org-article:84583987ba25418294e7c6e6a7a3013d2021-11-29T03:38:16ZReal‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector1751-96671751-965910.1049/ipr2.12295https://doaj.org/article/84583987ba25418294e7c6e6a7a3013d2021-12-01T00:00:00Zhttps://doi.org/10.1049/ipr2.12295https://doaj.org/toc/1751-9659https://doaj.org/toc/1751-9667Abstract 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.Wei JiaShiquan XuZhen LiangYang ZhaoHai MinShujie LiYe YuWileyarticlePhotographyTR1-1050Computer softwareQA76.75-76.765ENIET Image Processing, Vol 15, Iss 14, Pp 3623-3637 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photography TR1-1050 Computer software QA76.75-76.765
spellingShingle	Photography TR1-1050 Computer software QA76.75-76.765 Wei Jia Shiquan Xu Zhen Liang Yang Zhao Hai Min Shujie Li Ye Yu Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector
description	Abstract 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.
format	article
author	Wei Jia Shiquan Xu Zhen Liang Yang Zhao Hai Min Shujie Li Ye Yu
author_facet	Wei Jia Shiquan Xu Zhen Liang Yang Zhao Hai Min Shujie Li Ye Yu
author_sort	Wei Jia
title	Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector
title_short	Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector
title_full	Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector
title_fullStr	Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector
title_full_unstemmed	Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector
title_sort	real‐time automatic helmet detection of motorcyclists in urban traffic using improved yolov5 detector
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/84583987ba25418294e7c6e6a7a3013d
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Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

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