Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector
| العنوان: | Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector |
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| المؤلفون: | Shujie Li, Wei Jia, Zhen Liang, Shiquan Xu, Hai Min, Yang Zhao, Yu Ye |
| المصدر: | IET Image Processing, Vol 15, Iss 14, Pp 3623-3637 (2021) |
| بيانات النشر: | Wiley, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | QA76.75-76.765, Computer science, Signal Processing, Real-time computing, Detector, Photography, Computer Vision and Pattern Recognition, Computer software, Electrical and Electronic Engineering, TR1-1050, Software |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 1751-9659 1751-9667 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d1636d87e42f104ea0d4064cd8cd786 https://doaj.org/article/84583987ba25418294e7c6e6a7a3013d |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....9d1636d87e42f104ea0d4064cd8cd786 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17519659 17519667 |
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