دورية أكاديمية
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: | Wei Jia, Shiquan Xu, Zhen Liang, Yang Zhao, Hai Min, Shujie Li, Ye Yu |
| المصدر: | IET Image Processing, Vol 15, Iss 14, Pp 3623-3637 (2021) |
| بيانات النشر: | Wiley, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Computer software |
| مصطلحات موضوعية: | Optical, image and video signal processing, Image recognition, Computer vision and image processing techniques, Video signal processing, Traffic engineering computing, Other topics in statistics, Photography, TR1-1050, Computer software, QA76.75-76.765 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-9667 1751-9659 |
| Relation: | https://doaj.org/toc/1751-9659; https://doaj.org/toc/1751-9667 |
| DOI: | 10.1049/ipr2.12295 |
| URL الوصول: | https://doaj.org/article/84583987ba25418294e7c6e6a7a3013d |
| رقم الأكسشن: | edsdoj.84583987ba25418294e7c6e6a7a3013d |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17519667 17519659 |
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| DOI: | 10.1049/ipr2.12295 |