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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| Published in: | IET image processing Vol. 15; no. 14; pp. 3623 - 3637 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Wiley
01.12.2021
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| ISSN: | 1751-9659, 1751-9667 |
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| 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. |
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| AbstractList | 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. 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. |
| Author | Liang, Zhen Jia, Wei Min, Hai Xu, Shiquan Zhao, Yang Yu, Ye Li, Shujie |
| Author_xml | – sequence: 1 givenname: Wei surname: Jia fullname: Jia, Wei organization: Hefei University of Technology – sequence: 2 givenname: Shiquan surname: Xu fullname: Xu, Shiquan organization: Hefei University of Technology – sequence: 3 givenname: Zhen surname: Liang fullname: Liang, Zhen organization: Hefei University of Technology – sequence: 4 givenname: Yang surname: Zhao fullname: Zhao, Yang organization: Hefei University of Technology – sequence: 5 givenname: Hai surname: Min fullname: Min, Hai organization: Hefei University of Technology – sequence: 6 givenname: Shujie surname: Li fullname: Li, Shujie organization: Hefei University of Technology – sequence: 7 givenname: Ye orcidid: 0000-0001-5628-6237 surname: Yu fullname: Yu, Ye email: yuye@hfut.edu.cn organization: Hefei University of Technology |
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Mat. Res – ident: e_1_2_9_50_1 doi: 10.1007/978-3-030-01234-2_1 – ident: e_1_2_9_61_1 – ident: e_1_2_9_26_1 doi: 10.1007/978-981-13-0680-8_11 – ident: e_1_2_9_45_1 |
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| Snippet | In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle... Abstract In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in... |
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| SubjectTerms | Computer vision and image processing techniques Image recognition Optical, image and video signal processing Other topics in statistics Traffic engineering computing Video signal processing |
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| Title | Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector |
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