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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| Vydáno v: | IET image processing Ročník 15; číslo 14; s. 3623 - 3637 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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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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| Cites_doi | 10.1016/j.aap.2019.105319 10.1007/s11042-020-08627-w 10.1109/IJCNN.2017.7966233 10.1109/ICCV.2017.324 10.1109/IJCNN.2018.8489629 10.1109/ICCV.2017.593 10.1109/WACV45572.2020.9093538 10.1109/IJCNN.2016.7727586 10.1109/ICOEI.2019.8862543 10.1109/TIP.2020.3002345 10.1109/ICSEC.2018.8712778 10.1109/ICCVW.2019.00246 10.1109/ICIRCA48905.2020.9183287 10.1007/s11042-017-4482-7 10.1007/978-3-030-58595-2_22 10.1109/TITS.2020.2997084 10.1109/ICCV.2019.00972 10.1109/TITS.2017.2749964 10.1109/CICT48419.2019.9066191 10.1109/CVPR.2019.00060 10.1109/ICOSEC49089.2020.9215415 10.1109/CVPR42600.2020.00978 10.1109/CLEI.2013.6670613 10.1109/CVPR.2019.00093 10.1609/aaai.v33i01.3301922 10.1109/ICCV.2017.322 10.1007/978-3-319-46448-0_2 10.1109/CVPR.2019.00091 10.1109/CVPR.2016.91 10.1007/s11042-016-3565-1 10.1109/ICOSEC49089.2020.9215266 10.1109/CVPRW50498.2020.00203 10.1109/WACV48630.2021.00318 10.1109/CVPR46437.2021.00841 10.1109/TIP.2019.2910667 10.1007/978-3-030-00563-4_6 10.1109/ACCESS.2020.3021357 10.1109/CVPR.2018.00644 10.1109/CVPR.2019.00754 10.1007/s11042-017-4975-4 10.1007/978-3-030-58555-6_16 10.1109/ICCV.2019.00975 10.1049/iet-its.2018.5241 10.22214/ijraset.2020.30426 10.1109/CVPR.2018.00813 10.1007/978-3-030-01234-2_1 10.1007/978-981-13-0680-8_11 |
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| References_xml | – volume: 8 start-page: 162073 year: 2020 end-page: 162084 article-title: Helmet use detection of tracked motorcycles using CNN‐based multi‐task learning publication-title: IEEE Access – volume: 77 start-page: 13557 issue: 11 year: 2018 end-page: 13570 article-title: Moving object detection via robust background modeling with recurring patterns voting publication-title: Multimedia Tools Appl – year: 2019 article-title: Grid r‐cnn – volume: 06 start-page: 5 issue: 12 year: 2019 article-title: Helmet detection using machine learning and automatic license plate recognition publication-title: International Research Journal of Engineering and Technology – volume: 28 start-page: 4376 issue: 9 year: 2019 end-page: 4386 article-title: Weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes publication-title: IEEE Trans. Image Process. – year: 2018 article-title: Automatic detector for bikers with no helmet using deep learning – start-page: 1 year: 2020 end-page: 16 article-title: Detection of motorcycles in urban traffic using video analysis: A review publication-title: IEEE Trans. Intell. Transport. Syst. – year: 2013 article-title: Automatic detection of motorcyclists without helmet – year: 2020 article-title: CSPNet: A new backbone that can enhance learning capability of CNN – year: 2020 article-title: Helmet and number plate detection of motorcyclists using deep learning and advanced machine vision techniques – year: 2020 article-title: Low cost, high performance automatic motorcycle helmet violation detection – year: 2019 article-title: RepPoints: Point set representation for object detection – volume: 29 start-page: 7389 year: 2020 end-page: 7398 article-title: FoveaBox: beyound anchor‐based object detection publication-title: IEEE Trans. Image Process. – year: 2021 – volume: 134 year: 2020 article-title: Detecting motorcycle helmet use with deep learning. Accident publication-title: Anal. Prev. – volume: 33 start-page: 922 year: 2019 end-page: 929 – volume: 19 start-page: 230 issue: 1 year: 2018 end-page: 241 article-title: Embedding structured contour and location prior in siamesed fully convolutional networks for road detection publication-title: IEEE Trans. Intell. Transport. Syst. – year: 2020 article-title: Real time automatic detection of motorcyclists with and without a safety helmet – year: 2016 – year: 2018 – volume: 76 start-page: 11003 issue: 8 year: 2017 end-page: 11019 article-title: Local‐to‐global background modeling for moving object detection from non‐static cameras publication-title: Multimedia Tools Appl – volume: 8 start-page: 1112 issue: 7 year: 2020 end-page: 1116 article-title: Real‐time helmet detection of motorcyclists without helmet using convolutional neural network'IJRASET publication-title: International Journal for Research in Applied Science and Engineering Technology – volume: 931–932 start-page: 588 year: 2014 end-page: 592 article-title: Motorcyclist's helmet wearing detection using image processing publication-title: Adv. Mat. Res – start-page: 10 – year: 2021 article-title: Rotate to attend: Convolutional triplet attention module – volume: 9905 start-page: 21 year: 2016 end-page: 37 – year: 2019 article-title: An efficient helmet detection for MVD using deep learning – year: 2017 article-title: Detection of motorcyclists without helmet in videos using convolutional neural network – year: 2018 article-title: Cascade R‐CNN: delving into high quality object detection – volume: 77 start-page: 5659 issue: 5 year: 2018 end-page: 5683 article-title: Detection of helmets on motorcyclists publication-title: Multimedia Tools Appl – year: 2020 – year: 2019 article-title: Feature selective anchor‐free module for single‐shot object detection – volume: 182 start-page: 50 issue: 37 year: 2019 end-page: 55 article-title: A hybrid approach for helmet detection for riders safety using Image processing publication-title: Mach. Learn. Artif. Intell. – start-page: 9 – year: 2016 article-title: Automatic detection of bike‐riders without helmet using surveillance videos in real‐time – volume: 79 start-page: 14179 issue: 19–20 year: 2020 end-page: 14199 article-title: Automated detection of helmet on motorcyclists from traffic surveillance videos: A comparative analysis using hand‐crafted features and CNN publication-title: Multimedia Tools Appl – year: 2018 article-title: A robust real‐time automatic license plate recognition based on the YOLO detector – year: 2017 – year: 2020 article-title: Machine learning based surveillance system for detection of bike riders without helmet and triple rides – year: 2019 article-title: Libra R‐CNN: towards balanced learning for object detection – year: 2020 article-title: Bridging the gap between anchor‐based and anchor‐free detection via adaptive training sample selection – year: 2019 – volume: 13 start-page: 1190 issue: 7 year: 2019 end-page: 1198 article-title: Deep learning‐based helmet wear analysis of a motorcycle rider for intelligent surveillance system publication-title: IET Intell. Transp. Syst. – article-title: Automated helmet detection for multiple motorcycle riders using CNN – ident: e_1_2_9_42_1 doi: 10.1016/j.aap.2019.105319 – ident: e_1_2_9_56_1 – ident: e_1_2_9_30_1 doi: 10.1007/s11042-020-08627-w – ident: e_1_2_9_3_1 – ident: e_1_2_9_31_1 doi: 10.1109/IJCNN.2017.7966233 – ident: e_1_2_9_9_1 doi: 10.1109/ICCV.2017.324 – ident: e_1_2_9_16_1 doi: 10.1109/IJCNN.2018.8489629 – ident: e_1_2_9_18_1 doi: 10.1109/ICCV.2017.593 – ident: e_1_2_9_47_1 – ident: e_1_2_9_38_1 doi: 10.1109/WACV45572.2020.9093538 – ident: e_1_2_9_21_1 doi: 10.1109/IJCNN.2016.7727586 – volume: 06 start-page: 5 issue: 12 year: 2019 ident: e_1_2_9_33_1 article-title: Helmet detection using machine learning and automatic license plate recognition publication-title: International Research Journal of Engineering and Technology – ident: e_1_2_9_37_1 doi: 10.1109/ICOEI.2019.8862543 – ident: e_1_2_9_58_1 doi: 10.1109/TIP.2020.3002345 – ident: e_1_2_9_2_1 – ident: e_1_2_9_43_1 doi: 10.1109/ICSEC.2018.8712778 – ident: e_1_2_9_35_1 – ident: e_1_2_9_48_1 doi: 10.1109/ICCVW.2019.00246 – ident: e_1_2_9_5_1 – ident: e_1_2_9_27_1 – ident: e_1_2_9_34_1 doi: 10.1109/ICIRCA48905.2020.9183287 – ident: e_1_2_9_25_1 doi: 10.1007/s11042-017-4482-7 – ident: e_1_2_9_57_1 doi: 10.1007/978-3-030-58595-2_22 – ident: e_1_2_9_19_1 doi: 10.1109/TITS.2020.2997084 – ident: e_1_2_9_11_1 doi: 10.1109/ICCV.2019.00972 – ident: e_1_2_9_14_1 doi: 10.1109/TITS.2017.2749964 – ident: e_1_2_9_40_1 doi: 10.1109/CICT48419.2019.9066191 – volume: 182 start-page: 50 issue: 37 year: 2019 ident: e_1_2_9_23_1 article-title: A hybrid approach for helmet detection for riders safety using Image processing publication-title: Mach. Learn. Artif. Intell. – ident: e_1_2_9_49_1 doi: 10.1109/CVPR.2019.00060 – ident: e_1_2_9_22_1 doi: 10.1109/ICOSEC49089.2020.9215415 – ident: e_1_2_9_10_1 doi: 10.1109/CVPR42600.2020.00978 – ident: e_1_2_9_20_1 doi: 10.1109/CLEI.2013.6670613 – ident: e_1_2_9_59_1 doi: 10.1109/CVPR.2019.00093 – ident: e_1_2_9_15_1 doi: 10.1609/aaai.v33i01.3301922 – ident: e_1_2_9_6_1 doi: 10.1109/ICCV.2017.322 – ident: e_1_2_9_8_1 doi: 10.1007/978-3-319-46448-0_2 – ident: e_1_2_9_53_1 doi: 10.1109/CVPR.2019.00091 – ident: e_1_2_9_7_1 doi: 10.1109/CVPR.2016.91 – ident: e_1_2_9_28_1 doi: 10.1007/s11042-016-3565-1 – ident: e_1_2_9_36_1 doi: 10.1109/ICOSEC49089.2020.9215266 – ident: e_1_2_9_44_1 doi: 10.1109/CVPRW50498.2020.00203 – ident: e_1_2_9_51_1 doi: 10.1109/WACV48630.2021.00318 – ident: e_1_2_9_60_1 doi: 10.1109/CVPR46437.2021.00841 – ident: e_1_2_9_13_1 – ident: e_1_2_9_4_1 doi: 10.1109/TIP.2019.2910667 – ident: e_1_2_9_17_1 doi: 10.1007/978-3-030-00563-4_6 – ident: e_1_2_9_41_1 doi: 10.1109/ACCESS.2020.3021357 – ident: e_1_2_9_52_1 doi: 10.1109/CVPR.2018.00644 – ident: e_1_2_9_54_1 doi: 10.1109/CVPR.2019.00754 – ident: e_1_2_9_29_1 doi: 10.1007/s11042-017-4975-4 – ident: e_1_2_9_55_1 doi: 10.1007/978-3-030-58555-6_16 – ident: e_1_2_9_12_1 doi: 10.1109/ICCV.2019.00975 – ident: e_1_2_9_32_1 doi: 10.1049/iet-its.2018.5241 – ident: e_1_2_9_39_1 doi: 10.22214/ijraset.2020.30426 – ident: e_1_2_9_46_1 doi: 10.1109/CVPR.2018.00813 – volume: 931 start-page: 588 year: 2014 ident: e_1_2_9_24_1 article-title: Motorcyclist's helmet wearing detection using image processing publication-title: Adv. 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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