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Detection and tracking of safety helmet based on DeepSort and YOLOv5

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Abstract

Safety helmets can effectively prevent accidental head injuries to construction personnel. Helmet wearing detection is of great significance to the safety management of the construction site. The existing detection algorithms are difficult to detect small targets and dense targets, and the target occlusion and complex and changeable construction environment will reduce the detection accuracy. To solve the above problems, an intelligent helmet recognition system is designed, which combines multi-target tracking algorithm DeepSort and YOLOv5 detector. Firstly, the target bounding box is extracted by YOLOv5 algorithm and input into DeepSort framework. Further, the target trajectory prediction and tracking are realized by Kalman filter and Hungarian Algorithm. The actual test results on the complex construction sites show that the helmet recognition system based on YOLOv5 and DeepSort improves the detection speed and accuracy compared with the single detection algorithm and partial tracking algorithm. The average accuracy of the system is 94.5%, and the detection speed can reach 40fps, basically realizing real-time detection and providing an effective guarantee for the helmet-wearing detection task.

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References

  1. Benyang D, Xiaochun L, Miao Y (2020) Safety helmet detection method based on yolo v4. In: 2020 16th International conference on computational intelligence and security (CIS), pp 155–158. https://doi.org/10.1109/CIS52066.2020.00041

  2. Bertinetto L, Valmadre J, Henriques J, Vedaldi A, Torr P (2016) Fully-convolutional siamese networks for object tracking

  3. Bewley A, Ge Z, Ott L, Ramos F, Upcroft B (2016) Simple online and realtime tracking. In: 2016 IEEE International conference on image processing (ICIP), pp 3464–3468. https://doi.org/10.1109/ICIP.2017.8296962

  4. Bochkovskiy A, Wang C Y, Liao H (2020) Yolov4: Optimal speed and accuracy of object detection

  5. Chen S, Tang W, Ji T, Zhu H, Ouyang Y, Wang W (2020) Detection of safety helmet wearing based on improved faster r-cnn. In: 2020 International joint conference on neural networks (IJCNN), pp 1–7. https://doi.org/10.1109/IJCNN48605.2020.9207574

  6. Chiverton J (2012) Helmet presence classification with motorcycle detection and tracking. Intell Transp Syst 6:259–269. https://doi.org/10.1049/iet-its.2011.0138

    Article  Google Scholar 

  7. Dai B, Nie Y, Cui W, Liu R, Zheng Z (2020) Real-time safety helmet detection system based on improved ssd. In: Proceedings of the 2nd international conference on artificial intelligence and advanced manufacture. AIAM2020, pp 95–99. https://doi.org/10.1145/3421766.3421774

  8. Fang Q, Li H, Luo X, Ding L, Luo H, Rose T M, An W (2018) Detecting non-hardhat-use by a deep learning method from far-field surveillance videos. Autom Constr 85:1–9. https://doi.org/10.1016/j.autcon.2017.09.018

    Article  Google Scholar 

  9. Girshick R (2015) Fast r-cnn. In: 2015 IEEE International conference on computer vision (ICCV), pp 1440–1448. https://doi.org/10.1109/ICCV.2015.169

  10. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on computer vision and pattern recognition, pp 580–587. https://doi.org/10.1109/CVPR.2014.81

  11. Guo S, Li D, Wang Z, Zhou X (2020) Safety helmet detection method based on faster r-cnn, pp 423–434. https://doi.org/10.1007/978-981-15-8086-4_40

  12. Jamtsho Y, Riyamongkol P, Waranusast R (2021) Real-time license plate detection for non-helmeted motorcyclist using yolo. ICT Express 7 (1):104–109. https://doi.org/10.1016/j.icte.2020.07.008

    Article  Google Scholar 

  13. Li J, Liu H, Wang T, Jiang M, Wang S, Li K, Zhao X (2017) Safety helmet wearing detection based on image processing and machine learning. In: 2017 Ninth international conference on advanced computational intelligence (ICACI), pp 201–205. https://doi.org/10.1155/2020/9703560

  14. Li Y, Wei H, Han Z, Huang J, Wang W (2020) Deep learning-based safety helmet detection in engineering management based on convolutional neural networks. Adv Civil En 2020:1–10. https://doi.org/10.1155/2020/9703560

    Article  Google Scholar 

  15. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg A C (2016) Ssd: single shot multibox detector. In: Computer vision – ECCV 2016, pp 21–37

  16. Ming F, Teng-teng S, Zhen S (2019) Fast helmet-wearing-condition detection based on improved YOLOv2. Opt Precis Eng 27:1196–1205. https://doi.org/10.3788/OPE.20192705.1196

    Article  Google Scholar 

  17. Prajwal M, Tejas K, Varshad V, Madivalappa M, Shashidhar R (2019) A review on helmet detection by using image processing and convolutional neural networks. Int J Comput Applic 182:52–55. https://doi.org/10.5120/ijca2019918770

    Article  Google Scholar 

  18. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 779–788. https://doi.org/10.1109/CVPR.2016.91

  19. Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 6517–6525. https://doi.org/10.1109/CVPR.2017.690

  20. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement

  21. Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  22. Silva R, Aires K, Santos T, Abdala K, Veras R, Soares A (2013) Automatic detection of motorcyclists without helmet. In: 2013 XXXIX Latin american computing conference (CLEI), pp 1–7. https://doi.org/10.1109/CLEI.2013.6670613

  23. Silva R R V , Aires K R T, Veras R M S (2014) Helmet detection on motorcyclists using image descriptors and classifiers. In: 2014 27th SIBGRAPI Conference on graphics, patterns and images, pp 141–148. https://doi.org/10.1109/SIBGRAPI.2014.28

  24. Vignesh Raj AG, Manohar N, Dhyanjith G (2021) Helmet detection using single shot detector (ssd). In: 2021 Second international conference on electronics and sustainable communication systems (ICESC), pp 1241–1244. https://doi.org/10.1109/ICESC51422.2021.9532985

  25. Wang W, Gao S, Song R, Wang Z (2021) A safety helmet detection method based on the combination of ssd and hsv color space, 123–129. https://doi.org/10.1007/978-981-15-9354-3_12

  26. Wang X, Niu D, Luo P, Zhu C, Ding L, Huang K (2020) A safety helmet and protective clothing detection method based on improved-yolo v 3. In: 2020 Chinese automation congress (CAC), pp 5437–5441. https://doi.org/10.1109/CAC51589.2020.9327187

  27. Waranusast R, Bundon N, Timtong V, Tangnoi C, Pattanathaburt P (2013) Machine vision techniques for motorcycle safety helmet detection. In: 2013 28th International conference on image and vision computing New Zealand (IVCNZ 2013), pp 35–40. https://doi.org/10.1109/IVCNZ.2013.6726989

  28. Wojke N, Bewley A, Paulus D (2017) Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International conference on image processing (ICIP), pp 3645–3649. https://doi.org/10.1109/ICIP.2017.8296962

  29. Wu F, Jin G, Gao M, HE Z, Yang Y (2019) Helmet detection based on improved yolo v3 deep model. In: 2019 IEEE 16th International conference on networking, sensing and control (ICNSC), pp 363–368. https://doi.org/10.1109/ICNSC.2019.8743246

  30. Xu Y, Wang Z, Li Z, Yuan Y, Yu G (2020) Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines. Proceedings of the AAAI conference on artificial intelligence 34(07):12549–12556. https://doi.org/10.1609/aaai.v34i07.6944

    Article  Google Scholar 

  31. Zhou F, Zhao H, Nie Z (2021) Safety helmet detection based on YOLOv5. In: 2021 IEEE International conference on power electronics, computer applications (ICPECA), pp 6–11. https://doi.org/10.1109/ICPECA51329.2021.9362711

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Authors and Affiliations

  1. School of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, 266580, China

    Huajun Song, Xiuhui Zhang, Jie Song & Jianle Zhao

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  1. Huajun Song
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  2. Xiuhui Zhang
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Correspondence to Huajun Song.

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Xiuhui Zhang, Jie Song and Jianle Zhao contributed equally to this work.

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Song, H., Zhang, X., Song, J. et al. Detection and tracking of safety helmet based on DeepSort and YOLOv5. Multimed Tools Appl 82, 10781–10794 (2023). https://doi.org/10.1007/s11042-022-13305-0

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  • DOI: https://doi.org/10.1007/s11042-022-13305-0

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