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Computer Science

Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study

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Article: 2333209 | Received 18 May 2023, Accepted 15 Mar 2024, Published online: 12 Apr 2024

References

  • Adarsh, P., Rathi, P., & Kumar, M. (2020, March). YOLO v3-Tiny: Object detection and recognition using one stage improved model [Paper presentation]. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 687–694). IEEE. https://doi.org/10.1109/ICACCS48705.2020.9074315
  • Akbarzadeh, M., Zhu, Z., & Hammad, A. (2020) Nested network for detecting PPE on large construction sites based on frame segmentation [Paper presentation]. Creative Construction e-Conference 2020 (pp. 33–38). Budapest University of Technology and Economics.
  • Alateeq, M. M., Pp, F. R., & Ali, M. A. (2023). Construction site hazards identification using deep learning and computer vision. Sustainability, 15(3), 2358. https://doi.org/10.3390/su15032358
  • Avanzato, R., Beritelli, F., Russo, M., Russo, S., & Vaccaro, M. (2020). Yolov3-based mask and face recognition algorithm for individual protection applications. CEUR Workshop Proceedings. Volume 2768 (pp. 41–45). https://www.scopus.com/record/display.uri?eid=2-s2.0-85097903936&origin=inward&txGid=79805e76b928adf2df3aa196caa0bcd1
  • Barro-Torres, S., Fernández-Caramés, T. M., Pérez-Iglesias, H. J., & Escudero, C. J. (2012). Real-time personal protective equipment monitoring system. Computer Communications, 36(1), 42–50. https://doi.org/10.1016/j.comcom.2012.01.005
  • Bo, Y., Huan, Q., Huan, X., Rong, Z., Hongbin, L., Kebin, M., … Lei, Z. (2019, October). Helmet detection under the power construction scene based on image analysis [Paper presentation]. 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT) (pp. 67–71). IEEE. https://doi.org/10.1109/ICCSNT47585.2019.8962495
  • Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. https://doi.org/10.48550/arXiv.2004.10934
  • Cai, L., & Qian, J. (2011). A method for detecting miners based on helmets detection in underground coal mine videos. Mining Science and Technology (China), 21(4), 553–556. https://doi.org/10.1016/j.mstc.2011.06.016
  • Dai, J., Li, Y., He, K., & Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. Advances in neural information processing systems (p. 29). https://doi.org/10.48550/arXiv.1605.06409
  • Dalal, N., & Triggs, B. (2005, June). Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (Vol. 1, pp. 886–893). IEEE. DOI: 10.1109/CVPR.2005.177
  • Ding, L., Fang, W., Luo, H., Love, P. E., Zhong, B., & Ouyang, X. (2018). A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory. Automation in Construction, 86, 118–124. https://doi.org/10.1016/j.autcon.2017.11.002
  • Dong, S., He, Q., Li, H., & Yin, Q. (2015). Automated PPE misuse identification and assessment for safety performance enhancement [Paper presentation]. ICCREM 2015 (pp. 204–214). https://doi.org/10.1061/9780784479377.024
  • Fan, Z., Peng, C., Dai, L., Cao, F., Qi, J., & Hua, W. (2020). A deep learning-based ensemble method for helmet-wearing detection. PeerJ. Computer Science, 6, e311. https://doi.org/10.7717/peerj-cs.311
  • 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. Automation in Construction, 85, 1–9. https://doi.org/10.1016/j.autcon.2017.09.018
  • Fang, W., Ding, L., Luo, H., & Love, P. E. (2018). Falls from heights: A computer vision-based approach for safety harness detection. Automation in Construction, 91, 53–61. https://doi.org/10.1016/j.autcon.2018.02.018
  • Ferdous, M., & Ahsan, S. M. M. (2022). PPE detector: A YOLO-based architecture to detect personal protective equipment (PPE) for construction sites. PeerJ Computer Science, 8, e999. https://doi.org/10.7717/peerj-cs.999
  • Gallo, G., Di Rienzo, F., Garzelli, F., Ducange, P., & Vallati, C. (2022). A smart system for personal protective equipment detection in industrial environments based on deep learning at the edge. IEEE Access 10, 110862–110878. https://doi.org/10.1109/ACCESS.2022.3215148
  • Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision (pp. 1440–1448). IEEE.
  • Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587). IEEE.
  • Han, G., Zhu, M., Zhao, X., & Gao, H. (2021). Method based on the cross-layer attention mechanism and multiscale perception for safety helmet-wearing detection. Computers and Electrical Engineering, 95, 107458. https://doi.org/10.1016/j.compeleceng.2021.107458
  • He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision (pp. 2961–2969). IEEE.
  • https://kz.kursiv.media/2022-01-29/v-kazakhstane-rabochie-kalechatsya-na-proizvodstve-v-tri-raza-chasche-chem/.
  • Huang, L., Fu, Q., He, M., Jiang, D., & Hao, Z. (2021). Detection algorithm of safety helmet wearing based on deep learning. Concurrency and Computation: Practice and Experience, 33(13), e6234. https://doi.org/10.1002/cpe.6234
  • Iannizzotto, G., Bello, L. L., & Patti, G. (2021). Personal protection equipment detection system for embedded devices based on DNN and fuzzy logic. Expert Systems with Applications, 184, 115447. https://doi.org/10.1016/j.eswa.2021.115447
  • Ji, X., Gong, F., Yuan, X., & Wang, N. (2023). A high-performance framework for personal protective equipment detection on the offshore drilling platform. Complex & Intelligent Systems, 9(5), 5637–5652. https://doi.org/10.1007/s40747-023-01028-0
  • Jocher, G., Stoken, A., Borovec, J., Changyu, L., Hogan, A., Chaurasia, A., Abhiram, V., Hogan, A., Hajek, J., Diaconu, L., Kwon, Y., Defretin, Y., Lohia, A., Milanko, B., Fineran, B., Khromov, D., Yiwei, D., Ingham, F. (2021). ultralytics/yolov5: v4. 0-nn. SiLU activations, Weights & Biases logging, PyTorch Hub integration. Zenodo.
  • Kelm, A., Laußat, L., Meins-Becker, A., Platz, D., Khazaee, M. J., Costin, A. M., Helmus, M., & Teizer, J. (2013). Mobile passive Radio Frequency Identification (RFID) portal for automated and rapid control of Personal Protective Equipment (PPE) on construction sites. Automation in Construction, 36, 38–52. https://doi.org/10.1016/j.autcon.2013.08.009
  • Kumar, S., Gupta, H., Yadav, D., Ansari, I. A., & Verma, O. P. (2022). YOLOv4 algorithm for the real-time detection of fire and personal protective equipments at construction sites. Multimedia Tools and Applications, 81(16), 22163–22183. https://doi.org/10.1007/s11042-021-11280-6
  • Kwak, N., & Kim, D. (2023). Detection of worker’s safety helmet and mask and identification of worker using deeplearning. Computers, Materials & Continua, 75(1), 1671–1686. https://doi.org/10.32604/cmc.2023.035762
  • Li, J., Liu, H., Wang, T., Jiang, M., Wang, S., Li, K., & Zhao, X. (2017, February). Safety helmet wearing detection based on image processing and machine learning [Paper presentation]. 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) (pp. 201–205). IEEE. https://doi.org/10.1109/ICACI.2017.7974509
  • Li, X., He, M., Liu, Y., Luo, H., & Ju, M. (2023). SPCS: A spatial pyramid convolutional shuffle module for YOLO to detect occluded object. Complex & Intelligent Systems, 9(1), 301–315. https://doi.org/10.1007/s40747-022-00786-7
  • Lienhart, R., & Maydt, J. (2002, September). An extended set of haar-like features for rapid object detection. Proceedings International Conference on Image Processing (Vol. 1, pp. I–I). IEEE.
  • Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector [Paper presentation]. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, the Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21–37). Springer International Publishing.
  • Liu, Y., & Zheng, Y. F. (2005, July). One-against-all multi-class SVM classification using reliability measures. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005 (Vol. 2, pp. 849–854.). IEEE.
  • Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021). Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. Sustainable Cities and Society, 65, 102600. https://doi.org/10.1016/j.scs.2020.102600
  • Long, X., Cui, W., & Zheng, Z. (2019, March). Safety helmet wearing detection based on deep learning [Paper presentation]. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (pp. 2495–2499). IEEE. https://doi.org/10.1109/ITNEC.2019.8729039
  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  • Ma, L., Li, X., Dai, X., Guan, Z., & Lu, Y. (2022). A combined detection algorithm for personal protective equipment based on lightweight YOLOv4 model. Wireless Communications and Mobile Computing, 2022, 1–11. https://doi.org/10.1155/2022/3574588
  • Márquez-Sánchez, S., Campero-Jurado, I., Herrera-Santos, J., Rodríguez, S., & Corchado, J. M. (2021). Intelligent platform based on smart PPE for safety in workplaces. Sensors, 21(14), 4652. https://doi.org/10.3390/s21144652
  • Nath, N. D., Behzadan, A. H., & Paal, S. G. (2020). Deep learning for site safety: Real-time detection of personal protective equipment. Automation in Construction, 112, 103085. https://doi.org/10.1016/j.autcon.2020.103085
  • Otgonbold, M. E., Gochoo, M., Alnajjar, F., Ali, L., Tan, T. H., Hsieh, J. W., & Chen, P. Y. (2022). SHEL5K: An extended dataset and benchmarking for safety helmet detection. Sensors, 22(6), 2315. https://doi.org/10.3390/s22062315
  • Park, M. W., & Brilakis, I. (2012). Construction worker detection in video frames for initializing vision trackers. Automation in Construction, 28, 15–25. https://doi.org/10.1016/j.autcon.2012.06.001
  • Park, M. W., Elsafty, N., & Zhu, Z. (2015). Hardhat-wearing detection for enhancing on-site safety of construction workers. Journal of Construction Engineering and Management, 141(9), 04015024. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000974
  • Park, S., Yoon, S., & Heo, J. (2019). Image-based automatic detection of construction helmets using R-FCN and transfer learning. KSCE Journal of Civil and Environmental Engineering Research, 39(3), 399–407. https://doi.org/10.12652/Ksce.2019.39.3.0399
  • Pradana, R. D. W., Adhitya, R. Y., Syai’in, M., Sudibyo, R. M., Abiyoga, D. R. A., Jami’in, M. A., Rochiem, N. H. (2019, October). MIdentification system of personal protective equipment using Convolutional Neural Network (CNN) method [Paper presentation]. 2019 International Symposium on Electronics and Smart Devices (ISESD) (pp. 1–6). IEEE. https://doi.org/10.1109/ISESD.2019.8909629
  • Protik, A. A., Rafi, A. H., & Siddique, S. (2021, August). Real-time personal protective equipment (PPE) detection using YOLOv4 and TensorFlow [Paper presentation]. 2021 IEEE Region 10 Symposium (TENSYMP) (pp. 1–6). IEEE. https://doi.org/10.1109/TENSYMP52854.2021.9550808
  • Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7263–7271). IEEE. DOI: 10.1109/CVPR.2017.690
  • Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
  • Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779–788). IEEE.
  • Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems (p. 28). https://doi.org/10.48550/arXiv.1506.01497
  • Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards realtime object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031.
  • Rubaiyat, A. H., Toma, T. T., Kalantari-Khandani, M., Rahman, S. A., Chen, L., Ye, Y., & Pan, C. S. (2016, October). Automatic detection of helmet uses for construction safety [Paper presentation]. 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW) (pp. 135–142). IEEE. https://doi.org/10.1109/WIW.2016.045
  • Seo, J., Han, S., Lee, S., & Kim, H. (2015). Computer vision techniques for construction safety and health monitoring. Advanced Engineering Informatics, 29(2), 239–251. https://doi.org/10.1016/j.aei.2015.02.001
  • Shahin, M., Chen, F. F., Hosseinzadeh, A., Khodadadi Koodiani, H., Bouzary, H., & Shahin, A. (2023). Enhanced safety implementation in 5S+ 1 via object detection algorithms. The International Journal of Advanced Manufacturing Technology, 125(7–8), 3701–3721. https://doi.org/10.1007/s00170-023-10970-9
  • Shen, J., Xiong, X., Li, Y., He, W., Li, P., & Zheng, X. (2021). Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning. Computer-Aided Civil and Infrastructure Engineering, 36(2), 180–196. https://doi.org/10.1111/mice.12579
  • Stojanovic, V., He, S., & Zhang, B. (2020). State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises. International Journal of Robust and Nonlinear Control, 30(16), 6683–6700. https://doi.org/10.1002/rnc.5131
  • Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. https://doi.org/10.48550/arXiv.2207.02696
  • Wang, C. Y., Yeh, I. H., & Liao, H. Y. M. (2021). You only learn one representation: Unified network for multiple tasks. https://doi.org/10.48550/arXiv.2105.04206
  • Wang, H., Hu, Z., Guo, Y., Yang, Z., Zhou, F., & Xu, P. (2020). A real-time safety helmet wearing detection approach based on CSYOLOv3. Applied Sciences, 10(19), 6732. https://doi.org/10.3390/app10196732
  • Wang, Z., Wu, Y., Yang, L., Thirunavukarasu, A., Evison, C., & Zhao, Y. (2021). Fast personal protective equipment detection for real construction sites using deep learning approaches. Sensors, 21(10), 3478. https://doi.org/10.3390/s21103478
  • Wu, H., & Zhao, J. (2018). Automated visual helmet identification based on deep convolutional neural networks. Computer aided chemical engineering (Vol. 44, pp. 2299–2304). Elsevier.
  • Wu, J., Cai, N., Chen, W., Wang, H., & Wang, G. (2019). Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset. Automation in Construction, 106, 102894. https://doi.org/10.1016/j.autcon.2019.102894
  • Xie, Z., Liu, H., Li, Z., & He, Y. (2018, December). A convolutional neural network based approach towards real-time hard hat detection [Paper presentation]. 2018 IEEE International Conference on Progress in Informatics and Computing (PIC) (pp. 430–434). IEEE. https://doi.org/10.1109/PIC.2018.8706269
  • Xiong, R., & Tang, P. (2021). Pose guided anchoring for detecting proper use of personal protective equipment. Automation in Construction, 130, 103828. https://doi.org/10.1016/j.autcon.2021.103828
  • Zhang, S., Teizer, J., Pradhananga, N., & Eastman, C. M. (2015). Workforce location tracking to model, visualize and analyze workspace requirements in building information models for construction safety planning. Automation in Construction, 60, 74–86. https://doi.org/10.1016/j.autcon.2015.09.009
  • Zhang, X., Gao, Y., Wang, H., & Wang, Q. (2020). Improve YOLOv3 using dilated spatial pyramid module for multi-scale object detection. International Journal of Advanced Robotic Systems, 17(4), 172988142093606. 1729881420936062. https://doi.org/10.1177/1729881420936062
  • Zhu, Z., Park, M. W., & Elsafty, N. (2015 Automated monitoring of hardhats wearing for onsite safety enhancement [Paper presentation]. Proceedings of the 11th Construction Specialty Conference, Vancouver, UK, 8–10 June 2015 (pp. 1–9).