A Vision-Based Approach for Ensuring Proper Use of Personal Protective Equipment (PPE) in Decommissioning of Fukushima Daiichi Nuclear Power Station
Source abstract
Abstract
Decommissioning of the Fukushima Daiichi nuclear power station (NPS) is challenging due to industrial and chemical hazards as well as radiological ones. The decommissioning workers in these sites are instructed to wear proper Personal Protective Equipment (PPE) for radiation protection. However, workers may not be able to accurately comply with safety regulations at decommissioning sites, even with prior education and training. In response to the difficulties of on-site PPE management, this paper presents a vision-based automated monitoring approach to help to facilitate the occupational safety monitoring task of decommissioning workers to ensure proper use of PPE by the combination of deep learning-based individual detection and object detection using geometric relationships analysis. The performance of the proposed approach was experimentally evaluated, and the experimental results demonstrate that the proposed approach is capable of identifying decommissioning workers’ improper use of PPE with high precision and recall rate while ensuring real-time performance to meet the industrial requirements.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Graphite, nuclear technology, radiation studies · Nuclear and radioactivity studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Nuclear Engineering and Management, School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan
Funding
Japan Society for the Promotion of Science
References (26)
Show source-provided references
1. (2013). Safety Assessment for Decommissioning, INTERNATIONAL ATOMIC ENERGY AGENCY.
2. (2016). Efforts to Improve Working Environment and Reduce Radiation Exposure at Fukushima Daiichi Nuclear Power Station, Tokyo Electric Power Company. Technical Report.
3. Mori, K., Tateishi, S., and Hiraoka, K. (2016). Health issues of workers engaged in operations related to the accident at the Fukushima Daiichi Nuclear Power Plant. Psychosocial Factors at Work in the Asia Pacific, Springer.10.1007/978-3-319-44400-0_17 ↗
4. Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., and Sheikh, Y. (2018). OpenPose: Realtime multi-person 2D pose estimation using Part Affinity Fields. arXiv.10.1109/CVPR.2017.143 ↗
5. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
6. Mobile passive Radio Frequency Identification (RFID) portal for automated and rapid control of Personal Protective Equipment (PPE) on construction sites10.1016/j.autcon.2013.08.009 ↗
7. Dong, S., He, Q., Li, H., and Yin, Q. (2015, January 11–12). Automated PPE misuse identification and assessment for safety performance enhancement. Proceedings of the ICCREM 2015, Lulea, Sweden.10.1061/9780784479377.024 ↗
8. Hard-hat detection for construction safety visualization10.1155/2015/721380 ↗
9. Hardhat-wearing detection for enhancing on-site safety of construction workers10.1061/(ASCE)CO.1943-7862.0000974 ↗
10. Detecting non-hardhat-use by a deep learning method from far-field surveillance videos10.1016/j.autcon.2017.09.018 ↗
11. Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset10.1016/j.autcon.2019.102894 ↗
12. Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7–12). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
13. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
14. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C.L. (2014, January 6–12). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.10.1007/978-3-319-10602-1_48 ↗
15. (2020, July 22). Kinect for Windows. Available online: https://developer.microsoft.com/en-us/windows/kinect/.
16. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–23). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
17. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
18. Girshick, R. (2015, January 11–18). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Las Condes, Chile.10.1109/ICCV.2015.169 ↗
19. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
20. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
21. He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
22. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
23. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2–4). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX symposium on operating systems design and implementation (OSDI 16), Savannah, GA, USA.
24. Imagenet large scale visual recognition challenge10.1007/s11263-015-0816-y ↗
25. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
26. (2020, June 06). Microsoft Azure IoT. Available online: https://azure.microsoft.com/en-us/overview/iot/.