SafeFac: Video-based smart safety monitoring for preventing industrial work accidents

被引:11
作者
Ahn, Jungmo [1 ,2 ]
Park, JaeYeon [3 ]
Lee, Sung Sik [4 ]
Lee, Kyu-Hyuk [4 ]
Do, Heesung [4 ]
Ko, JeongGil [3 ]
机构
[1] Ajou Univ, Dept Artificial Intelligence, 206 Worldcup Ro, Suwon, Gyeonggi Do, South Korea
[2] Hyundai MOBIS, 203 Teheran Ro, Seoul, South Korea
[3] Yonsei Univ, Sch Integrated Technol, 50 Yonsei Ro, Seoul, South Korea
[4] Syst R&D Co, 150 Jinwei 2 Sandan Ro, Pyeongtaek, Gyeonggi Do, South Korea
关键词
Vision-based object detection; Factory safety management; Human-presence detection; Deep learning; LENS-DISTORTION;
D O I
10.1016/j.eswa.2022.119397
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This work presents SafeFac, an intelligent camera-based system for managing the safety of factory environ-ments. In SafeFac a set of cameras installed on the assembly line are used to capture images of workers that approach the machinery under hazardous situations to alert system managers and halt the line if needed. Given a challenging set of practical application-level requirements such as multi-camera support and low response latency, SafeFac exploits a YOLOv3-based light-weight human object detection. To address the latency-accuracy tradeoff, SafeFac incorporates a set of algorithms as pre-and post-processing modules and a novel adaptive camera scheduling scheme.Our evaluation with a video dataset containing more that 113,000 frames from real assembly line activity shows that SafeFac achieves high precision (99.93%) and recall (96.44%), and SafeFac successfully satisfies such challenging requirements as a ready-for-deployment system for safe factory management.
引用
收藏
页数:16
相关论文
共 68 条
  • [1] A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees
    Aboah, Armstrong
    Shoman, Maged
    Mandal, Vishal
    Davami, Sayedomidreza
    Adu-Gyamfi, Yaw
    Sharma, Anuj
    [J]. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021, 2021, : 4202 - 4207
  • [2] Ahn J., 2018, PLOS ONE, P13
  • [3] Amazon Web Service, 2022, AWS REK
  • [4] Tracking multiple construction workers through deep learning and the gradient based method with re-matching based on multi-object tracking accuracy
    Angah, Ohay
    Chen, Albert Y.
    [J]. AUTOMATION IN CONSTRUCTION, 2020, 119
  • [5] [Anonymous], 2015, THE GUARDIAN
  • [6] [Anonymous], 2019, WORKPL INJ
  • [7] Bay H., 2008, COMPUT VIS IMAGE UND, V110, P346, DOI [DOI 10.1016/j.cviu.2007.09.014, 10.1016/j.cviu.2007.09.014]
  • [8] Bochkovskiy A, 2020, Arxiv, DOI arXiv:2004.10934
  • [9] Bräuer-Burchardt C, 2004, LECT NOTES COMPUT SC, V3175, P570
  • [10] Bräuer-Burchardt C, 2001, IEEE IMAGE PROC, P225, DOI 10.1109/ICIP.2001.958994