A Violation Behaviors Detection Method for Substation Operators based on YOLOv5 And Pose Estimation
ID:14 View Protection:ATTENDEE Updated Time:2022-08-11 13:10:38 Hits:405 Oral Presentation

Start Time:2022-11-04 17:20(Asia/Shanghai)

Duration:20min

Session:S Power System and Automation » OS13Oral Session 13

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Abstract
Violation behaviors of substation operators remain obstacle to power safety production. Previous work mostly relies on detecting objects such as helmets in the image to judge the behavior of substation operators, rather than extracting characteristics of substation operators’ behavior. In this work,violation behaviors are divided into two categories. One can be characterized by absence of tools, such as not wearing safety helmets and not wearing working clothes. The other violation behaviors such as falling on the ground, climbing or crossing do not have specific tools features. Thus, a violation behaviors detection strategy which can accurately identify two kinds of violation behaviors is developed by combining object detection model based on YOLOv5, pose estimation model based on HRNet and skeleton-based action recognition model based on ST-GCN. The results of experimental verification on data from a substation prove the effectiveness of the proposed strategy.
Keywords
Speaker
Jing Wang
NARI Technology Development Co. Ltd

Submission Author
Jing Wang NARI Technology Development Co. Ltd
Hualiang Zhou NARI Technology Development Co. Ltd
Han Sun NARI Technology Development Co. Ltd
Zhantao Su NARI Technology Development Co. Ltd.
Xiaomeng Li NARI Technology Development Co. Ltd
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    Nov 03

    2022

    to

    Nov 05

    2022

  • Aug 01 2022

    Draft paper submission deadline

  • Nov 04 2022

    Registration deadline

  • Nov 05 2022

    Contribution Submission Deadline

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