Research on deep learning-based behavioral recognition technology for electricity operators

Xinsheng Chen; Xiaofa Zhou; Zongbo Chu; Boyang Sun

doi:10.1117/12.3015659

6 February 2024 Research on deep learning-based behavioral recognition technology for electricity operators

Xinsheng Chen, Xiaofa Zhou, Zongbo Chu, Boyang Sun

Proceedings Volume 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023); 129795Q (2024) https://doi.org/10.1117/12.3015659
Event: 9th International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 2023, Guilin, China

Abstract

At present, the on-site safety monitoring of power is mainly monitored by personnel through the whole process of surveillance video, but the use of manual detection method is not only a waste of time, but also prone to missing the situation, so that the personal safety of staff cannot be guaranteed. In order to realize the intelligent recognition of workers' behavior on the job site, a dangerous behavior recognition technology based on OpenPose was proposed. The method extracts the key bone information of electric workers from video stream images, uses deep neural network to realize the human behavior and posture perception of electric workers in multi-person scenarios, detects and recognizes the violations of construction workers in real time, and issues warnings. The proposed method realizes the accurate and real-time safety monitoring of the power field operators' behavior, guarantees the personal safety of the field operators and the smooth progress of the power operation, and has certain robustness and generalization ability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinsheng Chen, Xiaofa Zhou, Zongbo Chu, and Boyang Sun "Research on deep learning-based behavioral recognition technology for electricity operators", Proc. SPIE 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 129795Q (6 February 2024); https://doi.org/10.1117/12.3015659

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