Power operator behavior recognition method based on deep learning

Jun Liu; Zhenwei Qin; Yang Yang; Xinyu Han

doi:10.1117/12.3011490

7 December 2023 Power operator behavior recognition method based on deep learning

Jun Liu, Zhenwei Qin, Yang Yang, Xinyu Han

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129412S (2023) https://doi.org/10.1117/12.3011490
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, 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 intelligent recognition of workers’ behavior on the job site, an OpenPose based hazard behavior recognition method for power workers is proposed. The method extracts the key bone information of workers from video stream images, uses deep neural network to realize the human behavior and posture perception of workers in multi-person scenarios, detects and recognizes the violations of construction workers in real time, and issues warnings. The experimental results show that the proposed method can realize the accurate and real-time safety monitoring of the power field operators' behavior, ensure the personal safety of the field operators and the smooth progress of the power operation, and has certain robustness and generalization ability.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Liu, Zhenwei Qin, Yang Yang, and Xinyu Han "Power operator behavior recognition method based on deep learning", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129412S (7 December 2023); https://doi.org/10.1117/12.3011490

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