Research on electric power aerial work recognition model based on deep learning

Chaobing Wei

doi:10.1117/12.3026565

27 March 2024 Research on electric power aerial work recognition model based on deep learning

Chaobing Wei

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131052T (2024) https://doi.org/10.1117/12.3026565
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Electric power operation is an important component to ensure the continuous supply of electric power. However, electric power operation involves a wide range, complicated process, high risk factor, and serious accidents such as personal injury often occur. Although the rules and regulations of power safety production are gradually improved and the operation management mode is gradually optimized, the low degree of digitalization and intelligence still makes the power operation face a high risk of major accidents. In this paper, deep learning framework PyTorch and YOLOv3 algorithm are used to construct a digital and intelligent situation element extraction method for the feature extraction process of image data, build a high-altitude work identification model for electric power construction to learn and train the judgment of high-altitude work, detect workers and safety belts as targets, and accurately send out early warning signals. The results provide the data basis for predicting the risk situation of electric power operation.

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

Citation Download Citation

Chaobing Wei "Research on electric power aerial work recognition model based on deep learning", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131052T (27 March 2024); https://doi.org/10.1117/12.3026565

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KEYWORDS

Education and training

Deep learning

Object detection

Data modeling

Detection and tracking algorithms

Evolutionary algorithms

Target detection

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