Deep learning-based detection of insulator defects in power grids

Ying Wang; Wenjie Wang; Yatong Sun

doi:10.1117/12.2689337

10 August 2023 Deep learning-based detection of insulator defects in power grids

Ying Wang, Wenjie Wang, Yatong Sun

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127482G (2023) https://doi.org/10.1117/12.2689337
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

In order to quickly and accurately identify insulator defects and solve the problems such as labor waste and misjudgment of traditional detection methods, this paper proposes an insulator detection algorithm based on YOLOv3(You Only Look Once). Firstly, the K-means algorithm is improved by the genetic algorithm to generate anchors, then the YOLOv3algorithm is trained using the anchors, and finally the trained model is used to detect the insulator images. After experimental comparison, the algorithm in this paper is more accurate than the original K-means algorithm to generate anchors, and the YOLOv3defect detection model is more accurate than the original algorithm.

Citation Download Citation

Ying Wang, Wenjie Wang, and Yatong Sun "Deep learning-based detection of insulator defects in power grids", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127482G (10 August 2023); https://doi.org/10.1117/12.2689337

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KEYWORDS

Detection and tracking algorithms

Genetic algorithms

Defect detection

Deep learning

Image processing

Biological samples

Genetics

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