Electrical insulator identification method in multiple images based on two stage CNN network

Yong Yi; Yan Li; Jinqiao Du; Jie Tian; Zijun Liu; Fan Yang; Zhimin Li

doi:10.1117/12.2689379

10 August 2023 Electrical insulator identification method in multiple images based on two stage CNN network

Yong Yi, Yan Li, Jinqiao Du, Jie Tian, Zijun Liu, Fan Yang, Zhimin Li

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127480D (2023) https://doi.org/10.1117/12.2689379
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

Ultraviolet and infrared images are widely used to detect potential defects of electric equipment in advance. However, With the continuous expansion of the scale of electrical equipment, almost all the data is still uses manual analysis, which has some shortcoming example high cost. In order to improve the intelligence level of electric equipment detection, this paper proposes a multiple images identification method for electrical insulator based on two stage cnn network. First, a deep convolutional neural network identification model is built based on Faster-RCNN with initial weights in VGG. In addition, combine with the dataset expansion method example rotation, 462 images of electrical insulator are used for training. Finally, this model is used to identification image of equipment. The experimental show that the method proposed in this paper for electrical insulator map accuracy reaches 96.51%. Therefore, the proposed method can improve the identification accuracy, and provides condition for electrical equipment state detect.

Citation Download Citation

Yong Yi, Yan Li, Jinqiao Du, Jie Tian, Zijun Liu, Fan Yang, and Zhimin Li "Electrical insulator identification method in multiple images based on two stage CNN network", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127480D (10 August 2023); https://doi.org/10.1117/12.2689379

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