Thermal defect segmentation of electrical equipment based on saliency constraint

Huarong Xu; Wang Luo; Chengcheng Zhu; Zongxing Ji; Jia Bei

doi:10.1117/12.2658897

22 December 2022 Thermal defect segmentation of electrical equipment based on saliency constraint

Huarong Xu, Wang Luo, Chengcheng Zhu, Zongxing Ji, Jia Bei

Proceedings Volume 12508, International Symposium on Artificial Intelligence and Robotics 2022; 125080A (2022) https://doi.org/10.1117/12.2658897
Event: Seventh International Symposium on Artificial Intelligence and Robotics 2022, 2022, Shanghai, China

Abstract

Thermal defect detection aims to identify overheated areas of electric accessory with the help of infrared imaging technology. In this paper, we propose a thermal defect segmentation method based on saliency constraint. Specifically, we first design a convolutional neural network for infrared image classification, the thermal ones of which are then denoised and enhanced by image preprocessing; Next, the modified K-means clustering algorithm is utilized for region segmentation, which splinters infrared images as environment area, normal area and thermal area; Finally, we perform saliency detection on infrared images to obtain approximate region of temperature anomaly, and the overheated area is likewise segmented based on the modified K-means clustering algorithm, which is subsequently used to revise the thermal area segmented based on enhanced images to satisfy saliency constraint. Experimental results suggest that our method can improve the diagnostic efficiency of infrared images and realize the precise positioning of thermal defects, which outperforms the state-of-the-arts.

Citation Download Citation

Huarong Xu, Wang Luo, Chengcheng Zhu, Zongxing Ji, and Jia Bei "Thermal defect segmentation of electrical equipment based on saliency constraint", Proc. SPIE 12508, International Symposium on Artificial Intelligence and Robotics 2022, 125080A (22 December 2022); https://doi.org/10.1117/12.2658897

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