Fault diagnosis method for arrester in infrared images based on improved U-Net

• Published in: Measurement : journal of the International Measurement Confederation 2024-08, Vol.236, p.114996, Article 114996
• Main Authors: Li, Peng, Zhang, Jiatong, Tao, Qian, Yuan, Ye, Zhou, Yeren, Liu, Bo, Wu, Tian, Pu, Ziheng
• Format: Article
• Language: English
• Subjects: Arrester; Infrared image; Temperature recognition; U-Net
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2024.114996

•Enhanced segmentation accuracy of arrester body in infrared images.•More precise method for extracting temperature from infrared images.•Maintaining fault diagnosis accuracy amidst background and umbrella skirt interference. Conducting target detection on arresters in practical settings is challenging due to complex backgrounds and occlusion by surrounding objects. This study proposes a method based on improved U-Net for segmenting arresters in infrared images, and achieves fault diagnosis by obtaining surface temperature of the arrester. Firstly, traditional standard convolution in the U-Net backbone is replaced with depthwise separable convolution, reducing computational complexity to about 11% of the original. Secondly, ECA is introduced in the skip connection section to enable cross-channel information exchange, improving segmentation accuracy by 3.21%. Thirdly, to reduce potential errors during the conversion of grayscale images into temperature maps, the data file storage format defined in DL/T 664 is adopted, ensuring the accuracy of the temperature matrix obtained for the arrester. Finally, three fault diagnosis methods based on the surface temperature distribution curve of the arrester were proposed, and the effectiveness of the methods was verified through experiments.