Infrared Fault Classification Based on the Siamese Network

Abstract

The rapid development of solar energy technology has led to significant progress in recent years, but the daily maintenance of solar panels faces significant challenges. The diagnosis of solar panel failures by infrared detection devices can improve the efficiency of maintenance personnel. Currently, due to the scarcity of infrared solar panel failure samples and the problem of unclear image effective features, traditional deep neural network models can easily encounter overfitting and poor generalization performance under small sample conditions. To address these problems, this paper proposes a solar panel failure diagnosis method based on an improved Siamese network. Firstly, two types of solar panel samples of the same category are constructed. Secondly, the images of the samples are input into the feature model combining convolution, adaptive coordinate attention (ACA), and the feature fusion module (FFM) to extract features, learning the similarities between different types of solar panel samples. Finally, the trained model is used to determine the similarity of the input solar image, obtaining the failure diagnosis results. In this case, adaptive coordinate attention can effectively obtain interested effective feature information, and the feature fusion module can integrate the different effective information obtained, further enriching the feature information. The ACA-FFM Siamese network method can alleviate the problem of insufficient sample quantity and effectively improve the classification accuracy, achieving a classification accuracy rate of 83.9% on an open-accessed infrared failure dataset with high similarity.