# Infrared Fault Classification Based on the Siamese Network
## 2 Related Work  
Currently, the large-scale promotion of thermal infrared detection technology to construct intelligent solar power generation systems is a widely concerned research direction. Intelligent failure classification of solar panel components by thermal infrared detection and electrical characteristic analysis is currently the main method [9]. Thermal infrared detection is the method of taking thermal images of solar panels using infrared cameras and then detecting different types of failures based on the characteristics of the images [10]. Electrical characteristic analysis is the method of collecting the output characteristics of solar panel components and then using these characteristics to detect failures [11].  
Due to the ability to identify failures without affecting the normal operation of the solar power generation system, thermal infrared detection is adapted to the detection direction of failure identification. In recent years, with the application of deep learning and other technologies, it has become possible to identify failure characteristics with greater accuracy, and in this way, improve the accuracy of failure detection while reducing the cost of maintenance. Therefore, this method has been widely applied in the field of failure detection. With the continuous development of image feature extraction technology, the failure detection method based on thermal infrared images has received widespread attention [12]. [PERSON] et al. [13] propose a two-stage neural network single-step solar power prediction method that is applicable to small samples, solving the difficulty of applying conventional short-term solar power prediction methods. [PERSON] et al. [14] present a simple and enhanced perturb and observation method, which is enhanced by including the change in current, in addition to the changes in output voltage and output power of the PV module. In [15], a single sensor-based economical charging adapter is presented for electric vehicles to provide pillar top solar panels on remote locations, and the logic-tuneddeterministic optimization algorithm is proposed to accomplish maximum power point tracking operation and battery charging management.  
On the other hand, deep learning models represented by Siamese networks have effectively solved the small sample problem in photovoltaic thermal fault detection. [PERSON] and [PERSON] [16] propose a health representation learning method based on a Siamese network to prevent overfitting by which the differences between samples in the embedding space of the Siamese network should follow the differences in the remaining useful life values via the introduction of a multitask learning scheme. [PERSON] et al. [17] propose an IoU-aware matching-adaptive Siamese network for visual tracking, which integrates multiple types of encoded feature maps and adaptive sample matching information to simultaneously perform target classification and bounding box regression. [PERSON] et al. [18] propose a simple and effective spatio-temporal attention-based Siamese method, which performs reliable local searching and wide-range re-detection alternatively for robustly tracking drones in the wild. In [19], a high-frequency attention-guided Siamese network is proposed to enhance the high-frequency information of buildings, in which a built-in high-frequency attention block is applied. [PERSON] et al. [20] propose a smart contract vulnerability detection system based on the Siamese network, which improves the original Siamese network model to perform smart contract vulnerability detection by comparing the similarity of two subnetworks with the same structure and shared parameters. In [21], a Siamese network tracker is proposed, which combines shallow-middle-deep feature fusion with a clustering-based adaptive rectangular window filter. [PERSON] et al. [22] propose an object tracking algorithm combining attention mechanism and correlation filter theory based on the framework of full convolutional Siamese neural networks to solve the problem of tracking drift during movement.