Abstract: It is difficult to detect solar panel crack defect due to low resolution and contrast,and few samples lead to inadequate training problem.To solve these problems,this paper puts forward the super resolution reconstruction method of solar panel images based on meta-transfer,and we adopt joint training method,that is,the internal image and external large-scale image information are used as the different stages of training data.First,a large amount of data is used to pretrain the model to learn the external public characteristics of large-scale data.Then,we use the meta-learning model MAML for multi-task training to find initial parameters,which are suitable for the unsupervised task of few samples to improve the generalization ability of the model.Finally,we put pretrained parameters in improved ZSSR to improve the Self-supervised Learning.Through DIV2K,Set5,BSD100 and solar panels electroluminescent imaging training dataset,the experimental results show that compared with the traditional CARN,RCAN,IKC and ZSSR,this method has the higher peak signal-to-noise ratio,up to 36.66,and fewer parameters,compared with ZSSR,the number of parameters decreases by 70 000 with shorter computation time,and compared with CARN,the computation time decreases by 0.51 s.It is obvious that our method has the better reconstruction effect,the higher reconstruction efficiency.

Key words: Convolutional neural network, DIV2K training set, Meta-learning, Solar panel defects, Super-resolution reconstruction