Image Defect Detection and Segmentation Algorithm of Solar Cell Based on Convolutional Neural Network

The use of infrared or electroluminescence(EL) images of solar cell modules for defect detection is a very important method in non-destructive testing. Traditionally, this work is done by skilled technicians, which is time-consuming and susceptible to subjective factors. The surface defect detection method of solar cells based on machine learning has become one of the main research directions because of its high efficiency and convenience. For this reason, this paper proposes an improved fusion model based on VGGNet and U-Net++, which is used for defect detection and segmentation of EL images of solar cells. In the defect detection stage, the input image is processed pertinently, and by modifying the convolutional layer and the fully connected layer of the network, while improving the performance of the algorithm, it accelerates the convergence and avoids the phenomenon of over-fitting. In the defect segmentation stage, the defect location is marked based on the public data set, which is used for the training of each segmentation model, and the effect of different segmentation networks is compared to select a reasonable model. The experimental results show that the defect detection accuracy of the improved VGG16 network on the elpv-dataset is 95.2%, and the U-Net++ defect segmentation model has an average MIoU value of 0.955, which is better than other existing methods.