Contrastive learning for solar cell micro-crack detection

As the core component of the photovoltaic system, the quality of solar cells determines the conversion efficiency of electric energy. Some strategies have been proposed to detect the crack of solar cells, but most of them can not detect the crack efficiently. This paper proposed a new two-stage method for microcrack detection in polycrystalline images based on contrastive learning. First, the input picture without a label is learned by SimCLR to obtain the representation of the image. In the second stage, the linear classifier is trained based on the fixed encoder and the representation. In the comparative experiment, unsupervised contrastive learning is compared with cross-entropy training and supervised contrastive learning. The experimental results show that the linear classifier trained on unsupervised representation achieves a top-1 accuracy of 78.39%, which is 7.42% higher than the supervised contrastive learning method, compared with supervised learning, the results are comparable.