Automatic Detection of Defective Solar Cells in Electroluminescence Images via Global Similarity and Concatenated Saliency Guided Network

Electroluminescence imaging becomes a very useful technique to automatically detect defects for solar cells since it can provide high resolution electroluminescence images. However, few methods explicitly consider the visual characteristics of the defects and the noises in solar cells. In this article, a global pairwise similarity and concatenated saliency guided neural network is proposed by fully considering the observed visual characteristics in electroluminescence solar cell images. The proposed network exploits a global pairwise similarity module and a concatenated saliency module to refine the features extracted by the convolutional neural network. The global pairwise similarity module aims to refine the features of an image pixel by modeling long-range dependencies. The concatenated saliency module is exploited to suppress the background and decouple different salient regions to better represent the features of an image. Extensive experiments based on five different baselines, i.e., VGG16, ResNet56, ResNet50, DenseNet40, and GoogleNet, prove that the proposed method significantly outperforms the baseline models and show that both the global similarity module and the concatenated saliency module can help detect defective solar cells in electroluminescence images.

Automated summary

In this article, a global pairwise similarity and concatenated saliency guided neural network is proposed for automatically detecting defects in solar cells using electroluminescence images. The proposed method significantly outperforms baseline models and demonstrates the effectiveness of the global similarity module and the concatenated saliency module in defect detection.