Defect detection on Polycrystalline solar cells using Electroluminescence and Fully Convolutional Neural Networks

Quality control is critical in the production process of solar cells. A small crack in the cell can affect its future performance in energy production. Nowadays, one of the most used techniques to detect these defects is Electroluminescence (EL), which allows obtaining high-resolution images where the defects are highlighted and where a non-invasive inspection can be done. In this way, those defective cells can be removed from production without reaching the final product. In a previous work, the use of a Convolutional Neural Network (CNN) network that was executed multiple times on a EL cell image to obtain a defect segmentation map was proposed. In this paper, we propose to use a Fully Convolutional Network (FCN) to obtain the same defect segmentation map but in a single step. The experiments with the FCN have been performed on the same dataset used in the previous work to compare the results. The comparison comprehends two aspects, the precision to detect the defects and the execution time necessary to process a cell image.