An Unsupervised Multi-scale Micro-crack Segmentation Scheme for Multicrystalline Solar Cells

Weijing Dou, Shuo Shan, Nawei Zhang, Jinxia Zhang, Kanjian Zhang, Haikun Wei

Abstract: Solar cells play a key part in photovoltaic power system. Micro-crack is one of the common defaults, which will reduce the conversion efficiency and usable lifetime of solar cells enormously. So it is essential to locate it and identify the shape to help us obtain more detailed assessment of power loss. However, the automatic detection and segmentation of micro-crack from electroluminescence (EL) images of solar cells have been a challenging task. In this work, we propose an unsupervised multiscale micro-crack segmentation scheme for multicrystalline solar cells, which takes advantages of both of the superpixellevel and the pixel-level segmentation on the EL images. Firstly, in order to avoid disturbances from the grid of the cell, we partition the EL image of a single cell into pieces. Then, a fusion of superpixel and pixel outcomes is employed to obtain the accurate pixel-level micro-crack by considering the global and local features collectively. Finally, we restore the results of each pixel on the piece to the cell corresponding location. The experimental results show that the proposed scheme is more accurate, and the shape and area of micro-crack can be evaluated particularly.