Defect detection of solar cells based on Haar feature and kernel fuzzy c-means clustering

Abstract

The detection of micro cracks on the surface of solar cells is very important to improve the durability of photovoltaic modules. In this paper, Haar feature extraction and kernel fuzzy c-means clustering algorithms are proposed to detect the defects of solar cells. Haar extended template is used to extract the edge features as training samples, combined with kernel fuzzy c-means clustering (KFCM) algorithm and improved Xie Beni index to detect the surface defects of solar cells. The recognition rate of no defects is 98%, and the recognition rate of vertical finger defects is 97%, The recognition rate of microcrack is 93%, and that of fracture is 92%.