Structure-aware-based crack defect detection for multicrystalline solar cells

Abstract

Automatic crack defect detection for multicrystalline solar cells is a challenging task, owing to inhomogeneously textured background, disturbance of crystal grains pseudo defects, and low contrast between crack defect and background. In this paper, a novel structure-aware-based crack defect detection scheme (SACDDS) is proposed. Firstly, the structure features of crack defect and randomly distributed crystal grains are analyzed, and corresponding mathematical models are used to represent two structure features. Secondly, according to Hessian eigenvalues of the above mathematical models, the identification functions of linear-structure and blob-structure are obtained. Then, a novel structure similarity measure (SSM) function is designed by using the identification functions of two structures, which can highlight crack defect, suppress crystal grains simultaneously. It significantly weakens the interference of inhomogeneous texture and obtains uniform background. Further, in order to overcome non-uniform response of crack region and extract crack defect, a tensor voting-based non-maximum suppression (TV-NMS) method is developed. It improves the uniformity of crack defect response and extracts candidate crack defect pixels. Finally, an effective morphological operation is applied to remove non-crack pixels and complete crack defect can be located in the EL images. Experimental results show that the proposed method can completely extract crack defect in the inhomogeneously textured background, which is well effective and outperforms the previous methods.