Accurate and robust crack detection using steerable evidence filtering in electroluminescence images of solar cells. (July 2019)

• Record Type: Journal Article
• Authors: Chen, Haiyong; Zhao, Huifang; Han, Da; Liu, Kun
• Is Part Of: Optics and lasers in engineering. Volume 118(2019)
• Page Start: 22
• Page End: 33
• Publication Date: 2019-07
• Subjects: Crack detection -- Solar cells -- Image processing -- Steerable evidence filter -- Segmentation; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2019.01.016
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: An accurate and robust crack detection scheme for solar cells is proposed. A novel steerable evidence filter is developed to provide evidence for crack. The intensity of crystal grains and crack is similar in electroluminescence images. The scheme is robust to heterogeneously textured background made by crystal grains. The scheme will detect low contrast and complex cracks with bends and bifurcations. Abstract: Solar cells are the key part of photovoltaic power system, and the online quality inspection of solar cells ensures conversion efficiency and usable lifetime of photovoltaic modules. However, automatic crack detection in EL images of solar cells has been a challenging task, owing to the heterogeneously textured background, low contrast between crack and surrounding background including randomly distributed crystal grains, the diversity of crack types, and so on. To address these challenges, this paper presents a new accurate and robust crack detection scheme for multicrystalline solar cells. Firstly, a novel steerable evidence filter is developed to generate the crack saliency map, which significantly enhances the contrast between crack and surrounding background. Secondly, a segmentation-based method including local threshold and minimum spanning tree is employed, which ensures the extraction of complete crack. Next, the crack can be accurately located in the inspection image by computing the crack skeleton. Finally, experimental results on defective and defect-free EL images show that the proposed scheme is accurate and robust, and various types of cracks can be correctly detected. The proposed scheme achieved average detection rate of 94.4% on the data set, which performs better than the previous methods.