This paper presents an algorithm for the detection of micro-crack defects in the multicrystalline solar cells. This detection goal is very challenging due to the presence of various types of image anomalies like dislocation clusters, grain boundaries, and other artifacts due to the spurious discontinuities in the gray levels. In this work, an algorithm featuring an improved anisotropic diffusion filter and advanced image segmentation technique is proposed. The methods and procedures are assessed using 600 electroluminescence images, comprising 313 intact and 287 defected samples. Results indicate that the methods and procedures can accurately detect micro-crack in solar cells with sensitivity, specificity, and accuracy averaging at 97%, 80%, and 88%, respectively.
Anwar and Abdullah REF developed an algorithm for the detection of microcracks in polycrystalline solar cells.
Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique
EURASIP Journal on Image and Video Processing
Computer Science