Micro-Crack Detection Of Solar Cells Featuring Adaptive Anisotropic Diffusion Filter And Semi-Supervised Support Vector Learning - Majid, Said Amirul Anwar B Ab Hamid @ Ab (2014)
In this thesis, a machine vision-based application for detecting micro-crack in an electro luminescence (el) image of solar cell is presented. The detection is a very challenging problem due to the complexity of the textural properties and background inhomogeneity of el images. Nevertheless, the micro-crack defect exhibits some unique properties such as high in gradient and low gray-levels. These properties together with the shape feature of the micro-crack are used in developing the detection algorithm. In this work, an image processing algorithm featuring an adaptive anisotropic diffusion filter and a segmentation technique based on twostage thresholding is proposed. The outcomes of this algorithm have demonstrated a highly accurate segmentation results compared to other standard methods. Based on the accuracy measure, the proposed methods achieve the highest f-measure of 0.0821. The local image features such as shape representation of the binary connected components are extracted and used in the machine learning to distinguish between cracked and good solar cells.