Micro-Crack Detection Of Solar Cells Featuring Adaptive Anisotropic Diffusion Filter And Semi-Supervised Support Vector Learning
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...
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.usm.my/63835/ |
| Abstract | Abstract here |
| Summary: | 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. |
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