Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells
This study provides an automatic method for detecting finger interruptions in electroluminescence (EL) images of multicrystalline solar cells. The proposed method is a supervised classification method. We obtain regions of interest (ROI) by separating the EL image to several regions. The fingers wit...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/879675 |
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doaj-8a44eb5277fa4f9c88200c1ce4eec66d2020-11-24T22:12:32ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/879675879675Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar CellsDin-Chang Tseng0Yu-Shuo Liu1Chang-Min Chou2Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Road, Jhongli 32001, TaiwanDepartment of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Road, Jhongli 32001, TaiwanDepartment of Electronic Engineering, Chien Hsin University of Science and Technology, No. 229, Jianxing Road, Jhongli 32097, TaiwanThis study provides an automatic method for detecting finger interruptions in electroluminescence (EL) images of multicrystalline solar cells. The proposed method is a supervised classification method. We obtain regions of interest (ROI) by separating the EL image to several regions. The fingers within each ROI are candidates for defect detection. We horizontally scan each ROI region and extract features from each finger pixel. In the training stage, we record a set of features which are extracted from interrupted fingers and noninterrupted fingers. These features are represented as points in a spectral embedding space produced by spectral clustering method. These points will be classified into two clusters: interrupted fingers and noninterrupted fingers. In the classification stage, we firstly detect the position of fingers in an EL image and obtain features from each finger. The set of features in each finger combined with known features in the training stage will be represented as points in the spectral embedding space and then will be classified to the cluster with nearer cluster centroid of known features. Experimental results show that the proposed method can effectively detect finger interruptions on a set of EL images of various solar cells.http://dx.doi.org/10.1155/2015/879675 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Din-Chang Tseng Yu-Shuo Liu Chang-Min Chou |
spellingShingle |
Din-Chang Tseng Yu-Shuo Liu Chang-Min Chou Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells Mathematical Problems in Engineering |
author_facet |
Din-Chang Tseng Yu-Shuo Liu Chang-Min Chou |
author_sort |
Din-Chang Tseng |
title |
Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells |
title_short |
Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells |
title_full |
Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells |
title_fullStr |
Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells |
title_full_unstemmed |
Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells |
title_sort |
automatic finger interruption detection in electroluminescence images of multicrystalline solar cells |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
This study provides an automatic method for detecting finger interruptions in electroluminescence (EL) images of multicrystalline solar cells. The proposed method is a supervised classification method. We obtain regions of interest (ROI) by separating the EL image to several regions. The fingers within each ROI are candidates for defect detection. We horizontally scan each ROI region and extract features from each finger pixel. In the training stage, we record a set of features which are extracted from interrupted fingers and noninterrupted fingers. These features are represented as points in a spectral embedding space produced by spectral clustering method. These points will be classified into two clusters: interrupted fingers and noninterrupted fingers. In the classification stage, we firstly detect the position of fingers in an EL image and obtain features from each finger. The set of features in each finger combined with known features in the training stage will be represented as points in the spectral embedding space and then will be classified to the cluster with nearer cluster centroid of known features. Experimental results show that the proposed method can effectively detect finger interruptions on a set of EL images of various solar cells. |
url |
http://dx.doi.org/10.1155/2015/879675 |
work_keys_str_mv |
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