應用機器視覺於多晶矽太陽能電池與模組在電致發光影像之瑕疵檢測

Machine vision-based defect detection of solar cells/modules in electroluminescence images

吳世杰(Shih-Chieh Wu)

指導教授 ： 蔡篤銘

元智大學/工程學院/工業工程與管理學系/碩士(2011年)

https://doi.org/10.6838/YZU.2011.00285

摘要

在多晶太陽能電池與模組中，由於金屬手指斷線、微裂和裂痕之瑕疵是無法由肉眼或是一般CCD攝影機察覺，所以可運用電致發光(Electroluminescence, EL)影像技術來凸顯瑕疵。本研究利用機器視覺技術針對多晶矽太陽能電池與模組於電致發光影像之瑕疵進行檢測，但由於多晶太陽能電池與模組中的瑕疵與正常之晶格背景同時被凸顯於EL影像中，使得自動瑕疵檢測的困難度也大為增加。

本研究第一個主題探討多晶太陽能電池(Solar cell)於電致發光影像之瑕疵檢測，由於多晶太陽能電池於電致發光影像中，其金屬手指斷線、微裂及裂痕瑕疵具備條狀與線狀之特性，因此本研究以傅立葉轉換(Fourier transform)重建影像技術為基礎，將原始影像中之瑕疵消除，再比較原始影像與還原影像之差異，即可有效偵測瑕疵在EL影像中之位置。第二個主題檢測對象為多晶太陽能模組(Solar module)之電致發光影像，太陽能模組是由多個太陽能電池經串、並聯組合之產品，本研究運用獨立成份分析(Independent Component Analysis, ICA)，訓練多晶太陽能模組中正常之太陽能電池子影像的基底影像，並將每一待測太陽能電池之子影像利用基底影像的線性組合做影像重建，計算原始測試影像與重建影像之總差異值，即可有效判斷多晶矽太陽能模組中是否含有瑕疵太陽能電池之子影像。

在多晶太陽能電池之電致發光影像的檢測實驗中，本研究之傅立葉影像重建方法皆能提供正確的檢測結果，而一張多晶太陽能電池影像大小為550×550像素之檢測影像，平均檢測時間為0.29秒。在多晶太陽能模組之電致發光影像的檢測實驗中，本研究之ICA影像重建方法之平均分類正確率為90.6%，最高辨識率則為98.7%；當太陽能模組包含36片(6×6)太陽能電池，而模組影像大小為1250×1250像素時，平均檢測時間約為1.08秒。

關鍵字

機器視覺 ； 瑕疵檢測 ； 電致發光 ； 傅立葉轉換 ； 獨立成份分析 ； 太陽能電池 ； 太陽能模組

並列摘要

Finger interrupt, micro-crack and breakage are severer defects in the multicrystalline solar cell and cannot be observed by the naked eyes or the conventional CCD camera. The Electroluminescence (EL) imaging technique can be used instead to highlight these defects. In this study, a machine vision scheme is proposed to detect the defects of solar cells and solar modules in EL images. The EL image of a multicrystalline solar cell presents a heterogeneously textured pattern, which makes the defect detection task extremely difficult.

The first topic in this research focuses on defect detection of the multicrystalline solar cells in EL images. Since the finger interrupt and crack are line- or bar-shaped, the Fourier transform is used to eliminate suspected defects and results in a defect-free surface in the reconstructed image. By subtracting the reconstructed image from the original image, the defects can be distinctly enhanced in the difference image. Then, the defect is effectively segmented by a simple statistical control limit. The second topic of this research aims at defect inspection in the solar module, which is formed by a matrix of solar cells through series and parallel combinations. The Independent component analysis (ICA) is used to generate the basis images from defect-free solar cells. Each test image is reconstructed by a linear combination of the basis images. The accumulated gray-level difference between the test image and the reconstructed image is effectively used as a discrimination to detect the presence of defect in the solar cell subimages.

In the experimental results of solar cell EL images, the Fourier transform reconstruction scheme can effectively detect fingers interrupt, micro-crack, and breakage. The average computation time is 0.29 seconds for a solar cell image of size 550×550 pixels. The experimental results of solar module inspection show that the ICA image reconstruction method can provide up to 98.7% of correct classification. The average computation time is 1.08 seconds for a solar module image (containing 36 solar cells) of size 1250×1250 pixels.

並列關鍵字

Electroluminescence ； Fourier transform ； Independent component analysis ； Solar cell ； Solar module ； Machine vision ； Defect detection

參考文獻

陳心怡，2007 年，「太陽能電池板表面瑕疵檢測」，碩士論文，國立中央大學資訊工程研究所。

蔡欣儒，2007 年，「太陽能電池板的尺寸量測與線路瑕疵檢測」，碩士論文，國立中央大學資訊工程研究所。

Baykal, I. C. and G. A. Jullien, 2004, “In-camera detection of fabric defects,” Circuits

Bently, W. A., 1979, “The inspection: an automatic optical printed circuit board (PCB) inspection,” in Proc. Soc. Photo-Optical Instrument. Engrs., Vol. 201, pp. 37-47.

Bodnarova, A., M. Bennamoun and S.J. Latham, 2000, “A constrained minimisation approach to optimise Gabor filters for detecting flaws in woven textiles,” Acoustics, Speech, and Signal Processing, Istanbul, Vol. 6, pp. 3606-3609