太陽能電池板表面瑕疵檢測｜國立中央大學博碩士論文系統

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研究生：	陳心怡 Shin-Yi Chen
論文名稱：	太陽能電池板表面瑕疵檢測 Surface Defect Detection for Solar Cells
指導教授：	曾定章 Din-Chang Tseng
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
畢業學年度：	95
語文別：	英文
論文頁數：	85
相關次數：	點閱：5 下載：0
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查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

隨著能源的日益耗盡，我們迫切地追求新能源的開發。太陽能即是
一個具有潛在性的再生能源。而太陽能電池板是用來轉換太陽能的主要
裝置。為了維持太陽能電池板的品質，我們發展了數種方法來檢測太陽
能電池板的表面瑕疵。
我們所檢測的對象包含數種不同種類的太陽能電池板。針對具有均
勻表面的太陽能電池板，我們採用多選擇性的自動門檻值選取法來檢測
瑕疵。我們額外地以追蹤的方法來檢測電池板的邊緣及線路部份。
針對具有隨機紋理表面的太陽能電池板，若隨機紋理之間的對比並
不是很明顯，我們採用以線偵測為基礎的方法，檢測出較亮的瑕疵；否
則，我們先以非等方性的擴散方法模糊複雜的紋理背景，同時保留住較
亮的瑕疵。接著仍然以線偵測為基礎的方法擷取出瑕疵。
針對具有規律紋理表面的太陽能電池板，我們使用紋理學習的方法
檢測瑕疵。我們事先訓練無瑕疵影像上的十種不同紋理。接著，將待測
影像上的每一個小區塊和各種紋理模板做比較。若得到一個不相配的結
果，表示此區塊內有瑕疵。
我們以2048×2048 或者 640×640 解析度的單、多晶太陽能電池板影
像來測試我們的偵測效能。從實驗結果顯示，我們所提出的方法已接近
實用的階段。

Due to the decrease of the energy sources, the development of new
energy sources is urgently pursued. One of the potential energy is the solar
power. The solar cells are the primary devices to capture the solar energy. To
maintain the quality of solar cells, we develop several inspection methods to
detect defects on the solar cells.
Several different kinds of solar cells are inspected. For the solar cell with
uniform surface, we detect the defects using a multi-cased thresholding
method. The border and busbar areas of solar cells are separated and
inspected with a tracking style.
For the solar cells with random texture surface, we use line-based
detecting method to find brighter defects if the random textures of
background is not clear; otherwise, we use an anisotropic diffusion based
detection to blur the complex background while brighter defects would be
preserved. Then the defects are also extracted by the line-based method.
For the solar cells with regular texture surface, we use texture based
method to detect the defects. We trained ten different texture patterns on
defect-free solar images in advance. Then, we compare each patch of testing
images with the corresponding trained patterns. The un-matched result shows
defects in the patch.
In the experiments, three proposed methods are evaluated with single
crystal and polysilicon solar cells of 2048×2048 and 640×640 resolutions.
The results show that the proposed methods are near practice for industry
applications.

摘要.................................................................................................................. II
誌謝................................................................................................................. III
目錄.................................................................................................................IV
第一章 緒論................................................................................................... 一
第二章 相關研究........................................................................................... 二
第三章 利用自動門檻值選取做均勻區域之瑕疵檢測............................... 三
第四章 紋理影像的瑕疵檢測....................................................................... 四
第五章 實驗與評估....................................................................................... 五
第六章 結論及未來工作............................................................................... 六
附錄 英文版論文........................................................................................... 七
Abstract ............................................................................................................. ii
Contents ............................................................................................................ iii
List of Figures .................................................................................................. vi
List of Tables ..................................................................................................... x
Chapter 1 Introduction ...................................................................................... 1
1.1 Motivation ................................................................................................ 1
1.2 System overview ...................................................................................... 2
1.2.1 Block detection on uniform images .................................................. 3
1.2.2 Defect detection on textured images ................................................. 4
1.2.3 Connected component generation ..................................................... 5
1.3 Thesis organization .................................................................................. 5
Chapter 2 Related Works .................................................................................. 7
2.1 Thresholding ............................................................................................ 7
2.2 Defect detection on textured images........................................................ 9
2.2.1 Random textured images ................................................................... 9
2.2.2 Regular textured images .................................................................. 10
2.2.3 Gausian mixture models .................................................................. 11
2.3 Connected component generation .......................................................... 11
Chapter 3 Defect Detection of Uniform Regions by Thresholding ................ 13
3.1 Non-uniform area search and inspection ............................................... 16
3.1.1 A simple multiscale approach ......................................................... 17
3.1.2 Otsu’s bi-level thresholding ............................................................ 18
3.1.3 Search and inspection on busbar area ............................................. 20
3.1.4 Search and inspection on border area.............................................. 23
3.2 Block detection on the uniform area...................................................... 24
3.2.1 Otsu’s bi-level thresholding on the unimodal histogram ................ 26
3.2.2 Iterative near-optimal thresholding ................................................. 27
3.2.3 Mean-variance based defect detection ............................................ 29
Chapter 4 Defect Detection on Textured Images ............................................ 31
4.1 Detection on random textured images ................................................... 31
4.1.1 Line based defect detection ............................................................. 32
4.1.2 An anisotropic diffusion based defect detection ............................. 34
4.2 Detection on regular textured images .................................................... 38
4.2.1 Textural template learning .............................................................. 41
4.2.2 Patch detection ................................................................................ 44
Chapter 5 Experiments and Evaluations ......................................................... 45
5.1 Experiments ........................................................................................... 45
5.1.1 Thresholding based detection on uniform images .......................... 47
5.1.2 Line based defect detection ............................................................. 51
5.1.3 An anisotropic diffusion based defect detection ............................. 51
5.1.4 Texture based defect detection ........................................................ 55
5.2 Evaluations............................................................................................. 57
Chapter 6 Conclusions and Future Works ...................................................... 59
References ....................................................................................................... 61

                                

[1] Adelson, E. H., C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M.
Ogden, "Pyramid methods in image processing," RCA Engineer, vol.29,
no.6, pp.33-41, 1984.
[2] Arifin, A. Z. and A. Asano, "Image segmentation by histogram
thresholding using hierarchical cluster analysis," Pattern Recognition
Letters, vol.27, no.13, pp.1515-1521, 2006.
[3] Farnoosh, R., G. Yari, and B. Zarpak, "Image segmentation using
Gaussian mixture models," in Proc. 26th Int. Workshop on Bayesian
Inference and Maximum Entropy Methods in Science and Engineering,
Paris, France, Jul.8-13, 2006, pp.26-27.
[4] Fu, Z., Y. Zhao, Y. Liu, Q. Cao, M. Chen, J. Zhang, and J. Lee, "Solar
cell crack inspection by image processing," in Proc. Int. IEEE Conf.
Business of Electronic Product Reliability and Liability, Shanghai, China,
Apr.27-30, 2004, pp.77-80.
[5] Kapur, J. N., P. K. Sahoo, and A. K. C. Wong, "A new method for
gray-level picture thresholding using the entropy of the histogram,"
Comput. Vision Graphics Image Process, vol.29, no.3, pp.273-285,
1985.
[6] Kirshner, S., I. V. Cadez, P. Smyth, and C. Kamath, "Learning to
classify galaxy shapes using the EM algorithm," in Proc. 15th Neural
Information Processing Systems, Vancouver, British Columbia, Canada,
Dec.9-14, 2002, pp.1497-1504.
[7] Kittler, J. and J. Illingworth, "Minimum error thresholding," Pattern
Recognition, vol.19, no.1, pp.41-47, 1986.
[8] Lutz, R. K., "An algorithm for the real time analysis of digitized
images," The Computer Journal, vol.23, no.3, pp.262-269, 1980.
[9] Lyu, S., "A kernel between unordered sets of data: the Gaussian mixture
approach," in Proc. 16th Euro. Conf. Machine Learning, Porto, Portugal,
Oct.3-7, 2005, pp.255-267.
[10] Ng, H.-F., "Automatic thresholding for defect detection," in Proc. 3rd
Int. Conf. Image and Graphics, Hong Kong, China, Dec.18-20, 2004,
pp.532-535.
[11] Otsu, N., "A threshold selection method from gray-level histograms,"
IEEE Trans. Systems Man and Cybernetics, vol.9, no.1, pp.62-66, 1979.
[12] Sezgin, M. and B. Sankur, "Survey over image thresholding techniques
and quantitative performance evaluation," Electronic Imaging, vol.13,
no.1, pp.146-165, 2004.
[13] Suzuki, K., I. Horiba, and N. Sugie, "Fast connected-component labeling
based on sequential local operations in the course of forward raster scan
followed by backward raster scan," in Proc. 15th Int. Conf. Pattern
Recognition, Barcelona, Spain, Sep.3-7, 2000, pp.434-437.
[14] Tsai, D.-M. and C.-H. Chiang, "Automatic band selection for wavelet
reconstruction in the application of defect detection," Image and Vision
Computing, vol.21, no.5, pp.413-431, 2003.
[15] Tsai, D.-M. and S.-M. Chao, "An anisotropic diffusion-based defect
detection for sputtered surfaces with inhomogeneous textures," Image
and Vision Computing, vol.23, no.3, pp.325-338, 2005.
[16] Tsai, W.-H., "Moment-preserving thresholding: a new approach,"
Computer Vision, Graphics, and Image Processing, vol.29, no.3,
pp.377-393, 1985.
[17] Xie, X. and M. Mirmehdi, "Texture exemplars for defect detection on
random textures," in Proc. 3rd Int. Conf. Advances in Pattern
Recognition, Bath, UK, Aug.22-25, 2005, pp.404-413.
[18] Yin, P.-Y. and L.-H. Chen, "A fast iterative scheme for multilevel
thresholding methods," Signal Processing, vol.60, no.3, pp.305-313,
1997.

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