Defect detection on button surfaces with the weighted least-squares model

doi:10.1007/s12200-017-0687-7

Front. Optoelectron.

2017, Vol. 10

Issue (2) : 151-159 https://doi.org/10.1007/s12200-017-0687-7

RESEARCH ARTICLE

Defect detection on button surfaces with the weighted least-squares model

Yu HAN, Yubin WU, Danhua CAO(

), Peng YUN

School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China

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Abstract

Defect detection is important in quality assurance on production lines. This paper presents a fast machine-vision-based surface defect detection method using the weighted least-squares model. We assume that an inspection image can be regarded as a combination of a defect-free template image and a residual image. The defect-free template image is generated from training samples adaptively, and the residual image is the result of the subtraction between each inspection image and corresponding defect-free template image. In the weighted least-squares model, the residual error near the edge is suppressed to reduce the false alarms caused by spatial misalignment. Experiment results on different types of buttons show that the proposed method is robust to illumination vibration and rotation deviation and produces results that are better than those of two other methods.

Keywords machine vision surface defect detection weighted least-squares model

Corresponding Author(s): Danhua CAO

Just Accepted Date: 28 March 2017 Online First Date: 07 April 2017 Issue Date: 05 July 2017

Cite this article:

Yu HAN,Yubin WU,Danhua CAO, et al. Defect detection on button surfaces with the weighted least-squares model[J]. Front. Optoelectron., 2017, 10(2): 151-159.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-017-0687-7
https://academic.hep.com.cn/foe/EN/Y2017/V10/I2/151

Fig.1 Block diagram of the proposed method for defect detection

Fig.2 Image decomposition process. (a) Inspection image; (b) adaptive template image; and (c) residual image

Fig.3 Detection results of different methods. (a) is the input image. (b) and (d) are the results of the common least-squares model. (b) is the enhanced residual image, and (d) is the defect segmentation result. (c) and (e) are the results of the weighted least-squares model. (c) is the enhanced residual image, and (e) is the defect segmentation result

Tab.1 Details of the test samples used in the experiments

Tab.2 Experimental performance of different methods

Fig.4 Comparison of defect detection results on three samples by using different methods. (a)–(c) correspond to samples1–3. The first column contains the original images; the second and third columns show the results of saliency-based and ICA-based methods, respectively; and the last column presents the detection results of the proposed method

Fig.5 Recognition rate versus training dataset sizes in samples 1–3

	recognition rate
	sample1	sample2	sample3
$W ∝ diag (∇ I)$	0.94	0.99	0.98
$W ∝ diag (1)$	0.88	0.93	0.95
$W ∝ diag (exp ? (− ∇ I))$	0.75	0.78	0.89

Tab.3 Relationship between recognition rate and weight matrix W

Fig.6 Receiver operating characteristic curves of the proposed method

Tab.4 Recognition rate versus number of Gaussian components K

Fig.7 Comparison of the recognition rate of the proposed method with those of the saliency-based and ICA-based methods in terms of rotation deviation angles

Fig.8 Effect of illumination variations. (a) Original inspection images under different illumination intensities (top to bottom correspond to 2500, 3000, 3500, and 4000 lx); (b) adaptive template images corresponding to the first column; and (c) binary results corresponding to the first column

Tab.5 Recognition rates under different illuminations

1	Li W B, Lu C H, Zhang J C. A lower envelope Weber contrast detection algorithm for steel bar surface pit defects. Optics & Laser Technology, 2013, 45(1): 654–659 https://doi.org/10.1016/j.optlastec.2012.05.016
2	Crispin A J, Rankov V. Automated inspection of PCB components using a genetic algorithm template-matching approach. International Journal of Advanced Manufacturing Technology, 2007, 35(3): 293–300 https://doi.org/10.1007/s00170-006-0730-0
3	Arivazhagan S, Ganesan L, Bama S. Fault segmentation in fabric images using Gabor wavelet transform. Machine Vision and Applications, 2006, 16(6): 356–363 https://doi.org/10.1007/s00138-005-0007-x
4	Li W C, Tsai D M. Wavelet-based defect detection in solar wafer images with inhomogeneous texture. Pattern Recognition, 2012, 45(2): 742–756
5	Tsai D M, Wu S C, Chiu W Y. Defect detection in solar modules using ICA basis images. IEEE Transactions on Industrial Informatics, 2013, 9(1): 122–131 https://doi.org/10.1109/TII.2012.2209663
6	Cen Y G, Zhao R Z, Cen L H, Cui L H, Miao Z J, Wei Z. Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction. Neurocomputing, 2015, 149: 1206–1215 https://doi.org/10.1016/j.neucom.2014.09.007
7	Zhou W, Fei M, Zhou H, Li K. A sparse representation based fast detection method for surface defect detection of bottle caps. Neurocomputing, 2014, 123: 406–414 https://doi.org/10.1016/j.neucom.2013.07.038
8	Bai X, Fang Y, Lin W, Wang L, Ju B F. Saliency-based defect detection in industrial images by using phase spectrum. IEEE Transactions on Industrial Informatics, 2014, 10(4): 2135–2145 https://doi.org/10.1109/TII.2014.2359416
9	Tsai D M, Chiang I Y, Tsai Y H. A shift-tolerant dissimilarity measure for surface defect detection. IEEE Transactions on Industrial Informatics, 2012, 8(1): 128–137 https://doi.org/10.1109/TII.2011.2166797
10	Chan C H, Pang G K. Fabric defect detection by Fourier analysis. IEEE Transactions on Industry Applications, 2000, 36(5): 1267–1276 https://doi.org/10.1109/28.871274
11	Ngan H Y T, Pang G K H, Yung S P, Ng M K. Wavelet based methods on patterned fabric defect detection. Pattern Recognition, 2005, 38(4): 559–576 https://doi.org/10.1016/j.patcog.2004.07.009
12	Yang X, Pang G, Yung N. Robust fabric defect detection and classification using multiple adaptive wavelets. IEE Proceedings−Vision Image and Signal Processing, 2005, 152(6): 715 https://doi.org/10.1049/ip-vis:20045131
13	Ralló M, Millán M S, Escofet J. Unsupervised novelty detection using Gabor filters for defect segmentation in textures. Journal of the Optical Society of America A, Optics, Image Science, and Vision, 2009, 26(9): 1967–1976 https://doi.org/10.1364/JOSAA.26.001967 pmid: 19721681
14	Kumar A, Pang G K. Defect detection in textured materials using Gabor filters. IEEE Transactions on Industry Applications, 2002, 38(2): 425–440 https://doi.org/10.1109/28.993164
15	Wang C C, Jiang B C, Lin J Y, Chu C C. Machine vision-based defect detection in IC images using the partial information correlation coefficient. IEEE Transactions on Semiconductor Manufacturing, 2013, 26(3): 378–384 https://doi.org/10.1109/TSM.2013.2261566
16	Zontak M, Cohen I. Defect detection in patterned wafers using anisotropic kernels. Machine Vision and Applications, 2010, 21(2): 129–141 https://doi.org/10.1007/s00138-008-0146-y
17	Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking.In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999, 2: 246–252
18	Kaewtrakulpong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. Springer US, 2002: 135–144
19	Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of International Conference on Pattern Recognition, 2004

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