Detection of Fabric Defects by Auto-Regressive Spectral Analysis and Support Vector Data Description

Abstract

For the purpose of realizing fast and effective detection of defects in woven fabric, and in consideration of the inherent characteristics of fabric texture, i.e., periodicity and orientation, a new approach for fabric texture analysis, based on the modern spectral analysis of a time series rather than the classical spectral analysis of an image, is proposed in this paper. Traditionally, a power spectral estimated by a two-dimensional Fast Fourier transformation (FFT) is usually employed in the detection of fabric defects, which involves a large computational complexity and a relatively low accuracy of spectral estimation. To this effect, this paper makes a one-dimensional power spectral density (PSD) analysis of the fabric image via a Burg-algorithm-based Auto-Regressive (AR) spectral estimation model, and accordingly extracts features capable of effectively differentiating normal textures from defective ones. A support vector data description is adopted as a detector in order to deal with defect detection, a typical task of one-class classification. Experimental results for the detection of defects from several fabric collections with different texture backgrounds indicate that a low false alarm rate and a low missing rate can be simultaneously obtained with less computational complexity. Comparison of the detection results between the AR model and the FFT method confirms the superiority of the proposed method.

Keywords

fabric defect detection time series analysis AR spectral estimation one-class classification support vector data description

Get full access to this article

View all access options for this article.

References

Chen, S.H. , and Fu, L.X. , "Practical Digital Image Processing", Science Press , Beijing, 2005, pp. 268-271.

Ruan, Q.Q. , "Digital Image Processing", Publishing House of Electronics Industry, Beijing, 2001 , pp. 420-421.

Tsai, I.S. , and Hu, M.C. , Automatic Inspection of Fabric Defects using an Artificial Neural Network Technique, Textil. Res. J., 66(7), 472-482 (1996).

Chan, C.H. , and Pang, G.K.H. , Fabric defect detection by Fourier analysis, IEEE Trans. Ind. Appl., 36(5), 1267-1276 (2000 ).

Chetverikov, D. , and Hanbury, A. , Finding Defects in Texture using Regularity and Local Orientation, Pattern. Recogn., 35, 203-218 ( 2002).

Thanagasundram, S. , and Schlindwein , F.S. Autoregressive based diagnostics scheme for detection of bearing faults, in "Proceedings of ISMA 2006: International conference on noise and vibration engineering", Leuven Belgium, September 18- 20, 2006, V1-8, 3531-3545.

Zhan, Y.M. , and Mechefske, C.K. , Robust Detection of Gearbox Deterioration using Compromised Autoregressive Modeling and Kolmogorov-Smirnov Test Statistic - Part I: Compromised Autoregressive Modeling with the Aid of Hypothesis Tests and Simulation Analysis, Mech. Syst. Signal. Process., 21(5), 1953-1982 ( 2007).

Zhang, X.D. , "Modern Signal Processing", 2nd edn, Tsinghua University Press, Beijing, 2002, pp. 65-115.

10.

Schölkopf, B. , Platt, J.C. , Shawe-Taylor , J. , Smola, A.J. and Williamson, R.C. , Estimating the Support of a High-Dimensional Distribution, Neural. Comput. , 13(7), 1443-1471 (2001 ).

11.

Tax, D.M.J. , and Duin, R.P.W. , Support Vector Data Description, Mach. Learn., 54, 45-66 (2004).

12.

Platt, J.C. , " Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines", Technical Report MSR-TR-98-14, Microsoft Research, 1998.

13.

Fan, R.E. , Chen, P.H. , and Lin. , C. J., Working Set Selection using the Second Order Information for Training SVM, J. Mach. Learn. Res., 6, 1889-1918 (2005).

14.

Shawe-Taylor, J. , and Cristianini, N. , "Kernel Methods for Pattern Analysis", Cambridge University Press, Cambridge, 2004 , pp. 296-297.

15.

Banerjee, A. , Burlina, P. , and Diehl, C. , A Support Vector Method for Anomaly Detection in Hyperspectral Imagery, IEEE Trans. Geosci. Rem. Sens., 2282-2291 44(8) (2006).

16.

Müller, K.R. , Mika, S. , Rätsch, G. , Tsuda, K. , and Schölkopf , B. , An Introduction to Kernel-Based Learning Algorithms, IEEE Trans. Neural Network. , 12(2), 181-201 (2001).

17.

Bu, H.G. , Wang, J. , and Huang, X.B. , Fabric Defects Detection Based on Multiple Fractal Features and Support Vector Data Description, Eng. Appl. Artif. Intell., 22(2), 224-235 ( 2009).

18.

Theodoridis, S. , and Koutroumbas , K. (writers), Li , J.J. , et al. (translators), "Pattern Recognition", 2nd edn, Publishing House of Electronics Industry, Beijing, 2004, pp. 107-115.

19.

Chang, C.C. , and Lin, C.J. , LIBSVM: a Library for Support Vector Machines, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.

20.

Chang, C.C. , and Lin, C.J. , LIBSVM: a Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf, Document last updated: February 9, 2007.