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Automated Textile Defect Recognition System Using Computer Vision and Artificial Neural Networks

Authors: Atiqul Islam, Shamim Akhter, Tumnun E. Mursalin


Least Development Countries (LDC) like Bangladesh, whose 25% revenue earning is achieved from Textile export, requires producing less defective textile for minimizing production cost and time. Inspection processes done on these industries are mostly manual and time consuming. To reduce error on identifying fabric defects requires more automotive and accurate inspection process. Considering this lacking, this research implements a Textile Defect Recognizer which uses computer vision methodology with the combination of multi-layer neural networks to identify four classifications of textile defects. The recognizer, suitable for LDC countries, identifies the fabric defects within economical cost and produces less error prone inspection system in real time. In order to generate input set for the neural network, primarily the recognizer captures digital fabric images by image acquisition device and converts the RGB images into binary images by restoration process and local threshold techniques. Later, the output of the processed image, the area of the faulty portion, the number of objects of the image and the sharp factor of the image, are feed backed as an input layer to the neural network which uses back propagation algorithm to compute the weighted factors and generates the desired classifications of defects as an output.

Keywords: Computer vision, image acquisition device, machine vision, multi-layer neural networks.

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[1] M. Ralló, M. S. Millán, J. Escofet, "Wavelet based techniques for textile inspection", Opt. Eng. 26(2), 838-844 (2003)
[2] R. Meier, "Uster Fabriscan, The Intelligent Fabric Inspection,"
[Online document], cited 20 Apr. 2005], Available HTTP:
[3] R. Stojanovic, P. Mitropulos, C. Koulamas, Y. A. Karayiannis, S. Koubias, and G. Papadopoulos, "Real-time Vision based System for Textile Fabric Inspection", Real-Time Imaging, vol. 7, no. 6, 2001, pp. 507-518.
[4] R. C. Gonzalez, R. E. Woods, S. L. Eddins, "Digital Image Processing using MATLAB", ISBN 81-297-0515-X, 2005, pp. 76-104,142- 166,404-407
[5] M. T. Hagan, H. B. Demuth, M. Beale, "Neural Network Design", ISBN 981-240-376-0, 2002, part 2.5, 10.8
[6] Riedmiller, M., and H. Braun, "A direct adaptive method for faster backpropagation learning: The RPROP algorithm", Proceedings of the IEEE International Conference on Neural Networks, 1993.
[7] Neural Network Toolbox, "MATLAB -The Language of Technical Conputing",
[CD Document], Version, 2004
[8] B. G. Batchelor and P. F. Whelan, "Selected Papers on Industrial Machine Vision Systems," SPIE Milestone Series, 1994.
[9] T. S. Newman and A. K. Jain, "A Survey of Automated Visual Inspection," Computer Vision and Image Understanding, vol. 61, 1995, pp. 231-262.
[10] H Zhang, J. Guan and G. C. Sun, "Artificial Neural Network-Based Image Pattern Recognition", ACM 30th Annual Southeast Conference, 1992
[11] Ciamberlini C., Francini F., Longobardi G., Sansoni P., Tiribilli, B. "Defect detection in textured materials by optical filtering with structured detectors and selfadaptable masks", Opt. Eng. 35(3), 838- 844 (1996)
[12] Kang T.J. et al. "Automatic Recognition of Fabric Weave Patterns by Digital Image Analysis", Textile Res. J. 69(2), 77-83 (1999)
[13] Kang T.J. et al. "Automatic Structure Analysis and Objective Evaluation of Woven Fabric Using Image Analysis", Textile Res. J. 71(3), 261-270 (2001)
[14] Jasper W.J., Garnier S.J., Potlapalli H., "Texture characterization and defect detection using adaptive wavelets", Opt. Eng. 35(11), 3140- 3149 (1996)
[15] Jasper W.J., Potlapalli H., "Image analysis of mispicks in woven fabric", Text. Res.J. 65(1), 683-692 (1995)
[16] Escofet J., Navarro R., Mill├ín M.S., Pladellorens J., "Detection of local defects in textile webs using Gabor filters", in "Vision Systems: New Image Processing Techniques" Ph. Réfrégier, ed. Proceedings SPIE vol. 2785, 163-170 (1996)
[17] Escofet J., Navarro R., Millán M.S., Pladellorens J., "Detection of local defects in textile webs using Gabor filters", Opt. Eng. 37(8) 2297-2307 (1998)
[18] Millán M.S., Escofet J., "Fourier domain based angular correlation for quasiperiodic pattern recognition. Applications to web inspection", Appl. Opt. 35(31), 6253-6260 (1996)
[19] T. Martin, M. Jones, J. Edmison, T. Sheikh and Z. Nakad,"Modeling and Simulating Electronic Textile Applications", LCTES, USA, 2004
[20] A. Dockery, "Automatic Fabric Inspection: Assessing the Current State of the Art,"
[Online document], 2001,
[cited 29 Apr. 2005], Available HTTP:
[21] Y. Ji, K. H. Chang and CC. Hung, "Efficient Edge Detection and Object Segmentation Using Gabor Filters", ACMSE, USA, 2004.