Automated Textile Defect Recognition System Using Computer Vision and Artificial Neural Networks
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.
[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:
http://www.kotonline.com/english_pages/ana_basliklar/uster.asp
[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 7.0.0.19920(R14), 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., Millán 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.
[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:
http://www.kotonline.com/english_pages/ana_basliklar/uster.asp
[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 7.0.0.19920(R14), 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., Millán 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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:61941", author = "Atiqul Islam and Shamim Akhter and Tumnun E. Mursalin", title = "Automated Textile Defect Recognition System Using Computer Vision and Artificial Neural Networks", abstract = "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.", volume = "2", number = "1", pages = "73-6", }