Araştırma Makalesi
BibTex RIS Kaynak Göster

REAL-TIME DETECTION OF KNITTING FABRIC DEFECTS USING SHEARLET TRANSFORM

Yıl 2019, Cilt: 29 Sayı: 1, 1 - 10, 26.03.2019
https://doi.org/10.32710/tekstilvekonfeksiyon.482888

Öz

This paper proposes a vision-based
fabric inspection system for the circular knitting machine. Firstly, a
comprehensive fabric database called Fabric Defect Detection Database (FDDD) are
constructed. To extract significant features of fabric images, shearlet
transform is used. Means and variances are calculated from all subbands and
combined into a high-dimensional feature vector. The proposed system is evaluated
on a circular knitting machine in a textile factory. The real-time performance
analysis is only carried out by inspecting single jersey knitted fabric. Our
proposed system achieves the highest accuracy of 94.0% in the detection of single
jersey knitting fabric defects.

Kaynakça

  • [1] Hanbay K., Talu M. F., Özgüven Ö F., Öztürk D., 2015, Fabric defect detection methods for circular knitting machines, In: 23nd Signal Processing and Communications Applications Conference (SIU), p:735-738
  • [2] Raheja J.L., Ajay B., Chaudhary A., 2013, Real time fabric defect detection system on an embedded DSP platform, Opt. Int. J. Light Electron. Opt., V.124, No.21, p: 5280–5284
  • [3] Hanbay K., Talu M. F., Özgüven Ö F., 2016, Fabric defect detection systems and methods—A systematic literature review, Opt. Int. J. Light Electron. Opt., V.127, No.24, p:11960-11973
  • [4] Salem, Y., Nasri, S., 2010, Automatic recognition of woven fabrics based on texture and using svm, Signal Image Video Process., V.4, No.4, p:429–434
  • [5] Ismail N., Syahrir W.M., Zain J.M., Hai T., 2011, Fabric authenticity method using fast Fourier transformation detection, 2011, International Conference on Electrical, Control and Computer Engineering (INECCE), p:233–237
  • [6] Hanmandlu M., Choudhury D., Dash S., 2015, Detection of defects in fabrics using topothesy fractal dimension features, Signal Image and Video Processing, V.9, No.7, p:1521–1530
  • [7] Li Y., Ai J., Sun C., 2013, Online Fabric Defect Inspection Using Smart Visual Sensors, Sensors, V. 13, No. 4, p:4659-4673
  • [8] Tsai D., Huang T, 2003, Automated surface inspection for statistical textures, Image Vis. Comput., V.21, p:307-323
  • [9] Hu G.H., Wang Q.H., Zhang G.H., 2015, Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage, Appl. Opt., V.54, p: 2963-2980
  • [10] Karlekar V. V., Biradar M. S., Bhangale K. B., 2015, Fabric defect detection using wavelet filter, International Conference on Computing Communication Control and Automation, p: 712-715
  • [11] Kumar A., 2003, Neural network based detection of local textile defects, Pattern Recognit., V. 36, p: 1645-1659
  • [12] Kang Z., Yuan C., Yang Q., 2013, The fabric defect detection technology based on wavelet transform and neural network convergence, IEEE International Conference on Information and Automation (ICIA), p: 597-601.
  • [13] Zhang Y., Lu Z., Li J., 2010, Fabric defect classification using radial basis function network, Pattern Recognition Letters, V. 31, No. 13, p: 2033-2042,
  • [14] Liu X., Mei W., Du H., Bei J., 2016, A novel image fusion algorithm based on nonsubsampled shearlet transform and morphological component analysis, Signal Image Video Processing, V.10, No.5, p:959-966
  • [15] Sengur A., 2008, An expert system based on principal component analysis, artificial immune system and fuzzy kk-NN for diagnosis of valvular heart diseases, Computers in Biology and Medicine, V.38, No.3, p:329–338
  • [16] Hanbay K., Talu M. F., Özgüven Ö F., Öztürk D., 2017, Real time fabric defect detection by using fourier transform, Journal of the Faculty of Engineering and Architecture of Gazi University, V.32 No.1,p:151-158
  • [17] Cooley J.W., Tukey J.W., 1965, An algorithm for the machine calculation of complex fourier series, Mathematics of Computation, V.19, No.90, p:297–301
  • [18 Malek A. S., Drean J. Y., Bigue L., Osselin J. F., 2013, Optimization of automated online fabric inspection by fast Fourier transform (FFT) and cross-correlation, Textile Research Journal, V.83, No.3, p:256-268
  • [19] Do M.D., Vetterli M., 2002, Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance, IEEE Transactions on Image Processing, V.11, No.2, p:146-158
  • [20] Abdi H., Williams L. J., 2010, Principal component analysis, Wiley Interdiscip. Rev. Comput. Stat., Vol.2 No.4, p: 433-459
  • [21] Marrocco C., Duin R.P.W., Tortorella F., 2008, Maximizing the area under the ROC curve by pairwise feature combination, Pattern Recognition, V.41, No.6, p:1961-1974
  • [22] Zhou S., Shi J., Zhu J., Cai Y., Wang R., 2013, Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image, Biomedical Signal Processing and Control, V.8, No.6, p:688-696
  • [23] Kargı V. S. A., 2014, A comparison of artificial neural networks and multiple linear regression models as in predictors of fabric weft defects, Tekstil ve Konfeksiyon, V.24, No.3, p:309 –316
Yıl 2019, Cilt: 29 Sayı: 1, 1 - 10, 26.03.2019
https://doi.org/10.32710/tekstilvekonfeksiyon.482888

Öz

Kaynakça

  • [1] Hanbay K., Talu M. F., Özgüven Ö F., Öztürk D., 2015, Fabric defect detection methods for circular knitting machines, In: 23nd Signal Processing and Communications Applications Conference (SIU), p:735-738
  • [2] Raheja J.L., Ajay B., Chaudhary A., 2013, Real time fabric defect detection system on an embedded DSP platform, Opt. Int. J. Light Electron. Opt., V.124, No.21, p: 5280–5284
  • [3] Hanbay K., Talu M. F., Özgüven Ö F., 2016, Fabric defect detection systems and methods—A systematic literature review, Opt. Int. J. Light Electron. Opt., V.127, No.24, p:11960-11973
  • [4] Salem, Y., Nasri, S., 2010, Automatic recognition of woven fabrics based on texture and using svm, Signal Image Video Process., V.4, No.4, p:429–434
  • [5] Ismail N., Syahrir W.M., Zain J.M., Hai T., 2011, Fabric authenticity method using fast Fourier transformation detection, 2011, International Conference on Electrical, Control and Computer Engineering (INECCE), p:233–237
  • [6] Hanmandlu M., Choudhury D., Dash S., 2015, Detection of defects in fabrics using topothesy fractal dimension features, Signal Image and Video Processing, V.9, No.7, p:1521–1530
  • [7] Li Y., Ai J., Sun C., 2013, Online Fabric Defect Inspection Using Smart Visual Sensors, Sensors, V. 13, No. 4, p:4659-4673
  • [8] Tsai D., Huang T, 2003, Automated surface inspection for statistical textures, Image Vis. Comput., V.21, p:307-323
  • [9] Hu G.H., Wang Q.H., Zhang G.H., 2015, Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage, Appl. Opt., V.54, p: 2963-2980
  • [10] Karlekar V. V., Biradar M. S., Bhangale K. B., 2015, Fabric defect detection using wavelet filter, International Conference on Computing Communication Control and Automation, p: 712-715
  • [11] Kumar A., 2003, Neural network based detection of local textile defects, Pattern Recognit., V. 36, p: 1645-1659
  • [12] Kang Z., Yuan C., Yang Q., 2013, The fabric defect detection technology based on wavelet transform and neural network convergence, IEEE International Conference on Information and Automation (ICIA), p: 597-601.
  • [13] Zhang Y., Lu Z., Li J., 2010, Fabric defect classification using radial basis function network, Pattern Recognition Letters, V. 31, No. 13, p: 2033-2042,
  • [14] Liu X., Mei W., Du H., Bei J., 2016, A novel image fusion algorithm based on nonsubsampled shearlet transform and morphological component analysis, Signal Image Video Processing, V.10, No.5, p:959-966
  • [15] Sengur A., 2008, An expert system based on principal component analysis, artificial immune system and fuzzy kk-NN for diagnosis of valvular heart diseases, Computers in Biology and Medicine, V.38, No.3, p:329–338
  • [16] Hanbay K., Talu M. F., Özgüven Ö F., Öztürk D., 2017, Real time fabric defect detection by using fourier transform, Journal of the Faculty of Engineering and Architecture of Gazi University, V.32 No.1,p:151-158
  • [17] Cooley J.W., Tukey J.W., 1965, An algorithm for the machine calculation of complex fourier series, Mathematics of Computation, V.19, No.90, p:297–301
  • [18 Malek A. S., Drean J. Y., Bigue L., Osselin J. F., 2013, Optimization of automated online fabric inspection by fast Fourier transform (FFT) and cross-correlation, Textile Research Journal, V.83, No.3, p:256-268
  • [19] Do M.D., Vetterli M., 2002, Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance, IEEE Transactions on Image Processing, V.11, No.2, p:146-158
  • [20] Abdi H., Williams L. J., 2010, Principal component analysis, Wiley Interdiscip. Rev. Comput. Stat., Vol.2 No.4, p: 433-459
  • [21] Marrocco C., Duin R.P.W., Tortorella F., 2008, Maximizing the area under the ROC curve by pairwise feature combination, Pattern Recognition, V.41, No.6, p:1961-1974
  • [22] Zhou S., Shi J., Zhu J., Cai Y., Wang R., 2013, Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image, Biomedical Signal Processing and Control, V.8, No.6, p:688-696
  • [23] Kargı V. S. A., 2014, A comparison of artificial neural networks and multiple linear regression models as in predictors of fabric weft defects, Tekstil ve Konfeksiyon, V.24, No.3, p:309 –316
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Kazim Hanbay

Muhammed Fatih Talu Bu kişi benim

Ömer Faruk Özgüven Bu kişi benim

Dursun Öztürk Bu kişi benim

Yayımlanma Tarihi 26 Mart 2019
Gönderilme Tarihi 8 Aralık 2017
Kabul Tarihi 1 Eylül 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 29 Sayı: 1

Kaynak Göster

APA Hanbay, K., Talu, M. F., Özgüven, Ö. F., Öztürk, D. (2019). REAL-TIME DETECTION OF KNITTING FABRIC DEFECTS USING SHEARLET TRANSFORM. Textile and Apparel, 29(1), 1-10. https://doi.org/10.32710/tekstilvekonfeksiyon.482888

No part of this journal may be reproduced, stored, transmitted or disseminated in any forms or by any means without prior written permission of the Editorial Board. The views and opinions expressed here in the articles are those of the authors and are not the views of Tekstil ve Konfeksiyon and Textile and Apparel Research-Application Center.