Fabric Defect Detection with Cartoon–Texture Decomposition

  • Ying Lv
  • Xiaodong YueEmail author
  • Qiang Chen
  • Meiqian Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)


Automatic fabric defect detection plays an important role in textile industry. Most existing works utilize machine leaning methods to classify the fabric images with defects, however, because fabric defects are generally diverse and obscure. It is difficult to precisely identify the defects by direct image classifications. Aiming to tackle this problem, in this paper, we propose a two-stage method for automatic fabric defect detection. First, we utilize cartoon–texture decomposition to extract the features of textile structures from fabric images. Second, based on the features of cartoon textures, we build up a classifier with Deep Convolutional Neural Networks (DCNN) to distinguish the image regions containing defects, i.e., the regions of abnormal feature representation. Experimental results validate that the proposed method can precisely recognize the fabric defects and achieve good performances on the fabric images of various kinds of textiles.


Fabric defect detection Cartoon–texture decomposition Deep convolutional neural networks 



This work reported here was financially supported by the National Natural Science Foundation of China (Grant No. 61573235).


  1. 1.
    Mahajan, P.M., Kolhe, S.R., Patil, P.M.: A review of automatic fabric defect detection techniques. Adv. Comput. Res. 1(2), 18–29 (2009)Google Scholar
  2. 2.
    Hanbay, K., Talu, M.F., Ömer, F.: Fabric defect detection systems and methods—A systematic literature review. Optik—Int. J. Light and Electron Optics 127(24), 11960–11973 (2016)CrossRefGoogle Scholar
  3. 3.
    Ngan, H.Y.T., Pang, G.K.H., Yung, N.H.C.: Automated fabric defect detection—A review. Image Vis. Comput. 29(7), 442–458 (2011)CrossRefGoogle Scholar
  4. 4.
    Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Industr. Electron. 55(1), 348–363 (2008)CrossRefGoogle Scholar
  5. 5.
    Li, Y., Zhao, W., Pan, J.: Deformable patterned fabric defect detection with fisher criterion-based deep learning. IEEE Trans. Autom. Sci. Eng. 14(2), 1256–1264 (2017)CrossRefGoogle Scholar
  6. 6.
    Sayed, M.S.: Robust fabric defect detection algorithm using entropy filtering and minimum error thresholding. In: IEEE ISCAS. IEEE, pp. 1–4 (2017)Google Scholar
  7. 7.
    Buades, A., Le, T.M., Morel, J.M., et al.: Fast cartoon + texture image filters. IEEE Trans. Image Process. 19(8), 1978–1986 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Meyer, Y.: Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures. University Lecture Series (2001)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ying Lv
    • 2
  • Xiaodong Yue
    • 1
    • 2
    Email author
  • Qiang Chen
    • 2
  • Meiqian Wang
    • 2
  1. 1.Shanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghaiChina
  2. 2.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

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