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An Enhanced Nonlocal Self-Similarity Technique for Fabric Defect Detection

Boheng Wang1, Li Ma1, Jielin Jiang1,2,*

1 School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
2 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology, Nanjing, 210044, China.
* Corresponding Author: Jielin Jiang. Email:

Journal of Information Hiding and Privacy Protection 2019, 1(3), 135-142.


Fabric defect detection has been an indispensable and important link in fabric production, many studies on the development of vision based automated inspection techniques have been reported. The main drawback of existing methods is that they can only inspect a particular type of fabric pattern in controlled environment. Recently, nonlocal self-similarity (NSS) based method is used for fabric defect detection. This method achieves good defect detection performance for small defects with uneven illumination, the disadvantage of NNS based method is poor for detecting linear defects. Based on this reason, we improve NSS based defect detection method by introducing a gray density function, namely an enhanced NSS (ENSS) based defect detection method. Meanwhile, mean filter is applied to smooth images and suppress noise. Experimental results prove the validity and feasibility of the proposed NLRA algorithm.


Cite This Article

B. Wang, L. Ma and J. Jiang, "An enhanced nonlocal self-similarity technique for fabric defect detection," Journal of Information Hiding and Privacy Protection, vol. 1, no.3, pp. 135–142, 2019.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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