Wavelet based methods on patterned fabric defect detection

  • Authors:

  • Henry Y. T. Ngan;Grantham K. H. Pang;S. P. Yung;Michael K. Ng

  • Affiliations:

  • Department of Electrical and Electronic Engineering, Industrial Automation Research Laboratory, The University of Hong Kong, Pokfulam Road, Kowloon, Hong Kong;Department of Electrical and Electronic Engineering, Industrial Automation Research Laboratory, The University of Hong Kong, Pokfulam Road, Kowloon, Hong Kong;Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong;Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong

  • Venue:
  • Pattern Recognition
  • Year:
  • 2005

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Abstract

The wavelet transform (WT) has been developed over 20 years and successfully applied in defect detection on plain (unpatterned) fabric. This paper is on the use of the wavelet transform to develop an automated visual inspection method for defect detection on patterned fabric. A method called direct thresholding (DT) based on WT detailed subimages has been developed. The golden image subtraction method (GIS) is also introduced. GIS is an efficient and fast method, which can segment out the defective regions on patterned fabric effectively. In this paper, the method of wavelet preprocessed golden image subtraction (WGIS) has been developed for defect detection on patterned fabric or repetitive patterned texture. This paper also presents a comparison of the three methods. It can be concluded that the WGIS method provides the best detection result. The overall detection success rate is 96.7% with 30 defect-free images and 30 defective patterned images for one common kind of patterned Jacquard fabric.