Neural network based detection of local textile defects

Research output: Journal article publicationJournal articleAcademic researchpeer-review

168 Citations (Scopus)


A new approach for the segmentation of local textile defects using feed-forward neural network is presented. Every fabric defect alters the gray-level arrangement of neighboring pixels, and this change is used to segment the defects. The feature vector for every pixel is extracted from the gray-level arrangement of its neighboring pixels. Principal component analysis using singular value decomposition is used to reduce the dimension of feature vectors. Experimental results using this approach illustrate a high degree of robustness for the detection of a variety of fabric defects. The acceptance of a visual inspection system depends on economical aspects as well. Therefore, a new low-cost solution for the fast web inspection using linear neural network is also presented. The experimental results obtained from the real fabric defects, for the two approaches proposed in this paper, have confirmed their usefulness.
Original languageEnglish
Pages (from-to)1645-1659
Number of pages15
JournalPattern Recognition
Issue number7
Publication statusPublished - 1 Jan 2003
Externally publishedYes


  • Automated visual inspection
  • Defect detection
  • Machine vision
  • Neural networks
  • Quality assurance

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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