Differential evolution-based optimal Gabor filter model for fabric inspection

Le Tong, Wai Keung Wong, Chun Kit Kwong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

67 Citations (Scopus)

Abstract

In this paper, a defect detection model using optimized Gabor filters, which is suitable for real-time operation, is proposed to tackle the woven fabric inspection problem in fashion industry. Based on the analysis of the particular characteristics of fabric defects, the proposed model utilizes composite differential evolution (CoDE) to optimize the parameters of Gabor filters, which can achieve the optimal feature extraction of fabric defects. Together with thresholding and fusion operations, the optimal Gabor filters can successfully segment the defects from the original image background. By using optimal Gabor filters instead of a Gabor filter bank, the computational cost of the detection model can be significantly reduced. The performance of the proposed defect detection model is evaluated off-line through extensive experiments based on various types of fabric. Experimental results reveal that the proposed detection model is effective and robust, and is superior than four existing models in terms of the high detection rate and low false alarm rate.
Original languageEnglish
Pages (from-to)1386-1401
Number of pages16
JournalNeurocomputing
Volume173
DOIs
Publication statusPublished - 15 Jan 2016

Keywords

  • Defect detection
  • Differential evolution
  • Fabric quality inspection
  • Optimal Gabor filters

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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