A hybrid model using genetic algorithm and neural network for classifying garment defects

C. W.M. Yuen, Wai Keung Wong, S. Q. Qian, L. K. Chan, E. H.K. Fung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

59 Citations (Scopus)


The inspection of semi-finished and finished garments is very important for quality control in the clothing industry. Unfortunately, garment inspection still relies on manual operation while studies on garment automatic inspection are limited. In this paper, a novel hybrid model through integration of genetic algorithm (GA) and neural network is proposed to classify the type of garment defects. To process the garment sample images, a morphological filter, a method based on GA to find out an optimal structuring element, was presented. A segmented window technique is developed to segment images into several classes using monochrome single-loop ribwork of knitted garment. Four characteristic variables were collected and input into a back-propagation (BP) neural network to classify the sample images. According to the experimental results, the proposed method achieves very high accuracy rate of recognition and thus provides decision support in defect classification.
Original languageEnglish
Pages (from-to)2037-2047
Number of pages11
JournalExpert Systems with Applications
Issue number2 PART 1
Publication statusPublished - 1 Mar 2009


  • Garment inspection
  • Genetic algorithms
  • Image segmentation
  • Morphological filter
  • Neural network

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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