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

Authors:
C. W. M. Yuen;W. K. Wong;S. Q. Qian;L. K. Chan;E. H. K. Fung
Affiliations:
Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.