Evaluation of defect identification algorithms for textile woven fabrics

Pao Yue-kong Library Electronic Theses Database

Evaluation of defect identification algorithms for textile woven fabrics

 

Author: Chan, Hing-wan
Title: Evaluation of defect identification algorithms for textile woven fabrics
Degree: M.Sc.
Year: 2002
Subject: Hong Kong Polytechnic University -- Dissertations
Textile fabrics
Department: Multi-disciplinary Studies
Dept. of Electronic Engineering
Pages: 141 leaves : ill. (some col.) ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1625991
URI: http://theses.lib.polyu.edu.hk/handle/200/1975
Abstract: In the textile industry, it is important to perform quality inspection during the production of woven fabrics. A good fabric inspection system can lower the production cost, increase the yield, improve the quality of finished products and ultimately increase the profit. At present, much of the fabric inspection is performed manually. An automatic fabric inspection system is desirable to improve the quality of woven fabrics and to lower the production cost. Furthermore, it provides consistent results that correlate with the quality control standard of the textile industry. An automatic fabric inspection system consists of an illumination system, a digital camera system, an intelligent defect detection software and an alarm system. This report will focus on the intelligent defect detection software. There are many types of fabrics and nearly 400 different kinds of defects. It is impossible to propose a mathematical model to fit in all situations. So, it may be more promising to combine several simple algorithms together. As a result, strengths of each algorithm can be complemented with each other to make them more powerful. Five algorithms have been studied in this report: subimage colour, subimage roughness, histogram analysis, aligned correlation coefficient and Fourier analysis. A Matlab program is developed to evaluate each algorithm's performance in fabric inspection. Each algorithm's optimum values of parameters, e.g. subimage size and number of histogram bins, are evaluated with explanations. Optimum defect detection rate of each algorithm for each defect is also listed out in tables. Applications and limitations of each algorithm are described and analysed in detail. This report will illustrate that combining these algorithms would obtain an efficient, effective and reliable fabric inspection system.

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