Fabric Defect Detection using a GA Tuned Wavelet Filter
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Date
2003-10-30
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Abstract
The purpose of this research project is to show that a computerized system based on image processing software is capable of identifying defects in woven fabrics. Current defect detection is carried out through use of visual inspection of fabric rolls after the rolls have been doffed from the production machinery, which adds a substantial lag between defect creation and detection. Existing methods for automatic defect detection rely on methods that suffer from substantial analysis time or a low percentage of detection. The method described in this thesis represents a quick and accurate approach to automatic defect detection and is capable of identifying defects such as lines, tears, and spots. Utilizing a Genetic Algorithm (GA) as the primary means of solving the wavelet filter equations with respect to a fabric image proved adequate in the construction of a wavelet filter that was capable of removing large amounts of the fabric texture from the image, thus allowing defect segmentation algorithms to run more effectively. Although a real-time system is not developed, suggestions for constructing such a system are presented. This work provides a foundation for the development of a real-time automated defect detector based on the algorithms and methodologies employed in this work.
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Keywords
Wavelet Filter, Defect Detection, Genetic Algorithm
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Degree
MS
Discipline
Textile Engineering
