Defect Detection in Fabric using Wavelet Transform and Genetic Algorithm
Journal Title: Transactions on Machine Learning and Artificial Intelligence - Year 2015, Vol 3, Issue 6
Abstract
Fabric defect detection is one of the indispensable units in the manufacturing industry to maintain the quality of the end product. Wavelet transform is well suited for quality inspection application due to its multi-resolution representation and to extract fabric features. In this paper a new scheme is proposed for fabric defect detection in textile industry. For this purpose, all coefficients were extracted from perfect fabric. These coefficients can defect main fabric image & indicate defects of fabric textile by optimal subset of these coefficients. For finding defects a suitable subset of Genetic Algorithm is used in this process. The Shannon entropy is used as evaluation function in Genetic Algorithm By using two separable sets of wavelet coefficients for horizontal and vertical defects, it was seen that we get better results for defect detection. The advantage of this approach is that it improves accuracy of fabric defect detection as well decreases computation time.
Authors and Affiliations
Depavath Harinath, K. Ramesh Babu, M. V. Ramana Murthy
Keywords
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- EP ID EP278783
- DOI 10.14738/tmlai.36.1551
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