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Abstract
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Article Information:
Artificial Neural Network Techniques in Identifying Plain Woven Fabric Defects
P. Banumathi and G.M. Nasira
Corresponding Author: P. Banumathi
Submitted: July 27, 2014
Accepted: September 20, 2014
Published: February 05, 2015 |
Abstract:
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Textile industry is one of the main sources of revenue generating industry. The price of fabrics is severely affected by the defects of fabrics that represent a major threat to the textile industry. In manual inspection a very small percentage of defects are detected with highly trained, experienced inspectors. An automatic defect detection system can increase the defect detection percentage. It reduces the fabrication cost and economically profitable when we consider the labor cost and associated benefits. In this study we have proposed a method to detect the defects in woven fabric based on the changes in the intensity of fabric. The images are acquired; preprocessed, statistical features based on the gray level co-occurrence matrix are extracted. The Artificial Neural Network is used as classification model. The extracted features are given as input to the artificial neural network, it identifies the defect. The result of proposed method shows that a better performance achieved with less time when compared with the existing methods.
Key words: Artificial Neural Networks, defect detection, gray level co-occurrence matrix, image processing, statistical approach, ,
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Cite this Reference:
P. Banumathi and G.M. Nasira, . Artificial Neural Network Techniques in Identifying Plain Woven Fabric Defects. Research Journal of Applied Sciences, Engineering and Technology, (4): 272-276.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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