Artificial Neural Network Technique, Statistical and FFT in Identifying Defect in Plain Woven Fabric

Abstract

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. A very small percentage of defects are detected by the manual inspection even 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 paper 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 co-occurrence matrix and linear correlation coefficient using fast fourier transform 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 proposed method shows a better performance when compared with the existing methods.