Fault identification in woven fabric using Back propagation neural network

Fault identification of Fabrics is an important job in regular inspection method in fabric industry. Fault identification systems include three phases. In an early stage image preprocessing has been carried by using Butterworth Low pass Filter to remove the hairiness noise in an image. Beside with perform Equalization to adjust pixel intensities to enhance contrast. After completion of preprocessing, Haralick defined texture attributes are obtained from the preprocessed data with the help of Gray Level Co-occurrence Matrix (GLCM). The Gray level spatial dependency matrix characterizes the allocation of co-occurring pixels in an image data at a given distance, position angle between pair of pixels. Then extracted features are used to train neural network classifier to identify the fabric defect using Back Propagation technique along with gradient descent learning algorithm. The performance of the Network is analyzed through different learning rate of the learning algorithm.