Classification of knitted fabric defect detection using Artificial Neural Networks

Abstract

Classification of defects in knitted fabric is an active area of research around the globe. This paper presents a classification method to detect defects such as holes and thick places in knitted fabric. The work has been carried out in two phases. In the first phase the images of the defective samples of two classes were collected by a high resolution camera. The colour images of the samples were converted into grey scale images. The features were extracted from each grey scale image and stored in a database. In the second phase a neural classifier was trained with error back-propagation algorithm on the training dataset. After successful training of the neural network on train dataset, the performance of the trained neural network was evaluated on the test dataset. Different experiments were carried out by increasing the no of training data samples, it was found that the best evaluation performance was obtained as 83.3%.