An Empirical Approach to Optimize Design of Backpropagation Neural Network Classifier for Textile Defect Inspection

Automated fabric inspection systems have been drawing plenty of attention of the researchers in order to replace manual inspection. Two difficult problems are mainly posed by automated fabric inspection systems. They are defect detection and defect classification. Backpropagation is a popular learning algorithm and very promising for defect classification. In general, works reported to date have claimed varying level of successes in detection and classification of different types of defects through backpropagation model. In those published works, no investigation has been reported regarding to the variation of major performance parameters of neural network (NN) based classifiers such as training time and classification accuracy based on network topology and training parameters. As a result, application engineer has little or no guidance to take design decisions for reaching to optimum structure of NN based defect classifiers in general and backpropagation based in particular. Our work focuses on empirical investigation of interrelationship between design parameters and performance of backpropagation based classifier for textile defect classification. It is believed that such work will be laying the ground to empower application engineers to decide about optimum values of design parameters for realizing most appropriate backpropagation based classifier.