Application of Neural Networks (NNs) for Fabric Defect Classification

Abstract

The defect classification is as important as the defect detection in fabric inspection process. The detected defects are classified according to their types and recorded with their names during manual fabric inspection process. The material is selected as “undyed raw denim” fabric in this study. Four commonly occurring defect types, hole, warp lacking, weft lacking and soiled yarn, were classified by using artificial neural network (ANN) method. The defects were automatically classified according to their texture features. Texture feature extraction algorithm was developed to acquire the required values from the defective fabric samples. The texture features were assessed as the network input values and the defect classification is obtained as the output. The defective images were classified with an average accuracy rate of 96.3%. As the hole defect was recognized with 100% accuracy rate, the others were recognized with a rate of 95%.