Using a Neural Network to Identify Fabric Defects in Dynamic Cloth Inspection

Abstract

In this research, an image system is used as a tool for dynamic inspection of fabrics, and the inspection sample is a piece of plain white fabric. The four defects are holes, oil stains, warp-lacking, and weft-lacking. The image treatment employs a high-resolution linear scan digital camera. Fabric images are acquired first, then the images are transferred to a computer for analysis. Finally, the data are adopted as input data for a neural network, which is obtained from readings after treating the images. In this system, there are three feedforward networks, an input layer, one hidden layer, and an output layer. Because it has the ability to cope with the nonlinear regression property, this method can reinforce the effects of image identification.