Fabric Inspection Based on Best Wavelet Packet Bases

Abstract

In this study, we use best wavelet packet bases and an artificial neural network (ANN) to inspect four kinds of fabric defects. Multiresolution representation of an image using wavelet transform is a new and effective approach for analyzing image information content. In this study, we find the values and positions for the smallest-six entropy in a wavelet packet best tree that acts as the feature parameters of the ANN for identifying fabric defects. We explore three basic considerations of the classification rate of fabric defect inspection comprising wavelets with various maximum vanishing moments, different numbers of resolution levels, and differently scaled fabric images. The results show that the total classification rate for a wavelet function with a maximum vanishing moment of four and three resolution levels can reach 100%, and differently scaled fabric images have no obvious effect on the classification rate.