APPLYING AN ARTIFICIAL NEURAL-NETWORK TO PATTERN-RECOGNITION IN FABRIC DEFECTS

In this paper, we evaluate the efficiency and accuracy of a method of detecting fabric defects that have been classified into different cate gories by a neural network. Four kinds of fabric defects most likely t o be found during weaving were learned by the network. Based on the pr inciple of the back-propagation algorithm of learning rule, fabric def ects could be detected and classified exactly. The method used for pro cessing image feature extraction is a co-occurrence-based method, by w hich six feature parameters are obtained. All of them consist of contr ast measurements, which involve three spatial displacements (i.e., 1, 12, 16) and four directions (0, 45, 90, 135 degrees) of fabric defects ' images used for classification. The results show that fabric defects inspected by means of image recognition in accordance with the artifi cial neural network agree approximately with initial expectations.