Table of Contents
Journal of Photonics
Volume 2015 (2015), Article ID 376163, 9 pages
Research Article

Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network

Scientific Research Laboratory in Signal, Image Processing and Energy Mastery (SIME), University of Tunis, 5 Avenue Taha Hussein, 1008 Tunis, Tunisia

Received 12 August 2014; Accepted 8 December 2014

Academic Editor: Ivan Moreno

Copyright © 2015 Ali Rebhi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the textile manufacturing industry, fabric defect detection becomes a necessary and essential step in quality control. The investment in this field is more than economical when reduction in labor cost and associated benefits are considered. Moreover, the development of a wholly automated inspection system requires efficient and robust algorithms. To overcome this problem, in this paper, we present a new fabric defect detection scheme which uses the local homogeneity and neural network. Its first step consists in computing a new homogeneity image denoted as -image. The second step is devoted to the application of the discrete cosine transform (DCT) to the -image and the extraction of different representative energy features of each DCT block. These energy features are used by the back-propagation neural network to judge the existence of fabric defect. Simulations on different fabric images and different defect aspects show that the proposed method achieves an average accuracy of 97.35%.