Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network. (17th March 2015)

• Record Type: Journal Article
• Authors: Rebhi, Ali; Benmhammed, Issam; Abid, Sabeur; Fnaiech, Farhat
• Other Names: Moreno Ivan Academic Editor.
• Is Part Of: Journal of photonics. Volume 2015(2015)
• Publication Date: 2015-03-17
• Subjects: Photonics -- Periodicals; Photonics; Periodicals; 621.365
• DOI: 10.1155/2015/376163
• Languages: English
• ISSNs: 2356-7562

Abstract

Abstract : 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 asH -image. The second step is devoted to the application of the discrete cosine transform (DCT) to theH -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%.