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Fabric Defect Detection using Local Homogeneity Analysis and Neural Network


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

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 H-image. The second step is devoted to the application of the discrete cosine transform (DCT) to the H-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%.
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  • Fabric Defect Detection using Local Homogeneity Analysis and Neural Network

Abstract Views: 47  |  PDF Views: 1

Authors

Ali Rebhi
Scientific Research Laboratory in Signal, Image Processing and Energy Mastery (SIME), University of Tunis, 5 Avenue Taha Hussein, 1008 Tunis, Tunisia
Issam Benmhammed
Scientific Research Laboratory in Signal, Image Processing and Energy Mastery (SIME), University of Tunis, 5 Avenue Taha Hussein, 1008 Tunis, Tunisia
Sabeur Abid
Scientific Research Laboratory in Signal, Image Processing and Energy Mastery (SIME), University of Tunis, 5 Avenue Taha Hussein, 1008 Tunis, Tunisia
Farhat Fnaiech
Scientific Research Laboratory in Signal, Image Processing and Energy Mastery (SIME), University of Tunis, 5 Avenue Taha Hussein, 1008 Tunis, Tunisia

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 as H-image. The second step is devoted to the application of the discrete cosine transform (DCT) to the H-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%.