scholarly journals Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ali Rebhi ◽  
Issam Benmhammed ◽  
Sabeur Abid ◽  
Farhat Fnaiech
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Manufacturing Industry ◽  
Back Propagation ◽  
Labor Cost ◽  
Inspection System ◽  
Detection Scheme ◽  
Local Homogeneity ◽  
Fabric Defect Detection ◽  
Fabric Defect

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%.

scholarly journals Texture defect detection by using polynomial interpolation and multilayer perceptron

2019 ◽  
Vol 14 ◽  
pp. 155892501882527
Author(s):  
Sabeur Abid
Keyword(s):  
Defect Detection ◽  
Manufacturing Industry ◽  
Polynomial Interpolation ◽  
Detection System ◽  
Labor Cost ◽  
Fabric Defect Detection ◽  
Accuracy Comparison ◽  
Comparison Results ◽  
Fabric Defect ◽  
Texture Defect Detection

This article deals with fabric defect detection. The quality control in textile manufacturing industry becomes an important task, and the investment in this field is more than economical when reduction in labor cost and associated benefits are considered. This work is developed in collaboration with “PARTNER TEXTILE” company which expressed its need to install automated defect fabric detection system around its circular knitting machines. In this article, we present a new fabric defect detection method based on a polynomial interpolation of the fabric texture. The different image areas with and without defects are approximated by appropriate interpolating polynomials. Then, the coefficients of these polynomials are used to train a neural network to detect and locate regions of defects. The efficiency of the method is shown through simulations on different kinds of fabric defects provided by the company and the evaluation of the classification accuracy. Comparison results show that the proposed method outperforms several existing ones in terms of rapidity, localization, and precision.

Fabric Defect Detection Using Convolutional Neural Network

2020 ◽  
Vol 12 (05-SPECIAL ISSUE) ◽  
pp. 950-955
Author(s):  
Eldho Paul ◽  
Nivedha K ◽  
Nivethika M ◽  
Pavithra V ◽  
Priyadharshini G
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 70130-70140 ◽  
Author(s):  
Wenbin Ouyang ◽  
Bugao Xu ◽  
Jue Hou ◽  
Xiaohui Yuan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect
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