A Novel Fabric Defect Detection Scheme Based on Improved Local Binary Pattern Operator

2010 ◽  
Author(s):  
Liu Zhoufeng ◽  
Gao Erjin ◽  
Li Chunlei
Keyword(s):  
Defect Detection ◽  
Local Binary Pattern ◽  
Detection Scheme ◽  
Fabric Defect Detection ◽  
Fabric Defect

Fabric defect detection based on sparse representation of main local binary pattern

2017 ◽  
Vol 29 (3) ◽  
pp. 282-293 ◽  
Author(s):  
Zhoufeng Liu ◽  
Lei Yan ◽  
Chunlei Li ◽  
Yan Dong ◽  
Guangshuai Gao
Keyword(s):  
Defect Detection ◽  
Local Binary Pattern ◽  
Detection Algorithm ◽  
Research Approach ◽  
Content Type ◽  
Fabric Defect Detection ◽  
Central Pixel ◽  
Fabric Defect ◽  
The Difference ◽  
Scale Difference

Purpose The purpose of this paper is to find an efficient fabric defect detection algorithm by means of exploring the sparsity characteristics of main local binary pattern (MLBP) extracted from the original fabric texture. Design/methodology/approach In the proposed algorithm, original LBP features are extracted from the fabric texture to be detected, and MLBP are selected by occurrence probability. Second, a dictionary is established with MLBP atoms which can sparsely represent all the LBP. Then, the value of the gray-scale difference between gray level of neighborhood pixels and the central pixel, and the mean of the difference which has the same MLBP feature are calculated. And then, the defect-contained image is reconstructed as normal texture image. Finally, the residual is calculated between reconstructed and original images, and a simple threshold segmentation method can divide the residual image, and the defective region is detected. Findings The experiment result shows that the fabric texture can be more efficiently reconstructed, and the proposed method achieves better defect detection performance. Moreover, it offers empirical insights about how to exploit the sparsity of one certain feature, e.g. LBP. Research limitations/implications Because of the selected research approach, the results may lack generalizability in chambray. Therefore, researchers are encouraged to test the proposed propositions further. Originality/value In this paper, a novel fabric defect detection method which extracts the sparsity of MLBP features is proposed.

scholarly journals A Hybrid Method of Textile Defect Detection using GLCM, LBP, SVD and Wavelet Transform

2020 ◽  
Vol 8 (6) ◽  
pp. 5356-5360
Keyword(s):  
Defect Detection ◽  
Local Binary Pattern ◽  
Defect Inspection ◽  
Image Processing Techniques ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Value Decomposition ◽  
Processing Techniques ◽  
Detection And Localization

A roll of fabric with defects can have a depreciation of 45 to 65% with respect to the original price. While some commercial solutions exist, automatic fabric defect detection remains an active field of development and research. The goal is to extract the characteristics of the texture of the fabric to detect defects contained using image processing techniques. To date, there is no standard method which ensures the detection of texture defects in fabrics with high precision. In the following work, the use of Singular Value Decomposition (SVD), Local Binary Pattern (LBP) and GrayLevel Co-Occurrence Matrix (GLCM) features of images for the identification of defects in textiles is presented, where the application of techniques for pre-processing is presented, and for the analysis of texture LBP and the GLCM in order to extract features and segmentation is done using SVD approach. This model makes it possible to obtain compact and precise detection of the faulty texture structures. Our method is capable of achieving very precise detection and localization of texture defects in the images of the Fabric-Defect-Inspection-GLSR database, while ensuring a reasonable processing time.

Texture Defect Detection Using Gabor Filters

2012 ◽  
Vol 229-231 ◽  
pp. 1176-1179
Author(s):  
Peng Fei Li ◽  
Huan Huan Zhang ◽  
Xue Juan Kang ◽  
Jun Feng Jing
Keyword(s):  
Defect Detection ◽  
Texture Features ◽  
Gabor Filters ◽  
Experimental Results ◽  
Detection Scheme ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Texture Defect Detection ◽  
Regular Texture

This paper aims at investigating a method for tackling the problem of automated fabric defect detection. A new scheme for defect detection using Gabor filters was proposed. In the experiment, texture features is extracted using Gabor filters. The proposed method would automatically segment defects from the regular texture. The experimental results showed the effectiveness of the proposed detection scheme.

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%.

Machine Vision Window Division on Self-Adaptation

2011 ◽  
Vol 460-461 ◽  
pp. 617-620
Author(s):  
Xiu Chen Wang
Keyword(s):  
Image Analysis ◽  
Machine Vision ◽  
Defect Detection ◽  
Actual Situation ◽  
Adaptive Window ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Self Adaptation ◽  
Recognition Efficiency ◽  
Division Model

Aiming at time-consuming and ineffective problem of image window division in fabric defect detection, this paper proposes a new adaptive division method after a large number of experiments. This method can quickly and exactly recognize defect feature. Firstly, a division model on adaptive window is established, secondly, the formula to anticipate generally situation of fabric image is given according to the peaks and valleys change in the model, and methods to calculate the division size and position of adaptive window are given. Finally, we conclude that the algorithm in this paper can quickly and simply select the size and position of window division according to actual situation of different fabric images, and the time of image analysis is shortened and the recognition efficiency is improved.

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