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.

scholarly journals A Block-Processing Approach Using Texture Analysis for Fabric Defect Detection

2020 ◽  
Author(s):  
Luiz Antonio Buschetto ◽  
Felipe Vieira Roque ◽  
Luan Casagrande ◽  
Tiago Oliveira Weber ◽  
Cristian Cechinel
Keyword(s):  
Texture Analysis ◽  
Local Binary Pattern ◽  
Search Algorithm ◽  
Support Vector ◽  
Fabric Defect Detection ◽  
Processing Approach ◽  
Extraction Step ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Grid Search Algorithm

The quality control is an essential step in fabric industries. Detectdefects in the early stages can reduce costs and increase the qualityof the products. Currently, this task is mainly done by humans,whose judgment can be affected by fatigue. Computer vision-basedtechniques can automatically detect defects, reducing the need forhuman intervention. In this context, this work proposes an imageblock-processing approach, where we compare the Segmentation-Based Fractal Texture Analysis, Gray Level Co-Occurrence Matrix,and Local Binary Pattern in the feature extraction step. Aimingto show the efficiency of this approach for the problem, these resultswere compared with the same algorithms without the blockprocessingapproach. A Support Vector Machine optimized by Grid-Search Algorithm was used to classify the fabrics. The databaseused, which is available online, is composed of 479 images fromsamples with defects and without it. The results show that thisblock processing approach can improve the classification results,achieving 100% in this work.

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.

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