Textural Fabric Defect Detection using Adaptive Quantized Gray-level Co-occurrence Matrix and Support Vector Description Data

A new texture-feature description operator, called the multidirectional binary patterns (MDBP) operator, is proposed in this paper. The operator can extract the detailed distribution of textures in local regions by comparing the differences in the gray levels between neighboring pixels. Moreover, the texture expression ability is enhanced by focusing on the texture features in the linear neighborhood of the image in multiple directions. The MDBP operator was modified by introducing a “uniform” pattern to reduce the grayscale values in the image. Combining the “uniform” MDBP operator and the gray-level co-occurrence matrix, an unpatterned fabric-defect detection scheme is proposed, including texture-feature extraction and detection stages. In the first stage, the multidirectional texture-feature matrix of a nondefective fabric image is extracted, and then the detection threshold is determined based on the similarity between the feature matrices. In the second stage, the defect is detected with the detection threshold. The proposed method is adapted to various grayscale textile images with different characteristics and is robust to a wide variety of image-processing operations. In addition, it is invariant to grayscale changes, performs well when representing textures and detecting defects and has lower computational complexity than other methods.

Download Full-text

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

10.14210/cotb.v11n1.p221-228 ◽

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.

Download Full-text

An Efficient Fabric Defect Prediction Based on Modular Neural Network Classifier with Alternative Hard C-Means Clustering

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.27.17892 ◽

2018 ◽

Vol 7 (3.27) ◽

pp. 277

Author(s):

S Rathinavel ◽

T Kannaianl

Keyword(s):

Neural Network ◽

Defect Detection ◽

Woven Fabrics ◽

Support Vector ◽

Back Propagation Algorithm ◽

Skilled Workers ◽

Modular Neural Network ◽

Fabric Defect Detection ◽

Switching Median Filter ◽

Fabric Defect

In India, textile industry has been mainly focused because it increased the economy day by day. But, it has some problem in the field of quality control. At present, it is mainly solved visually through skilled workers. Though, due to the human errors and eye fatigue, the system reliability has been restricted. So, in this research has been focused automatic fabric defect detection scheme. Here, Modular Neural Network (MNN) is proposed for fabric defect detection and classification with low cost and high accurate rate via using image processing schemes in the woven fabrics. At first, the images are collected from the machine and then preprocessed by using Enhanced Directional Switching Median Filter (EDWF) to reduce the impulse and stationary noise. To attain high accurate prediction, the preprocessed image has been segmented by using Alternative Hard C-Means (AHCM) cluster. After clustering, the images are converted to binary image. Then, the first order features has been extracted from the image. The extracted features are given as input to MNN, which classifies the fabric defects. In MNN, the weight factors are calculated by using back propagation algorithm and generate the output. The simulation results show that the proposed MNN attained high accuracy rate of 96.7% when compared to existing Artificial Neural Network (ANN) than Support Vector Machine with Genetic Algorithm (SVM-GA) classification algorithms.

Download Full-text

Fabric defect detection based on multiple fractal features and support vector data description

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2008.05.006 ◽

2009 ◽

Vol 22 (2) ◽

pp. 224-235 ◽

Cited By ~ 69

Author(s):

Hong-gang Bu ◽

Jun Wang ◽

Xiu-bao Huang

Keyword(s):

Defect Detection ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Fabric Defect Detection ◽

Fabric Defect

Download Full-text

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

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9569.038620 ◽

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.

Download Full-text