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.

A fabric defect detection algorithm via context-based local texture saliency analysis

2015 ◽  
Vol 27 (5) ◽  
pp. 738-750 ◽  
Author(s):  
Zhoufeng Liu ◽  
Chunlei Li ◽  
Quanjun Zhao ◽  
Liang Liao ◽  
Yan Dong
Keyword(s):  
Defect Detection ◽  
Texture Features ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Image Texture ◽  
Content Type ◽  
Local Texture ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Saliency Analysis

Purpose – Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis. Design/methodology/approach – In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Findings – The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively. Originality/value – In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

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 Gabor filters and defect classification based on LBP and Tamura method

2013 ◽  
Vol 104 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Junfeng Jing ◽  
Huanhuan Zhang ◽  
Jing Wang ◽  
Pengfei Li ◽  
Jianyuan Jia
Keyword(s):  
Defect Detection ◽  
Gabor Filters ◽  
Defect Classification ◽  
Fabric Defect Detection ◽  
Fabric Defect

A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix

2019 ◽  
Vol 90 (7-8) ◽  
pp. 776-796 ◽  
Author(s):  
Feng Li ◽  
Lina Yuan ◽  
Kun Zhang ◽  
Wenqing Li
Keyword(s):  
Defect Detection ◽  
Detection Threshold ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Detection Scheme ◽  
Second Stage ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Different Characteristics

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.

A dual-branch balance saliency model based on discriminative feature for fabric defect detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhoufeng Liu ◽  
Menghan Wang ◽  
Chunlei Li ◽  
Shumin Ding ◽  
Bicao Li
Keyword(s):  
Defect Detection ◽  
Contextual Information ◽  
Experimental Results ◽  
Content Type ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Model Based ◽  
Saliency Model ◽  
Discriminative Feature ◽  
Fabric Defect

PurposeThe purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.Design/methodology/approachThis paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.FindingsTo evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.Originality/valueThe dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.

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