A fabric defect detection algorithm via context-based local texture 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.

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Fabric defect detection method based on cascaded low-rank decomposition

International Journal of Clothing Science and Technology ◽

10.1108/ijcst-03-2019-0037 ◽

2020 ◽

Vol 32 (4) ◽

pp. 483-498

Author(s):

Chunlei Li ◽

Chaodie Liu ◽

Zhoufeng Liu ◽

Ruimin Yang ◽

Yun Huang

Keyword(s):

Defect Detection ◽

Detection Method ◽

Sparse Matrix ◽

Saliency Map ◽

Low Rank ◽

Content Type ◽

Fabric Defect Detection ◽

Fabric Defect ◽

Rank Decomposition ◽

Low Rank Decomposition

PurposeThe purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.Design/methodology/approachThis paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.FindingsThe proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.Originality/valueThe cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

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

International Journal of Clothing Science and Technology ◽

10.1108/ijcst-04-2016-0040 ◽

2017 ◽

Vol 29 (3) ◽

pp. 282-293 ◽

Cited By ~ 9

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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Fabric defect detection via learned dictionary-based visual saliency

International Journal of Clothing Science and Technology ◽

10.1108/ijcst-12-2015-0134 ◽

2016 ◽

Vol 28 (4) ◽

pp. 530-542 ◽

Cited By ~ 6

Author(s):

Chunlei Li ◽

Ruimin Yang ◽

Zhoufeng Liu ◽

Guangshuai Gao ◽

Qiuli Liu

Keyword(s):

Defect Detection ◽

Correlation Coefficients ◽

Visual Saliency ◽

Saliency Map ◽

Defect Region ◽

Image Block ◽

Content Type ◽

Fabric Defect Detection ◽

Learned Dictionary ◽

Fabric Defect

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 using learned dictionary-based visual saliency. Design/methodology/approach – First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region. Findings – Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region. Originality/value – In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.

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