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Article
Publication date: 16 March 2020

Chunlei Li, Chaodie Liu, Zhoufeng Liu, Ruimin Yang and Yun Huang

The 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…

187

Abstract

Purpose

The 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/approach

This 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.

Findings

The 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/value

The 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.

Details

International Journal of Clothing Science and Technology, vol. 32 no. 4
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 5 June 2017

Zhoufeng Liu, Lei Yan, Chunlei Li, Yan Dong and Guangshuai Gao

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…

289

Abstract

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.

Details

International Journal of Clothing Science and Technology, vol. 29 no. 3
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 14 December 2021

Zhoufeng Liu, Menghan Wang, Chunlei Li, Shumin Ding and Bicao Li

The 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…

120

Abstract

Purpose

The 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/approach

This 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.

Findings

To 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/value

The 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.

Details

International Journal of Clothing Science and Technology, vol. 34 no. 3
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 21 June 2021

Zhoufeng Liu, Shanliang Liu, Chunlei Li and Bicao Li

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep…

282

Abstract

Purpose

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field.

Design/methodology/approach

To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically.

Findings

The paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector).

Research limitations/implications

Our proposed algorithm can provide a promising tool for fabric defect detection.

Practical implications

The paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change.

Social implications

This work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrial

Originality/value

Therefore, our proposed algorithm can provide a promising tool for fabric defect detection.

Details

International Journal of Clothing Science and Technology, vol. 34 no. 2
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 1 August 2016

Chunlei Li, Ruimin Yang, Zhoufeng Liu, Guangshuai Gao and Qiuli Liu

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…

292

Abstract

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.

Details

International Journal of Clothing Science and Technology, vol. 28 no. 4
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 7 September 2015

Zhoufeng Liu, Chunlei Li, Quanjun Zhao, Liang Liao and Yan Dong

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…

291

Abstract

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.

Details

International Journal of Clothing Science and Technology, vol. 27 no. 5
Type: Research Article
ISSN: 0955-6222

Keywords

Available. Content available
Article
Publication date: 7 September 2015

George Stylios

202

Abstract

Details

International Journal of Clothing Science and Technology, vol. 27 no. 5
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 8 January 2019

Haishan Liang, Wei Sun, M.M. Fonseka and Feng Zhou

The purpose of this paper is to investigate the relationships between different types of team goal orientations (team learning orientation, team prove orientation and team avoid…

472

Abstract

Purpose

The purpose of this paper is to investigate the relationships between different types of team goal orientations (team learning orientation, team prove orientation and team avoid orientation) and team performance in new product development (NPD) and how these relationships are mediated by team absorptive capacity.

Design/methodology/approach

Data were collected through two surveys from 71 NPD teams and analyzed by the confirmatory factor analysis, correlation and hierarchical regression analysis methods.

Findings

The authors find that both teams’ learning and prove orientations are positively related to their absorptive capacity, which leads to increasing team performance in NPD. Further, the authors find support for the mediating role of team absorptive capacity in connecting team learning orientation and team prove orientation with team performance in NPD.

Practical implications

For practitioners, this paper suggests that to benefit from their NPD team efforts, firms with innovative aspirations should consider their existing and desired access to external knowledge sources and particularly the extent to which they can successfully integrate external knowledge with their internal knowledge structure.

Originality/value

The explication of team absorptive capacity is as a key mechanism through which different goal orientations of NPD teams inform the ability to successfully develop new products. By integrating the concepts of team goal orientations, team absorptive capacity and team performance in NPD, the authors seek to gain a better understanding of why some firms are more likely to do better than others in NPD. Findings of this paper extend concept of the nomological network on how absorptive capacity may serve as a direct outcome of different goal orientations. This paper responds to how Chinese firms can increase their innovative performance by infusing their current knowledge bases with external knowledge and extends the literature on knowledge management and managerial ties on innovation.

Details

Chinese Management Studies, vol. 13 no. 2
Type: Research Article
ISSN: 1750-614X

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Article
Publication date: 6 October 2023

Aoxiang Qiu, Weimin Sang, Feng Zhou and Dong Li

The paper aims to expand the scope of application of the lattice Boltzmann method (LBM), especially in the field of aircraft engineering. The traditional LBM is usually applied…

98

Abstract

Purpose

The paper aims to expand the scope of application of the lattice Boltzmann method (LBM), especially in the field of aircraft engineering. The traditional LBM is usually applied to incompressible flows at a low Reynolds number, which is not sufficient to satisfy the needs of aircraft engineering. Devoted to tackling the defect, the paper proposes a developed LBM combining the subgrid model and the multiple relaxation time (MRT) approach. A multilayer adaptive Cartesian grid method to improve the computing efficiency of the traditional LBM is also employed.

Design/methodology/approach

The subgrid model and the multilayer adaptive Cartesian grid are introduced into MRT-LBM for simulations of incompressible flows at a high Reynolds number. Validated by several typical flow simulations, the numerical methods in this paper can efficiently study the flows under high Reynolds numbers.

Findings

Some numerical simulations for the lid-driven flow of cavity, flow around iced GLC305, LB606b and ONERA-M6 are completed. The paper presents the investigation results, indicating that the methods are accurate and effective for the separated flow after icing.

Originality/value

LBM is developed with the addition of the subgrid model and the MRT method. A numerical strategy is proposed using a multilayer adaptive Cartesian grid method and its treatment of boundary conditions. The paper refers to innovative algorithm developments and applications to the aircraft engineering, especially for iced wing simulations with flow separations.

Details

Engineering Computations, vol. 40 no. 9/10
Type: Research Article
ISSN: 0264-4401

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