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.

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 Automatic Fabric Defect Detection Method Using PRAN-Net

2020 ◽  
Vol 10 (23) ◽  
pp. 8434
Author(s):  
Peiran Peng ◽  
Ying Wang ◽  
Can Hao ◽  
Zhizhong Zhu ◽  
Tong Liu ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Ground Truth ◽  
Inspection Time ◽  
Feature Maps ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Feature Pyramid ◽  
Quality Process

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

Fabric defect detection method based on cascaded low-rank decomposition

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.

Fabric Defect Detection via Multi-scale Feature Fusion-Based Saliency

2021 ◽  
pp. 240-251
Author(s):  
Zhoufeng Liu ◽  
Ning Huang ◽  
Chunlei Li ◽  
Zijing Guo ◽  
Chengli Gao
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Scale Feature ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect

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.

scholarly journals Automatic Fabric Defect Detection Using Cascaded Mixed Feature Pyramid with Guided Localization

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 871 ◽  
Author(s):  
You Wu ◽  
Xiaodong Zhang ◽  
Fengzhou Fang
Keyword(s):  
Defect Detection ◽  
Large Scale ◽  
Region Of Interest ◽  
Feature Maps ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Feature Pyramid ◽  
Set Up

Generic object detection algorithms for natural images have been proven to have excellent performance. In this paper, fabric defect detection on optical image datasets is systematically studied. In contrast to generic datasets, defect images are multi-scale, noise-filled, and blurred. Back-light intensity would also be sensitive for visual perception. Large-scale fabric defect datasets are collected, selected, and employed to fulfill the requirements of detection in industrial practice in order to address these imbalanced issues. An improved two-stage defect detector is constructed for achieving better generalization. Stacked feature pyramid networks are set up to aggregate cross-scale defect patterns on interpolating mixed depth-wise block in stage one. By sharing feature maps, center-ness and shape branches merges cascaded modules with deformable convolution to filter and refine the proposed guided anchors. After balanced sampling, the proposals are down-sampled by position-sensitive pooling for region of interest, in order to characterize interactions among fabric defect images in stage two. The experiments show that the end-to-end architecture improves the occluded defect performance of region-based object detectors as compared with the current detectors.

Fabric defect detection via learned dictionary-based visual saliency

2016 ◽  
Vol 28 (4) ◽  
pp. 530-542 ◽  
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.

Fabric defect detection based on multi-source feature fusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhoufeng Liu ◽  
Shanliang Liu ◽  
Chunlei Li ◽  
Bicao Li
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
New Method ◽  
Detection Accuracy ◽  
Single Shot ◽  
Content Type ◽  
Fabric Defect Detection ◽  
Promising Tool ◽  
Fabric Defect

PurposeThis 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/approachTo 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.FindingsThe 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/implicationsOur proposed algorithm can provide a promising tool for fabric defect detection.Practical implicationsThe 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 implicationsThis 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 industrialOriginality/valueTherefore, our proposed algorithm can provide a promising tool for fabric defect detection.

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