scholarly journals Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1064 ◽  
Author(s):  
Shuang Mei ◽  
Yudan Wang ◽  
Guojun Wen
Keyword(s):  
Network Model ◽  
Defect Detection ◽  
Denoising Autoencoder ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect

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 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

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.

scholarly journals Fabric defect detection using saliency of multi-scale local steering kernel

2020 ◽  
Vol 14 (7) ◽  
pp. 1265-1272 ◽  
Author(s):  
Kaibing Zhang ◽  
Yadi Yan ◽  
Pengfei Li ◽  
Junfeng Jing ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Defect Detection ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect

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.

scholarly journals Fabric defect detection based on deep-feature and low-rank decomposition

2020 ◽  
Vol 15 ◽  
pp. 155892502090302 ◽  
Author(s):  
Zhoufeng Liu ◽  
Baorui Wang ◽  
Chunlei Li ◽  
Miao Yu ◽  
Shumin Ding
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Low Rank ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Decomposition Model ◽  
Fabric Defect ◽  
Rank Decomposition ◽  
Low Rank Decomposition

Fabric defect detection plays an important role in controlling the quality of textile production. In this article, a novel fabric defect detection algorithm is proposed based on a multi-scale convolutional neural network and low-rank decomposition model. First, multi-scale convolutional neural network, which can extract the multi-scale deep feature of the image using multiple nonlinear transformations, is adopted to improve the characterization ability of fabric images with complex textures. The effective feature extraction makes the background lie in a low-rank subspace, and a sparse defect deviates from the low-rank subspace. Then, the low-rank decomposition model is constructed to decompose the feature matrix into the low-rank part (background) and the sparse part (salient defect). Finally, the saliency maps generated by the sparse matrix are segmented based on an improved optimal threshold to locate the fabric defect regions. Experimental results indicate that the feature extracted by the multi-scale convolutional neural network is more suitable for characterizing the fabric texture than the traditional hand-crafted feature extraction methods, such as histogram of oriented gradient, local binary pattern, and Gabor. The adopted low-rank decomposition model can effectively separate the defects from the background. Moreover, the proposed method is superior to state-of-the-art methods in terms of its adaptability and detection efficiency.

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