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.

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 A fabric defect detection method based on deep learning

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Qiang Liu ◽  
Chuan Wang ◽  
Yusheng Li ◽  
Mingwang Gao ◽  
Jingao Li
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Detection Method ◽  
Fabric Defect Detection ◽  
Fabric Defect

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 based on Improved Faster RCNN

2021 ◽  
Vol 12 (04) ◽  
pp. 23-32
Author(s):  
Yuan He ◽  
Han-Dong Zhang ◽  
Xin-Yue Huang ◽  
Francis Eng Hock Tay
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Production Process ◽  
Product Quality ◽  
Defect Detection ◽  
Classification Accuracy ◽  
Detection Method ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Computer Vision Technology

In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.

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
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