scholarly journals Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

2019 ◽  
Vol 9 (17) ◽  
pp. 3506 ◽  
Author(s):  
Huanhuan Zhang ◽  
Jinxiu Ma ◽  
Junfeng Jing ◽  
Pengfei Li
Keyword(s):  
Fuzzy C Means ◽  
Fabric Defect Detection ◽  
Iterative Calculation ◽  
Fabric Defect ◽  
Background Texture ◽  
Objective Tests ◽  
L0 Gradient Minimization ◽  
Computational Resources ◽  
Vision Detection ◽  
Industrial Machine

In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources.

scholarly journals Fabric Defect Detection via Un-Decimated Wavelet Decomposition and Gumbel Distribution Model

2018 ◽  
Vol 13 (1) ◽  
pp. 155892501801300
Author(s):  
Guang Hua Hu ◽  
Qing Hui Wang
Keyword(s):  
Defect Detection ◽  
Wavelet Decomposition ◽  
Gumbel Distribution ◽  
Distribution Model ◽  
Feature Map ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Log Likelihood ◽  
Background Texture ◽  
Homogeneous Background

This paper investigates an unsupervised approach for fabric defect detection using un-decimated wavelet decomposition and simple statistical models. A novel data fusion scheme is proposed to merge the information from the different channels into a unique feature map in which potential defective regions will be highlighted distinctly. The distribution of the pixel values corresponding to the defect-free background texture in the feature map is modeled as per the Gumbel distribution model whose parameters are estimated by partitioning the feature map into a set of small patches. By calculating the log-likelihood value of each patch, a log-likelihood map (LLM) can be conveniently created, which provides a good cluster representation of the non-defective regions. A simple thresholding procedure then follows to discriminate between defective regions and homogeneous background in the LLM. The performance of the method has been extensively evaluated using a variety of real fabric samples, and the effectiveness of the proposed scheme has been verified by experimental results in comparison with other methods.

Machine Vision Window Division on Self-Adaptation

2011 ◽  
Vol 460-461 ◽  
pp. 617-620
Author(s):  
Xiu Chen Wang
Keyword(s):  
Image Analysis ◽  
Machine Vision ◽  
Defect Detection ◽  
Actual Situation ◽  
Adaptive Window ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Self Adaptation ◽  
Recognition Efficiency ◽  
Division Model

Aiming at time-consuming and ineffective problem of image window division in fabric defect detection, this paper proposes a new adaptive division method after a large number of experiments. This method can quickly and exactly recognize defect feature. Firstly, a division model on adaptive window is established, secondly, the formula to anticipate generally situation of fabric image is given according to the peaks and valleys change in the model, and methods to calculate the division size and position of adaptive window are given. Finally, we conclude that the algorithm in this paper can quickly and simply select the size and position of window division according to actual situation of different fabric images, and the time of image analysis is shortened and the recognition efficiency is improved.

A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion

2021 ◽  
Author(s):  
Zhengrui Peng ◽  
Xinyi Gong ◽  
Zhenfeng Lu ◽  
Xiangyi Xu ◽  
Bengang Wei ◽  
...  
Keyword(s):  
Defect Detection ◽  
Attention Mechanism ◽  
Fabric Defect Detection ◽  
Fabric Defect
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