Fabric defect detection algorithm based on MFS and SVM

2018 ◽  
Author(s):  
Cuifang Zhao ◽  
Yu Chen ◽  
Jiacheng Ma
Keyword(s):  
Defect Detection ◽  
Detection Algorithm ◽  
Fabric Defect Detection ◽  
Fabric Defect

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.

Defect Detection Study of Fabric Based on Gabor Filter Algorithm

2011 ◽  
Vol 697-698 ◽  
pp. 491-494
Author(s):  
G.X. Li ◽  
Y.F. Li
Keyword(s):  
Iterative Algorithm ◽  
Defect Detection ◽  
Gabor Filter ◽  
Detection Algorithm ◽  
Algorithm Analysis ◽  
Post Processing ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Multichannel Data ◽  
Defect Image

This thesis exploits a multichannel Gabor filters detection algorithm. Analysis filtering images from different orientations and scales, then fuses the multichannel data. Finally, a threshold iterative algorithm and mathematical morphology post-processing is used to achieve the fabric defect detection. The experiment selects five types of fabric defect image. Experimental results suggest that this algorithm can effectively identify blob-shaped, linear and planar defect and has well real-time character.

Fabric defect detection algorithm using RDPSO-based optimal Gabor filter

2018 ◽  
Vol 110 (4) ◽  
pp. 487-495 ◽  
Author(s):  
Yueyang Li ◽  
Haichi Luo ◽  
Miaomiao Yu ◽  
Gaoming Jiang ◽  
Honglian Cong
Keyword(s):  
Defect Detection ◽  
Gabor Filter ◽  
Detection Algorithm ◽  
Fabric Defect Detection ◽  
Fabric Defect
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