Fabric defect detection using local contrast deviations

Authors:
Meihong Shi;Rong Fu;Yong Guo;Shixian Bai;Bugao Xu
Affiliations:
School of Computer Science, Xi'an Polytechnic University, Xi'an, People's Republic of China 710048;School of Computer Science, Xi'an Polytechnic University, Xi'an, People's Republic of China 710048;School of Computer Science, Xi'an Polytechnic University, Xi'an, People's Republic of China 710048;School of Computer Science, Xi'an Polytechnic University, Xi'an, People's Republic of China 710048;Department of Human Ecology, University of Texas at Austin, Austin, USA 78712
Venue:
Multimedia Tools and Applications
Year:
2011

Citing 4
Cited 0

Automated Inspection of Textile Fabrics Using Textural Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
2-D Iteratively Reweighted Least Squares Lattice Algorithm and Its Application to Defect Detection in Textured Images*This work has been partially supported by Turkish Technology Development Foundation under contract number TTGV-169.

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Using perceptual relation of regularity and anisotropy in the texture with independent component model for defect detection

Pattern Recognition
Fabric defect detection using modified local binary patterns

EURASIP Journal on Advances in Signal Processing

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Abstract

Defect inspection is a vital step for quality assurance in fabric production. The development of a fully automated fabric defect detection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to delicate features of defects complicated by variations in weave textures and changes in environmental factors (e.g., illumination, noise, etc.). Based on characteristics of fabric structure, an approach of using local contrast deviation (LCD) is proposed for fabric defect detection in this paper. LCD is a parameter used to describe features of the contrast difference in four directions between the analyzed image and a defect-free image of the same fabric, and is used with a bilevel threshold function for defect segmentation. The validation tests on the developed algorithms were performed with fabric images from TILDA's Textile Texture Database and captured by a line-scan camera on an inspection machine. The experimental results show that the proposed method has robustness and simplicity as opposed to the approach of using modified local binary patterns (LBP).