Year: 2015
Journal of Fiber Bioengineering and Informatics, Vol. 8 (2015), Iss. 1 : pp. 195–206
Abstract
Two methods are proposed in this paper to inspect printed fabrics. One method is to apply a genetic algorithm to select parameters of optimal Gabor filter. Optimal Gabor filter can reduce the noise information of printed fabrics, which can achieve defect detection of printed fabrics. The other is in utilizing distance matching function to determine the unit of printed fabrics. Extracting features on a moving unit of printed fabrics can realize defect segmentation of printed fabrics. Two approaches of defect detection have their own advantages. Detecting method with Gabor filter using genetic algorithm has perfect detection results of random printed fabrics, the other method based on statistical rule can receive better defect detection results of regular printed fabrics. Both methods can be realized in practice and detection time of proposed methods can occupy little in total detection time.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.3993/jfbi03201519
Journal of Fiber Bioengineering and Informatics, Vol. 8 (2015), Iss. 1 : pp. 195–206
Published online: 2015-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 12
Keywords: Defect Detection
-
A new approach to detect surface defects from 3D point cloud data with surface normal Gabor filter (SNGF)
Lee, Eddie Taewan | Fan, Zhaoyan | Sencer, BurakJournal of Manufacturing Processes, Vol. 92 (2023), Iss. P.196
https://doi.org/10.1016/j.jmapro.2023.02.047 [Citations: 15] -
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
Czimmermann, Tamás | Ciuti, Gastone | Milazzo, Mario | Chiurazzi, Marcello | Roccella, Stefano | Oddo, Calogero Maria | Dario, PaoloSensors, Vol. 20 (2020), Iss. 5 P.1459
https://doi.org/10.3390/s20051459 [Citations: 242] -
Detection of Defects in Warp Knitted Fabrics Based on Local Feature Scale Adaptive Comparison
Zhang, Yongchao | Shi, Weimin | Zhang, JindouApplied Sciences, Vol. 14 (2024), Iss. 22 P.10754
https://doi.org/10.3390/app142210754 [Citations: 0] -
An adaptive coarse-to-fine framework for automatic first article inspection of flexographic printing labels
Xiao, Pan | Yan, Shule | Long, Jinliang | Lin, Jianfa | Xiao, Meng | Cai, Nian | Chen, Xindu | Leng, JiewuExpert Systems with Applications, Vol. 227 (2023), Iss. P.120241
https://doi.org/10.1016/j.eswa.2023.120241 [Citations: 0] -
Applications of Computer Vision in Fashion and Textiles
Computer vision and its application in detecting fabric defects
Eldessouki, M.
2018
https://doi.org/10.1016/B978-0-08-101217-8.00004-X [Citations: 7] -
Deep Neural Network Models for the Recognition of Traffic Signs Defects
Nagy, Amr M. | Czuni, Laszlo2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), (2021), P.725
https://doi.org/10.1109/IDAACS53288.2021.9660936 [Citations: 0] -
Automatic defect detection for fabric printing using a deep convolutional neural network
Chakraborty, Samit | Moore, Marguerite | Parrillo-Chapman, LisaInternational Journal of Fashion Design, Technology and Education, Vol. 15 (2022), Iss. 2 P.142
https://doi.org/10.1080/17543266.2021.1925355 [Citations: 12] -
Fast and Parallel Summed Area Table for Fabric Defect Detection
Ragab, Khaled
International Journal of Pattern Recognition and Artificial Intelligence, Vol. 30 (2016), Iss. 09 P.1660004
https://doi.org/10.1142/S0218001416600041 [Citations: 4]