scholarly journals Application of Neural Networks (NNs) for Fabric Defect Classification

10.5772/63427 ◽  
2016 ◽  
Author(s):  
H. Ibrahim Çelik ◽  
L. Canan Dülger ◽  
Mehmet Topalbekiroglu
Keyword(s):  
Neural Networks ◽  
Defect Classification ◽  
Fabric Defect

Moment-Based Features of Knitted Cotton Fabric Defect Classification by Artificial Neural Networks

2020 ◽  
pp. 1-9
Author(s):  
Subrata Das ◽  
Amitabh Wahi ◽  
S. Madhan Kumar ◽  
Ravi Shankar Mishra ◽  
S. Sundaramurthy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cotton Fabric ◽  
Defect Classification ◽  
Fabric Defect ◽  
Artificial Neural

scholarly journals Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 198600-198614
Author(s):  
Masood Aslam ◽  
Tariq M. Khan ◽  
Syed Saud Naqvi ◽  
Geoff Holmes ◽  
Rafea Naffa
Keyword(s):  
Neural Networks ◽  
Knowledge Transfer ◽  
Convolutional Neural Networks ◽  
Defect Classification ◽  
Industrial Settings

Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method

2013 ◽  
Vol 104 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Junfeng Jing ◽  
Huanhuan Zhang ◽  
Jing Wang ◽  
Pengfei Li ◽  
Jianyuan Jia
Keyword(s):  
Defect Detection ◽  
Gabor Filters ◽  
Defect Classification ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Texture classification of fabric defects using machine learning

2020 ◽  
Vol 10 (4) ◽  
pp. 4390
Author(s):  
Yassine Ben Salem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Texture Classification ◽  
Support Vector ◽  
Defect Classification ◽  
Analysis Method ◽  
Fabric Defect ◽  
Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

Yarn-dyed fabric defect classification based on convolutional neural network

2017 ◽  
Vol 56 (09) ◽  
pp. 1 ◽  
Author(s):  
Junfeng Jing ◽  
Amei Dong ◽  
Pengfei Li ◽  
Kaibing Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Classification ◽  
Fabric Defect ◽  
Dyed Fabric
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