Fabric defect detection using deep learning

Fabric defect detection have importance in terms of sectoral quality. Automatic systems are developed on the defect detection, in this regard many methods are tried to obtain high precision with image processing studies. In this study, deep learning which distinguishes with multi-layer architectures and reveals high achievement is applied to fabric defect detection. Autoencoder -a deep learning algorithm- that aimed to represent input data via compression or decompression is tried to detect defect of fabrics and it gains acceptable success. The vital goal of this study is to increase achievement of feature extraction by tuning up the autoencoder's input value and hyper parameters.

[1]  Chi-Ho Chan,et al.  Fabric defect detection by Fourier analysis , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[2]  Tao Yang,et al.  An Anomaly Detection Method Based On Deep Learning , 2015 .

[3]  Fei-Fei Li,et al.  Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Pascal Vincent,et al.  Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.

[5]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.