Fabric defect detection using deep learning [Derin Ögrenme ile Kumas Hatasi Tespiti]
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. © 2016 IEEE.