Deep learning based fabric defect detection

The use of deep learning approach in textile industry for defect detection purpose has become an increasing trend in the last twenty years. Majority of publications have investigated a specific problem in this field. Furthermore, many of published reviews or surveys articles preferred to investigate papers from a more general perspective. Compared with published review publications, this thesis is the first up-to-date study that investigates implementation of deep learning approaches for detection of fabric defects from 2003 to now. In this thesis, deep learning algorithms, VGG 19 and Capsule Networks, are also implemented. In addition, different variants of the auto-encoder method, which is a deep learning method, were also applied. As the main objectives of this thesis is to review deep learning based fabric defects detection and to implement deep learning, the publications regarding fabric defects detection by using deep learning are examined and experimental results are presented.