Deep learning-based fabric defect detection: A review

Abstract

The use of the deep learning approach in the textile industry for the purpose of defect detection has become an increasing trend in the past 20 years. The majority of publications have investigated a specific problem in this field. Furthermore, many of published reviews or survey articles preferred to investigate papers from a more general perspective. Compared with published review publications, this study is the first up-to-date study that investigates the implementation of deep learning approaches for the detection of fabric defects from 2003 to the present. As the main objective of this study is to review deep learning-based fabric defect detection, the publications regarding fabric defect detection by using deep learning are examined. The methods, database, performance rates, comparisons, and architecture type of these works were compared with each other. The most widely used deep learning architectures customized deep convolutional neural networks, long short-term memory, generative adversarial networks, and autoencoders. Besides the use of the most used deep learning algorithms, the advantages and disadvantages of these approaches have also been expressed.