2022/10/10 by Yavuz Kahraman, Alptekin Durmuşoğlu · 98 citations
Engineering · Materials Science · Mathematics · #Artificial intelligence #Computer science #Convolutional neural network #Deep learning #Field (mathematics) #Industrial Vision Systems and Defect Detection #Integrated Circuits and Semiconductor Failure Analysis #Machine learning #Mathematics #Textile materials and evaluations
paper · doi:10.1177/00405175221130773
published in Textile Research Journal 93(5-6), 1485-1503 (SAGE Publishing)
crossref issued 2022/10/10 · crossref published 2022/10/10 · crossref published-online 2022/10/10 · openalex publication_date 2022/10/10 · crossref created 2022/10/11 · crossref published-print 2023/03/01 · openalex created_date 2025/10/10 · crossref deposited 2026/05/01 · crossref indexed 2026/08/05 · openalex updated_date 2026/08/06
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.