An Empirical Study on Fabric Defect Classification Using Deep Network Models

  • Nguyen Thi Hong AnhEmail author
  • Bui Cong Giao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11814)


Fabric defect inspection plays an essential role in the textile manufacturing process. Traditional detection is carried out using defect visualization. This method obviously is inefficient in both accuracy and inspection time. Automatic detection, which is based on image processing and machine learning, has been proven to be a suitable approach for this problem. However, due to the variety of defect kinds in a broad range of deferent fabrics, existing methods are actually proposed for typical group of fabric defects. This paper aims to investigate models of deep neural network for the general fabric classification problem. In particular, two models including VGG16 and Darknet are used to classify the defect fabric categories. The models are tested for the TILDA database to evaluate their performances.


Fabric defect classification Deep neural network Supervised learning 



This work is funded by Saigon University, Ho Chi Minh City, Vietnam under the grant number [CS2018-68] (project contract No. 891/HD-QPTKHCN, dated 26/7/2018).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Electronics and TelecomminicationsSaigon UniversityHo Chi Minh CityVietnam

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