Fiber Defect Detection of Inhomogeneous Voluminous Textiles

  • Dirk SiegmundEmail author
  • Timotheos Samartzidis
  • Biying Fu
  • Andreas Braun
  • Arjan Kuijper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)


Quality assurance of dry cleaned industrial textiles is still a mostly manually operated task. In this paper, we present how computer vision and machine learning can be used for the purpose of automating defect detection in this application. Most existing systems require textiles to be spread flat, in order to detect defects. In contrast, we present a novel classification method that can be used when textiles are in inhomogeneous, voluminous shape. Normalization and classification methods are combined in a decision-tree model, in order to detect different kinds of textile defects. We evaluate the performance of our system in real-world settings with images of piles of textiles, taken using stereo vision. Our results show, that our novel classification method using key point pre-selection and convolutional neural networks outperform competitive methods in classification accuracy.


Local Binary Pattern Convolutional Neural Network Illumination Normalization Neural Network Classification Ambient Occlusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dirk Siegmund
    • 1
    Email author
  • Timotheos Samartzidis
    • 1
  • Biying Fu
    • 1
  • Andreas Braun
    • 1
  • Arjan Kuijper
    • 1
  1. 1.Fraunhofer Institute for Computer Graphics Research (IGD)DarmstadtGermany

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