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  4. Fiber defect detection of inhomogeneous voluminous textiles
 
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2017
Conference Paper
Title

Fiber defect detection of inhomogeneous voluminous textiles

Abstract
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 realworld 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.
Author(s)
Siegmund, Dirk
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Samartzidis, Timotheos
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Fu, Biying  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Braun, Andreas
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
Pattern Recognition. 9th Mexican Conference, MCPR 2017  
Conference
Mexican Conference on Pattern Recognition (MCPR) 2017  
DOI
10.1007/978-3-319-59226-8_27
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • computer vision

  • machine learning

  • neural networks

  • quality assurance

  • textile industry

  • Lead Topic: Digitized Work

  • Research Line: Computer vision (CV)

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