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An Integrated Deep Neural Network for Defect Detection in Dynamic Textile Textures

  • Dirk Siegmund
  • Ashok Prajapati
  • Florian Kirchbuchner
  • Arjan Kuijper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11047)

Abstract

This paper presents a comprehensive defect detection method for two common fabric defects groups. Most existing systems require textiles to be spread out in order to detect defects. This method can be applied when the textiles are not spread out and does not require any pre- processing. The deep learning architecture we present is based on transfer learning and localizes and recognizes cuts, holes and stain defects. Classification and localization is combined into a single system combining two different networks. The experiments this paper presents show that even without adding depth information, the network was able to distinguish between stain and shadow. This method has been successful even for textiles in voluminous shape and is less computationally intensive than other state-of-the-art methods.

Notes

Acknowledgments

This work was supported by the German Federal Ministry of Education and Research (BMBF) as well as by the Hessen State Ministry for Higher Education, Research and the Arts (HMWK) within CRISP.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dirk Siegmund
    • 1
    • 2
  • Ashok Prajapati
    • 1
    • 2
  • Florian Kirchbuchner
    • 1
    • 2
  • Arjan Kuijper
    • 1
    • 2
  1. 1.Fraunhofer Institute for Computer Graphics Research (IGD)DarmstadtGermany
  2. 2.Technische Universität DarmstadtDarmstadtGermany

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