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Fiber defect detection of inhomogeneous voluminous textiles

: Siegmund, Dirk; Samartzidis, Timotheos; Fu, Biying; Braun, Andreas; Kuijper, Arjan


Carrasco-Ochoa, Jesús Ariel (Ed.):
Pattern Recognition. 9th Mexican Conference, MCPR 2017 : Huatulco, Mexico, June 21-24, 2017, Proceedings
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10267)
ISBN: 978-3-319-59225-1 (Print)
ISBN: 978-3-319-59226-8 (Online)
Mexican Conference on Pattern Recognition (MCPR) <9, 2017, Huatulco>
Fraunhofer IGD ()
computer vision; machine learning; neural networks; quality assurance; textile industry; Guiding Theme: Digitized Work; Research Area: Computer vision (CV)

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.