August 21, 2019 Conference paper Open Access

Sebastian Zambal; Christoph Heindl; Christian Eitzinger

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  <dc:creator>Sebastian Zambal</dc:creator>
  <dc:creator>Christoph Heindl</dc:creator>
  <dc:creator>Christian Eitzinger</dc:creator>
  <dc:date>2019-08-21</dc:date>
  <dc:description>Abstract:

For many industrial machine vision applications it is difficult to acquire good training data to deploy deep learning techniques. In this paper we propose a method based on probabilistic modelling and rendering to generate artificial images of carbon fiber fabrics. We deploy a convolutional neural network (CNN) to learn detection of fabric contours from artificially generated images. Our network largely follows the recently proposed U-Net architecture. We provide results for a set of real images taken under controlled lighting conditions. The method can easily be adapted to similar problems in quality control for composite parts.</dc:description>
  <dc:identifier>https://zenodo.org/record/3373641</dc:identifier>
  <dc:identifier>10.5281/zenodo.3373641</dc:identifier>
  <dc:identifier>oai:zenodo.org:3373641</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/721362/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3373640</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:subject>Deep learning, probabilistic modelling, U-Net</dc:subject>
  <dc:title>Probabilistic modelling combined with a CNN for boundary detection of carbon fiber fabrics</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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