Conference paper Open Access

Probabilistic modelling combined with a CNN for boundary detection of carbon fiber fabrics

Sebastian Zambal; Christoph Heindl; Christian Eitzinger


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  <identifier identifierType="DOI">10.5281/zenodo.3373641</identifier>
  <creators>
    <creator>
      <creatorName>Sebastian Zambal</creatorName>
      <affiliation>PROFACTOR GmbH</affiliation>
    </creator>
    <creator>
      <creatorName>Christoph Heindl</creatorName>
      <affiliation>PROFACTOR GmbH</affiliation>
    </creator>
    <creator>
      <creatorName>Christian Eitzinger</creatorName>
      <affiliation>PROFACTOR GmbH</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Probabilistic modelling combined with a CNN for boundary detection of carbon fiber fabrics</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Deep learning, probabilistic modelling, U-Net</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-08-21</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3373641</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3373640</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Abstract:&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/721362/">721362</awardNumber>
      <awardTitle>Zero-defect manufacturing of composite parts in the aerospace industry</awardTitle>
    </fundingReference>
  </fundingReferences>
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