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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    <subfield code="a">Deep learning, probabilistic modelling, U-Net</subfield>
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    <subfield code="d">22-25 July 2019</subfield>
    <subfield code="g">INDIN'19</subfield>
    <subfield code="a">IEEE International Conference on Industrial Informatics</subfield>
    <subfield code="c">Helsinki-Espoo, Finland</subfield>
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    <subfield code="u">PROFACTOR GmbH</subfield>
    <subfield code="a">Christoph Heindl</subfield>
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    <subfield code="u">PROFACTOR GmbH</subfield>
    <subfield code="a">Christian Eitzinger</subfield>
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    <subfield code="s">3547665</subfield>
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    <subfield code="c">2019-08-21</subfield>
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    <subfield code="a">Sebastian Zambal</subfield>
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    <subfield code="a">Probabilistic modelling combined with a CNN for boundary detection of carbon fiber fabrics</subfield>
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  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">721362</subfield>
    <subfield code="a">Zero-defect manufacturing of composite parts in the aerospace industry</subfield>
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    <subfield code="a">Other (Non-Commercial)</subfield>
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    <subfield code="a">&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;</subfield>
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    <subfield code="a">10.5281/zenodo.3373640</subfield>
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