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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    "description": "<p>Abstract:</p>\n\n<p>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.</p>", 
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    "title": "Probabilistic modelling combined with a CNN for boundary detection of carbon fiber fabrics", 
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        "title": "Zero-defect manufacturing of composite parts in the aerospace industry", 
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    "keywords": [
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    "publication_date": "2019-08-21", 
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        "name": "Sebastian Zambal"
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        "name": "Christoph Heindl"
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    "meeting": {
      "acronym": "INDIN'19", 
      "dates": "22-25 July 2019", 
      "place": "Helsinki-Espoo, Finland", 
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