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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3373641", 
  "title": "Probabilistic modelling combined with a CNN for boundary detection of carbon fiber fabrics", 
  "issued": {
    "date-parts": [
      [
        2019, 
        8, 
        21
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Sebastian Zambal"
    }, 
    {
      "family": "Christoph Heindl"
    }, 
    {
      "family": "Christian Eitzinger"
    }
  ], 
  "type": "paper-conference", 
  "id": "3373641"
}
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