publication . Other literature type . Conference object . Article . 2019

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

Christian Eitzinger; Sebastian Zambal; Christoph Heindl;
Open Access
  • Published: 21 Aug 2019
  • Publisher: Zenodo
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.
free text keywords: Deep learning, probabilistic modelling, U-Net, Real image, Architecture, Computer vision, Rendering (computer graphics), Deep learning, Convolutional neural network, Artificial intelligence, business.industry, business, Boundary detection, Probabilistic modelling, Carbon fibers, visual_art.visual_art_medium, visual_art, Computer science
Related Organizations
Funded by
EC| ZAero
Zero-defect manufacturing of composite parts in the aerospace industry
  • Funder: European Commission (EC)
  • Project Code: 721362
  • Funding stream: H2020 | IA
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Other literature type . 2019
Provider: Datacite
Conference object . 2019
Provider: ZENODO
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