10.5281/zenodo.3373641
https://zenodo.org/records/3373641
oai:zenodo.org:3373641
Sebastian Zambal
Sebastian Zambal
PROFACTOR GmbH
Christoph Heindl
Christoph Heindl
PROFACTOR GmbH
Christian Eitzinger
Christian Eitzinger
PROFACTOR GmbH
Probabilistic modelling combined with a CNN for boundary detection of carbon fiber fabrics
Zenodo
2019
Deep learning, probabilistic modelling, U-Net
2019-08-21
10.5281/zenodo.3373640
https://zenodo.org/communities/eu
Other (Non-Commercial)
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.
European Commission
10.13039/501100000780
721362
Zero-defect manufacturing of composite parts in the aerospace industry