Conference paper Open Access

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

Sebastian Zambal; Christoph Heindl; Christian Eitzinger

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.

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