The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 711–720, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-711-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 711–720, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-711-2022
 
30 May 2022
30 May 2022

IMAGE-BASED DEEP LEARNING FOR RHEOLOGY DETERMINATION OF BINGHAM FLUIDS

A. Ponick1, A. Langer1, D. Beyer2, M. Coenen2, M. Haist2, and C. Heipke1 A. Ponick et al.
  • 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany
  • 2Institute of Building Materials Science, Leibniz Universität Hannover, Germany

Keywords: Rheology, Building Materials, Stereo View, Deep Learning

Abstract. In this work, a method to predict the rheological properties of ultrasonic gel, as a reference substance of cement paste, is presented. For this purpose, images are taken with a stereo camera system which show a mixing paddle moving through the ultrasonic gels of different consistency, thus setting them in motion. A digital elevation model (DEM) and a corresponding orthophoto are created from the image pairs using classical image matching and orthoprojection methods. These are used as inputs into a Convolutional Neural Network (CNN), which predicts the support points of a flow curve which classically have to be determined in a rheometer in the laboratory. A simple network architecture consisting of a small number of convolution layers is compared with a pre-trained ResNet-18, which is fine-tuned using gel images. In a second series of experiments, rheological parameters, which alternatively need to be deduced from the flow curve in a separate step, are determined directly from the images. In the third series of experiments, the influence of different factors is tested, such as the position of the cameras relative to the direction of paddle movement and the importance of the DEMs and orthophotos in the training. It is shown in this paper that it is possible to predict the rheological properties of the ultrasonic gels with a suitable setup with a satisfying accuracy.