Details
| Original language | English |
|---|---|
| Title of host publication | XXIV ISPRS Congress (2022 edition) |
| Pages | 711-720 |
| Number of pages | 10 |
| Publication status | Published - 30 May 2022 |
| Event | 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II - Nice, France Duration: 6 Jun 2022 → 11 Jun 2022 |
Publication series
| Name | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
|---|---|
| Publisher | International Society for Photogrammetry and Remote Sensing |
| Number | B2-2022 |
| Volume | 43 |
| ISSN (Print) | 1682-1750 |
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.
Keywords
- Building Materials, Deep Learning, Rheology, Stereo View
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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XXIV ISPRS Congress (2022 edition). 2022. p. 711-720 (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives; Vol. 43, No. B2-2022).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Image-based deep learning for rheology determination of bingham fluids
AU - Ponick, A.
AU - Langer, Amadeus
AU - Beyer, D.
AU - Coenen, M.
AU - Haist, M.
AU - Heipke, C.
N1 - Funding Information: This work is supported by the Federal Ministry of Education and Research of Germany (BMBF) as part of the research project ReCyControl [Project number 0336260A], https://www. recycontrol.uni-hannover.de/de/
PY - 2022/5/30
Y1 - 2022/5/30
N2 - 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.
AB - 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.
KW - Building Materials
KW - Deep Learning
KW - Rheology
KW - Stereo View
UR - http://www.scopus.com/inward/record.url?scp=85132039392&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLIII-B2-2022-711-2022
DO - 10.5194/isprs-archives-XLIII-B2-2022-711-2022
M3 - Conference contribution
AN - SCOPUS:85132039392
T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SP - 711
EP - 720
BT - XXIV ISPRS Congress (2022 edition)
T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II
Y2 - 6 June 2022 through 11 June 2022
ER -