Bubble Evolution Detector B.E.D. – A Neural Network-Based Approach to Accurately Detect, Classify, and Evaluate Gas Bubbles Captured by A High-Speed Camera on Textured Surfaces
Authors
Researcher, Research Center Energy Storage Technologies EST, Clausthal University of Technology, Am Stollen 19A, 38640 Goslar
Researcher, Research Center Energy Storage Technologies EST, Clausthal University of Technology, Am Stollen 19A, 38640 Goslar
Sebastian Handrich
sebastian.handrich@visualistik.eu
Programming, TVG – Technische Visualistik GmbH, Gerhart- Hauptmann-Str. 21, 39108 Magdeburg
Christoph Niems
Support, Computer Center of the Clausthal University of Technology, Erzstraße 18, 38678 Clausthal-Zellerfeld
Head of Group, Research Center Energy Storage Technologies EST, Clausthal University of Technology, Am Stollen 19A, 38640 Goslar
DOI: https://doi.org/10.5334/jors.505 | Journal eISSN: 2049-9647
Language: English
Submitted on: Feb 2, 2024
Accepted on: Mar 20, 2025
Published on: Mar 28, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2025 Lukas Lentz, Dorian Hüne, Sebastian Handrich, Christoph Niems, Thomas Gimpel, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.