ArticleScientific reports2021
Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 95 citations in OpenAlex.
- Longitudinal Multi-Batch Characterisation of Beer Wort Fermentation Foam Using Image-Based Semantic Segmentation.Foods (Basel, Switzerland) · 2026Article
- Boundary-Transition Characterization in High-Speed Underwater Bubble Imaging Under Broadband and Narrow-Band Illumination.Sensors (Basel, Switzerland) · 2026Article
- Analysis of Multiscale Condensation Phenomena Using a Zero-Shot Computer Vision Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computer-Vision- and Deep-Learning-Based Determination of Flow Regimes, Void Fraction, and Resistance Sensor Data in Microchannel Flow Boiling.Sensors (Basel, Switzerland) · 2024Article
- Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Flipping food during grilling tasks, a dataset of utensils kinematics and dynamics, food pose and subject gaze.Scientific data · 2022Article
- A Fast Bubble Detection Method in Microtubes Based on Pulsed Ultrasound.Micromachines · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While investigating multiphase flows experimentally, the spatiotemporal variation in the interfacial shape between different phases must be measured to analyze the transport phenomena. For this, numerous image processing techniques have been proposed, showing good performance. However, they require trial-and-error optimization of thresholding parameters, which are not universal for all experimental conditions; thus, their accuracy is highly dependent on human experience, and the overall processing cost is high. Motivated by the remarkable improvements in deep learning-based image processing, we trained the Mask R-CNN to develop an automated bubble detection and mask extraction tool that works universally in gas-liquid two-phase flows. The training dataset was rigorously optimized to improve the model performance and delay overfitting with a finite amount of data. The range of detectable bubble size (particularly smaller bubbles) could be extended using a customized weighted loss function. Validation with different bubbly flows yields promising results, with AP
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