Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076184/ https://www.ncbi.nlm.nih.gov/pubmed/33903689 http://dx.doi.org/10.1038/s41598-021-88334-0 |
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author | Kim, Yewon Park, Hyungmin |
author_facet | Kim, Yewon Park, Hyungmin |
author_sort | Kim, Yewon |
collection | PubMed |
description | 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(50) reaching 98%. Even while testing with bubble-swarm flows not included in the training set, the model detects more than 95% of the bubbles, which is equivalent or superior to conventional image processing methods. The pure processing speed for mask extraction is more than twice as fast as conventional approaches, even without counting the time required for tedious threshold parameter tuning. The present bubble detection and mask extraction tool is available online (https://github.com/ywflow/BubMask). |
format | Online Article Text |
id | pubmed-8076184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80761842021-04-27 Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows Kim, Yewon Park, Hyungmin Sci Rep Article 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(50) reaching 98%. Even while testing with bubble-swarm flows not included in the training set, the model detects more than 95% of the bubbles, which is equivalent or superior to conventional image processing methods. The pure processing speed for mask extraction is more than twice as fast as conventional approaches, even without counting the time required for tedious threshold parameter tuning. The present bubble detection and mask extraction tool is available online (https://github.com/ywflow/BubMask). Nature Publishing Group UK 2021-04-26 /pmc/articles/PMC8076184/ /pubmed/33903689 http://dx.doi.org/10.1038/s41598-021-88334-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kim, Yewon Park, Hyungmin Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows |
title | Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows |
title_full | Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows |
title_fullStr | Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows |
title_full_unstemmed | Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows |
title_short | Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows |
title_sort | deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076184/ https://www.ncbi.nlm.nih.gov/pubmed/33903689 http://dx.doi.org/10.1038/s41598-021-88334-0 |
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