Image-Based Flow Regime Recognition in Aerated Stirred Tanks Using Deep Transfer Learning

Research output: Contribution to journalResearch articleContributedpeer-review



Monitoring of flow regimes in aerated stirred tanks is important to ensure energy efficiency and product quality. The use of deep learning models for the recognition of flow regimes shows promising results. However, such models require a large amount of data for training. The aim of this paper is to apply the deep transfer learning approach to address this challenge. We compare various pre-trained models with the differential learning rate and 2-step transfer learning approaches to analyse the resultant model performance. We also investigate the effect of the dataset size on the classification accuracy.


Original languageEnglish
Pages (from-to)1172-1179
Number of pages8
JournalChemie Ingenieur Technik
Issue number7
Publication statusPublished - 10 May 2023

External IDs

Scopus 85152554073
WOS 000984990500001
ORCID /0000-0001-5165-4459/work/142248296


Research priority areas of TU Dresden

Subject groups, research areas, subject areas according to Destatis


  • Deep learning, Deep transfer learning, Flow regime recognition, Machine learning