Título: Bubble Detection with Semantic Segmentation for Multiphase Flow Particle Image Velocimetry
Autores: Walisson Chaves Ferreira Pinto, Antonio M. Pinto, Paulo Rodrigo Cavalin, David Menotti, Igor Braga de Paula, Helon Vicente Hultmann Ayala
Resumo: In this paper, we present an evaluation of semantic segmentation for bubble detection in multiphase flow particle image velocimetry (PIV), from which a lot of applications in oil, gas, and chemical industries, for instance, can benefit. The problem is challenging, however, given the differences in contrast that can make the bubble almost invisible at blind eye. Thus, for this research we have collected and manually annotated a dataset with 1161 images, and trained a U-Net neural network to perform the detection of the bubbles. The experiments presented pixel accuracies of about 86.78% on the largest test set, with more challenging images, but more than 99% can be achieved with more training images and less test images. We believe that, although preliminary, the results are encouraging towards the development of a fully-automated Computer Vision-based system for PIV, but more effort should be put into expanding the training set and enhancing the evaluation protocol in future work
Palavras-chave: Particle image velocimetry, Semantic segmentation, Taylor bubble, Machine learning, Deep learning
Páginas: 7
Código DOI: 10.21528/CBIC2023-030
Artigo em pdf: CBIC_2023_paper030.pdf
Arquivo BibTeX: CBIC_2023_030.bib