Abstract
Visual analysis allows an estimate of different local boiling characteristics including bubble growth rate, departure diameters and frequencies of nucleation, nucleation site density and evolution of bubbles and dry spots in time. At the same time, visual determination of the presented characteristics in case of big amounts of data requires the development of the appropriate software which will allow not only determination of bubble location, but also an estimate of their sizes based on high-speed video. The presented problem can be solved by using the instance segmentation approach based on a convolutional neural network. In the presented work Mask R-CNN network architecture was used for estimation of the local boiling characteristics.
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