Data from: BubbleID: A deep learning framework for bubble interface dynamics analysis

This paper presents BubbleID, a sophisticated deep learning architecture
designed to comprehensively identify both static and dynamic attributes of
bubbles within sequences of boiling images. By amalgamating segmentation
powered by Mask R-CNN with SORT-based tracking techniques, the framework
is capable of analyzing each bubble's location, dimensions, interface
shape, and velocity over its lifetime and capturing dynamic events such as
bubble departure. BubbleID is trained and tested on boiling images across
diverse heater surfaces and operational settings. This paper also offers a
comparative analysis of bubble interface dynamics prior to and
post-critical heat flux conditions.