  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
(CHF) conditions.
