May 15, 2024, 4:42 a.m. | Christy Dunlap, Changgen Li, Hari Pandey, Ngan Le, Han Hu

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07994v1 Announce Type: cross
Abstract: 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 …

abstract analysis architecture arxiv attributes bubble cnn cs.ai cs.cv cs.lg deep learning deep learning framework dynamic dynamics eess.iv framework identify images location paper r-cnn segmentation sort tracking type

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