Deep Learning-Based Multi-Scale Bubble Detection and Feature Analysis

Deep Learning-Based Multi-Scale Bubble Detection and Feature Analysis

The morphological characteristics of bubbles in gas–liquid two-phase flow serve as an important basis for the study of heat and mass transfer as well as the design of chemical reaction equipment. These characteristics are widely applied in industrial fields such as petroleum, chemical engineering, e...

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Bibliographic Details
Main Author: Lichun Bai (6684683) (author)
Other Authors: Xuan Wang (55634) (author), Sen Lin (182597) (author), Zishu Chai (21495952) (author), Ronghui Zhao (14325732) (author)
Published: 2025
Subjects:
Genetics
Space Science
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
phase flow serve
global contextual information
enhanced algorithm attains
efficient complete intersection
integrates multiscale features
capture multiscale features
reduce feature redundancy
good detection performance
chemical reaction equipment
scale bubble detection
bubble morphological parameters
channel reconstruction convolution
bubble sizes
feature analysis
detection process
chemical engineering
detection capability
multiscale bubbles
widely applied
speed camera
paper proposes
neck network
morphological characteristics
missed detections
mass transfer
loss function
large variation
industrial fields
important basis
fully demonstrating
finely adjust
false detections
experimental findings
environmental protection
dense distribution
deep learning
bounding boxes
backbone network
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