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Fish Swimming Data Reconstruction Method Based on the Deep Learning Algorithm
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DOI：10.17736/ijope.2025.jc936.png)
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Abstract
En 中文
The field of bionic engineering and hydrodynamic research requires a deep understanding of a fish swimming vortex field and motion maneuvering processes to develop high-mobility underwater vehicles. However, it is often difficult to collect reliable swimming fish data due to shooting limitations and data processing difficulties, which hinder further exploration of fish maneuvers and vortex evolution rules. This article presents an experimental study using living zebrafish as the test subject. Images of zebrafish are collected during motor movement by high-speed camera. The collection of the vortex flow field data during fish swimming is completed through particle image velocimetry.
Keywords:
Fish swimming
particle image velocimetry experiment
deep learning algorithm
wake vortex identification
Journal
I
IF:
0.6
Papers:
27
Citations:
776