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Related Experiment Video

Updated: Nov 17, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

792

Deep neural networks for active wave breaking classification.

Caio Eadi Stringari1, Pedro Veras Guimarães2,3, Jean-François Filipot2

  • 1France Energies Marines, 29280, Plouzané, France. Caio.Stringari@france-energies-marines.com.

Scientific Reports
|February 12, 2021
PubMed
Summary
This summary is machine-generated.

A new machine learning method accurately detects active wave breaking in ocean imagery. This advancement aids in understanding wave energy dissipation and improving oceanographic models and coastal management.

Related Experiment Videos

Last Updated: Nov 17, 2025

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Area of Science:

  • Oceanography
  • Fluid Dynamics
  • Machine Learning

Background:

  • Wave breaking is crucial for ocean energy dissipation, impacting coastal morphodynamics, air-sea interactions, and satellite data retrieval.
  • A lack of observational field data hinders a comprehensive physical understanding of wave breaking processes.
  • Improved methods and data are essential for advancing wave breaking research.

Purpose of the Study:

  • To develop and present a novel machine learning method for detecting active wave breaking from video imagery.
  • To provide a freely accessible tool and dataset for the scientific community to study wave breaking.
  • To enhance the understanding of wave breaking dynamics and its associated physical properties.

Main Methods:

  • Utilized a combination of classical and deep learning techniques for wave breaking detection.
  • Trained and validated machine learning models on video imagery data capturing active wave breaking with bubble entrainment.
  • Achieved high classification accuracy in identifying active wave breaking events.

Main Results:

  • The best-performing machine learning model achieved over 90% balanced classification accuracy on the test dataset.
  • The developed method successfully identifies active wave breaking, characterized by visible bubble entrainment.
  • The application of the method allows for statistical analysis of breaking wave properties.

Conclusions:

  • The novel machine learning method offers a significant advancement in detecting and analyzing wave breaking events.
  • The freely available method and dataset will support future research in operational forecasting, coastal management, and remote sensing.
  • This work provides crucial data and tools to bridge the gap in the physical understanding of wave breaking.