Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Dataset for Detection and Segmentation of Underwater Marine Debris in Shallow Waters.

Scientific data·2024
Same author

The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art.

Sensors (Basel, Switzerland)·2021
Same author

Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line.

Bioinspiration & biomimetics·2019
Same author

Bio-inspired all-optical artificial neuromast for 2D flow sensing.

Bioinspiration & biomimetics·2018
Same author

d-Tubocurarine and Berbamine: Alkaloids That Are Permeant Blockers of the Hair Cell's Mechano-Electrical Transducer Channel and Protect from Aminoglycoside Toxicity.

Frontiers in cellular neuroscience·2017
Same author

Performance of neural networks for localizing moving objects with an artificial lateral line.

Bioinspiration & biomimetics·2017

Related Experiment Video

Updated: Dec 30, 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

952

Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial

Ben J Wolf1, Jos van de Wolfshaar1, Sietse M van Netten1

  • 1Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands.

Journal of the Royal Society, Interface
|January 23, 2020
PubMed
Summary

This study presents a novel method for tracking multiple underwater objects using simulated hydrodynamic flow. The system effectively localizes moving sources in 3D space by processing fluid velocity data with advanced algorithms.

Keywords:
convolutional neural networkhydrodynamic imaginginverse problemlateral linesensor arraysource localization

More Related Videos

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

13.0K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.3K

Related Experiment Videos

Last Updated: Dec 30, 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

952
Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

13.0K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.3K

Area of Science:

  • Biomimetics and Bio-inspired Engineering
  • Hydrodynamics and Fluid Dynamics
  • Signal Processing and Machine Learning

Background:

  • Fish utilize their lateral line system to detect and localize underwater objects by sensing hydrodynamic disturbances.
  • Existing methods for underwater object localization often face challenges with multiple simultaneous sources and complex 3D environments.

Purpose of the Study:

  • To develop and evaluate a signal processing method for simultaneously localizing multiple moving underwater objects in a 3D volume.
  • To simulate hydrodynamic flow and leverage fish lateral line organ principles for source localization.
  • To investigate the efficacy of convolutional neural networks and iterative algorithms for this task.

Main Methods:

  • A two-step localization process was employed: first, a convolutional neural network (CNN) estimated source presence probability in a 2D image.
  • Second, an automated iterative 3D-aware algorithm determined the precise 3D position of each source.
  • The system utilized sampled fluid velocity data from two parallel lateral lines, exploring various CNN architectures and input presentation methods.

Main Results:

  • Optimized CNN architectures, including multi-level amplified inputs and merged convolutional streams, significantly improved imaging performance.
  • The combined system demonstrated adequate 3D localization capabilities for multiple underwater sources.
  • The proposed method effectively processes complex hydrodynamic signals for source detection.

Conclusions:

  • The bio-inspired sensory system, integrating hydrodynamic simulation and advanced signal processing, shows promise for robust multi-object localization in 3D underwater environments.
  • This research validates the potential of combining neural networks with iterative algorithms for complex underwater sensing tasks.
  • Further development could lead to enhanced underwater surveillance and navigation systems.