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

Methane Concentration Inversion Based on Multi-Feature Fusion and Stacking Integration.

Sensors (Basel, Switzerland)·2025
Same author

GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies-Sea Ice Thickness Inversion in Beaufort Sea.

Sensors (Basel, Switzerland)·2024
Same author

Red Tide Detection Method Based on Improved U-Net Model-Taking GOCI Data in East China Sea as an Example.

Sensors (Basel, Switzerland)·2023
Same author

Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network.

Sensors (Basel, Switzerland)·2023
Same author

Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion.

Sensors (Basel, Switzerland)·2022
Same author

Clinical Progress and Optimization of Information Processing in Artificial Visual Prostheses.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Feb 28, 2026

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.8K

Cost-Effective Fish Volume Estimation in Aquaculture Using Infrared Imaging and Multi-Modal Deep Learning.

Like Zhang1, Yanling Han1, Ge Song1

  • 1College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost infrared camera system for accurate fish volume estimation in aquaculture. The innovative pipeline enables scalable biomass monitoring, supporting sustainable seafood production.

Keywords:
aquaculturebiomass monitoringcost-effectivefish volume estimationinfrared imagingmulti-modal deep learningsynthetic data generation

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

13.1K
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

1.6K

Related Experiment Videos

Last Updated: Feb 28, 2026

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.8K
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

13.1K
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

1.6K

Area of Science:

  • Aquaculture technology
  • Computer vision
  • Biomass estimation

Background:

  • Accurate fish volume estimation is crucial for sustainable aquaculture but traditional methods are invasive and costly.
  • Existing non-invasive techniques often require expensive multi-sensor systems, limiting scalability.
  • There is a need for cost-effective, non-invasive solutions for real-time biomass monitoring in dense aquaculture tanks.

Purpose of the Study:

  • To develop a cost-effective infrared (IR)-only pipeline for reconstructing depth and Red Green Blue (RGB) data from low-cost IR videos.
  • To enable scalable and accurate fish biomass monitoring in dense aquaculture environments.
  • To reduce hardware costs for fish volume estimation while maintaining high accuracy.

Main Methods:

  • Developed an IR-only pipeline with five integrated modules: IR-to-depth estimation, IR-to-RGB generation, detection and tracking, instance segmentation, and volume estimation.
  • Utilized contour-guided attention, texture-conditioned injection, cross-modal fusion, depth-guided branches, and trajectory-depth Transformer fusion.
  • Incorporated specific losses like smoothing loss, water-adaptive loss, and deformation-adaptive loss for improved performance.
  • Trained the system on a dataset of 166 goldfish across 124 videos, with 8-16 fish per tank.

Main Results:

  • Achieved a Mean Absolute Error (MAE) of 0.85 cm³ and a coefficient of determination (R²) of 0.961 for fish volume estimation.
  • Outperformed state-of-the-art methods by 19-41% in accuracy.
  • Reduced hardware costs by 80% compared to existing multi-sensor setups.
  • Demonstrated robustness in dense tank conditions with 8-16 fish per tank.

Conclusions:

  • The proposed IR-only pipeline offers a cost-effective and accurate solution for fish volume estimation and biomass monitoring in aquaculture.
  • This technology advances precision aquaculture, enabling better feed optimization and health monitoring.
  • The system promotes environmental sustainability by supporting efficient resource management in response to rising global seafood demand.