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Related Experiment Video

Updated: Jul 16, 2026

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

Development of a Novel Convolution to Interactive Capture and Recalibration Enhancement Module for Underwater Fish

Vinie Lee Silva-Alvarado1, Ali Ahmad1, Sandra Sendra1

  • 1Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, Grao de Gandia, 46730 Valencia, Spain.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

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This study introduces C2ICARE, a novel attention module for underwater fish monitoring. C2ICARE enhances feature discrimination in optical sensor networks, improving detection accuracy in challenging aquatic environments.

Area of Science:

  • Computer Vision
  • Marine Biology
  • Sensor Networks

Background:

  • Underwater optical sensor networks face challenges like illumination variability, low contrast, and complex backgrounds, hindering fish monitoring.
  • Existing attention mechanisms in deep networks struggle with spatial information loss and limited multi-scale interaction in these conditions.

Purpose of the Study:

  • To introduce Convolution to Interactive Capture and Recalibration Enhancement (C2ICARE), a lightweight attention module designed to improve feature representation for underwater fish detection.
  • To enhance feature discrimination and maintain computational efficiency in challenging underwater imagery.

Main Methods:

  • Developed C2ICARE, a lightweight attention module adapting memory interaction principles into an edge-oriented attention framework.
Keywords:
YOLOaquatic monitoringattention mechanismdeep learningobject detectionsmart aquacultureunderwater detection

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Last Updated: Jul 16, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
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09:32

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

Published on: November 20, 2017

  • Incorporated innovations: 1:3 memory-feature split, parallel multi-scale depthwise convolutions (3x3 and 7x7), and a cross-branch interaction with a ConvNeXt-style feed-forward network.

    • Main Results:

    • YOLO26n with C2ICARE achieved a mean average precision (mAP@0.5:0.95) of 0.7033 on an underwater fish dataset.
    • Outperformed existing attention methods (Coordinate Attention, FasterBlock, CBAM) with minimal parameter and GFLOPs increase.
    • Multi-objective Pareto Frontier analysis confirmed C2ICARE's balance of accuracy, efficiency, and generalization.

    Conclusions:

    • C2ICARE effectively enhances feature discrimination for underwater fish detection, offering a lightweight and efficient solution.
    • The module's design facilitates seamless integration with underwater sensor networks and fog platforms for real-time applications.
    • Future work includes exploring broader marine applications and cross-platform deployment.