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Related Experiment Video

Updated: Sep 11, 2025

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Fish feeding behavior recognition via lightweight two stage network and satiety experiments.

Shilong Zhao1,2, Kewei Cai3,4,5, Yanbin Dong1,2

  • 1College of Marine Technology and Environment, Dalian Ocean University, 52 Heishijiao Street, Dalian, 116023, China.

Scientific Reports
|August 16, 2025
PubMed
Summary

This study quantifies fish feeding behavior using computer vision for intelligent aquaculture. The developed model achieves high accuracy in classifying fish satiety while reducing computational complexity for real-time applications.

Keywords:
Action recognitionFish feeding behaviourGCNPose detectionYOLOv8

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Area of Science:

  • Aquaculture
  • Computer Vision
  • Artificial Intelligence

Background:

  • Industrial aquaculture demands efficient and cost-effective feeding strategies.
  • Computer vision offers a non-invasive method for monitoring fish, but current methods lack quantitative data and efficiency.
  • Existing models struggle with redundant image data and high computational complexity, limiting real-time applications.

Purpose of the Study:

  • To develop a quantitative approach for assessing fish feeding behavior and satiety.
  • To design an efficient computer vision model for intelligent fish feeding systems.
  • To address limitations of current models, including qualitative outputs, data redundancy, and complexity.

Main Methods:

  • Quantification of fish feeding behaviors through satiety experiments to generate quantitative labels.
  • A two-stage recognition network employing pose detection and graph convolutional networks (GCN) to model fish posture and distribution.
  • Development of lightweight RepSELAN and SPPSF modules to reduce model complexity.

Main Results:

  • Achieved 98.1% accuracy in satiety classification.
  • Reduced model parameters by 31.4% and computational load by 26.2%.
  • Maintained high performance with minimal decrease in mAP(B) and an increase in mAP(P).

Conclusions:

  • The proposed method provides a novel and efficient foundation for intelligent fish feeding strategies.
  • The quantitative approach and optimized model significantly outperform conventional methods in accuracy and efficiency.
  • This advancement supports reduced costs and enhanced fish welfare in industrial aquaculture.