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FishKP-YOLOv11: An Automatic Estimation Model for Fish Size and Mass in Complex Underwater Environments.

Jinfeng Wang1, Zhipeng Cheng1, Mingrun Lin1

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Summary
This summary is machine-generated.

This study introduces a non-contact fish size and mass estimation framework for complex aquaculture environments. The system accurately measures fish dimensions and weight, improving aquaculture management.

Keywords:
computer visionfish mass estimationfish size estimationkey point detectionstereo vision

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Area of Science:

  • Aquaculture Technology
  • Computer Vision
  • Biometrics

Background:

  • Accurate fish size and mass estimation is vital for aquaculture management.
  • Existing methods are limited by ideal condition requirements, hindering real-world application.
  • Complex underwater environments pose challenges for non-contact measurement.

Purpose of the Study:

  • To develop a robust non-contact framework for fish size and mass estimation in complex underwater conditions.
  • To improve the accuracy and applicability of fish measurement techniques in practical aquaculture settings.

Main Methods:

  • Integration of an improved FishKP-YOLOv11 module (based on YOLOv11) for key point detection.
  • Utilization of stereo vision technology to reconstruct 3D fish key point coordinates from 2D detections.
  • Application of a Random Forest model to establish the relationship between fish size and mass.

Main Results:

  • The FishKP-YOLOv11 module demonstrated superior performance (mAP) compared to various YOLO versions.
  • Mean Absolute Errors (MAE) for length, width, and mass estimations were 0.35 cm, 0.10 cm, and 2.7 g, respectively.
  • The framework achieved high accuracy in complex underwater environments.

Conclusions:

  • The proposed framework is effective for non-contact fish size and mass estimation in challenging aquaculture environments.
  • The system offers a practical solution for real-time aquaculture management and monitoring.
  • The study validates the framework's suitability for actual fish breeding scenarios.