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Echo Particle Image Velocimetry
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A lightweight model for echo trace detection in echograms based on improved YOLOv8.

Jungang Ma1,2,3,4, Jianfeng Tong5,6,7, Minghua Xue1,2,3

  • 1College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai, 201306, China.

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|December 5, 2024
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Summary
This summary is machine-generated.

This study introduces YOLOv8-SBE, a lightweight fish detection model for underwater unmanned platforms. It enhances efficiency and accuracy in noisy echograms, making it ideal for resource-limited echosounders.

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Area of Science:

  • Marine Biology
  • Robotics
  • Computer Vision

Background:

  • Increasing use of underwater unmanned platforms (UUPs) like AUVs necessitates advanced underwater detection.
  • Integration of scientific echosounders with UUPs for biometric applications faces challenges with current parameter-heavy models.
  • Existing detection models struggle with noisy, irregular, and dense echograms common in underwater environments.

Purpose of the Study:

  • To develop a lightweight and efficient fish detection model suitable for embedding in resource-constrained echosounders.
  • To address the limitations of current models in handling complex echogram data and small object recognition.
  • To improve the performance of underwater biometric applications using UUPs.

Main Methods:

  • Introduced YOLOv8-SBE, a lightweight model based on YOLOv8, incorporating specialized modules for enhanced performance.
  • Integrated the C2f_ScConv module to improve model efficiency and reduce parameter count.
  • Employed the BiFPN structure for enhanced information fusion and the EMA attention module for improved small target detection.

Main Results:

  • Achieved an 18.5% reduction in computational complexity and a 40% decrease in model parameters.
  • Improved mean Average Precision (mAP) at IoU threshold 0.5 (mAP 0.5) to 79.5%.
  • Enhanced mAP across various IoU thresholds (mAP 0.5:0.95) to 58.2%.

Conclusions:

  • YOLOv8-SBE offers a computationally efficient and accurate solution for fish detection in challenging underwater echograms.
  • The model's lightweight design makes it suitable for deployment on echosounders with limited computational resources.
  • This advancement supports more effective biometric applications for underwater unmanned platforms.