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November 11, 2025Frontiers in Marine ScienceOpen Access

A lightweight YOLO network for robotic underwater biological detection

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Authors

YHYanyu HuangJHJianwei HuangJHJianwei Huang

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Overview

This method improves feature extraction for underwater detection, achieving 85.7% mAP in underwater environments, indicating enhanced biological analysis.

Key Points

  • Achieving 85.7% mAP, this method excels in underwater detection despite low light conditions and high occlusion.
  • Key innovations include feature extraction with a lightweight yolo network designed for robotic applications.
  • The approach integrates hybrid dilated attention and mixed aggregation star techniques to better recognize underwater organisms.
  • The lightweight model demonstrates potential for improving robotic biological detection in complex environments like turbid waters.

Cite This Study

Huang et al. (2025) studied this question.

synapsesocial.com/papers/69252e96c0ce034ddc3562afhttps://doi.org/10.3389/fmars.2025.1673437
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