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Updated: Aug 2, 2025

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An Improved YOLOv5-Based Underwater Object-Detection Framework.

Jian Zhang1,2, Jinshuai Zhang2, Kexin Zhou2

  • 1School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces an improved YOLOv5 framework for marine organism detection, enhancing accuracy in challenging underwater conditions. The novel architecture significantly boosts detection performance, outperforming existing models.

Area of Science:

  • Computer Vision
  • Marine Biology
  • Artificial Intelligence

Background:

  • General object-detection models struggle with underwater imagery due to poor quality, complex backgrounds, and scale variations.
  • Accurate identification of marine organisms is crucial for ecological monitoring and research.

Purpose of the Study:

  • To develop an advanced object-detection architecture for improved marine biological identification.
  • To address challenges in underwater image analysis and enhance detection accuracy.

Main Methods:

  • An improved YOLOv5 framework incorporating the Real-Time Models for object Detection (RTMDet) backbone with Cross-Stage Partial Layer (CSPLayer) for comprehensive contextual information capture.
  • Integration of the BoT3 module with multi-head self-attention (MHSA) mechanism into the YOLOv5 neck for enhanced detection of dense targets.
Keywords:
CSPNeXt blockYOLOv5bottleneck transformerobject detection

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  • Application of union dataset augmentation (UDA) using Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) for robust underwater image processing.

    • Main Results:

    • The proposed framework effectively mitigates underwater image degradation.
    • Achieved mean Average Precision (mAP@0.5) of 79.8% on URPC2019 and 79.4% on URPC2020.
    • Demonstrated performance improvements of 3.8% and 1.1% over YOLOv8 on the respective datasets.

    Conclusions:

    • The enhanced YOLOv5 architecture offers superior performance for high-precision marine organism detection.
    • The study highlights the effectiveness of integrating RTMDet, BoT3 with MHSA, and MLLE augmentation for underwater object detection.