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Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA
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Shallow mud detection algorithm for submarine channels based on improved YOLOv5s.

Jiankang Hou1, Cunyong Zhang1

  • 1School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang, 222005, China.

Heliyon
|May 23, 2024
PubMed
Summary

This study introduces an enhanced YOLOv5s algorithm for detecting submarine mud, improving marine channel navigation safety. The new method achieves high accuracy and efficiency in identifying underwater hazards.

Keywords:
EMANWD lossShallow mudSub-bottom profilerSubmarine channelYOLOv5s-EF

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Area of Science:

  • Marine Geotechnical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Submarine mud presents significant risks to maritime channel navigation safety.
  • Traditional detection methods for submarine mud are often inefficient and lack accuracy.
  • Accurate identification of shallow submarine mud is crucial for safe dredging and channel maintenance.

Purpose of the Study:

  • To propose an enhanced shallow submarine mud detection algorithm using an improved YOLOv5s model.
  • To increase the accuracy and effectiveness of identifying submarine mud hazards in marine channels.
  • To provide a novel technical approach for ensuring safe operation and maintenance of dredging in submarine channels.

Main Methods:

  • Utilized sub-bottom profiler data from Lianyungang Port to acquire shallow mud sound print images.
  • Incorporated C2F feature module into the backbone for enhanced gradient flow and feature extraction.
  • Integrated Efficient Multi-Scale Attention (EMA) mechanism into the neck module for optimized channel dimensions and efficiency.
  • Introduced Normalized Wasserstein Distance (NWD) loss function for improved bounding box regression and multi-scale defect handling.

Main Results:

  • The improved YOLOv5s-EF algorithm demonstrated superior performance compared to the original YOLOv5s and other detection algorithms.
  • Achieved a validation set precision rate of 97.8%, recall rate of 97.6%, and F1 value of 97.7%.
  • Recorded a mean Average Precision (mAP)@0.5 of 98.2% and an FPS of 51.8, indicating high detection accuracy and speed.

Conclusions:

  • The enhanced YOLOv5s-EF algorithm offers a robust and accurate solution for shallow submarine mud detection.
  • The integration of specific modules and loss functions significantly improved detection performance.
  • This novel approach is vital for enhancing the safety and efficiency of maritime channel navigation and dredging operations.