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Multi-Scale Marine Object Detection in Side-Scan Sonar Images Based on BES-YOLO.

Quanhong Ma1, Shaohua Jin1, Gang Bian1

  • 1Department of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, China.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary

This study introduces BES-YOLO for improved multi-scale seafloor target detection in noisy sonar images. The novel network enhances accuracy and efficiency for underwater object identification.

Keywords:
YOLOdeep learningmultiscaleobject detectionside-scan sonar

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Area of Science:

  • Marine technology
  • Artificial intelligence
  • Image processing

Background:

  • Side-scan sonar images present challenges for multi-scale target detection due to high noise and complex textures.
  • Existing methods often struggle with accuracy in these demanding underwater environments.

Purpose of the Study:

  • To develop an accurate and efficient model for multi-scale seafloor target detection in side-scan sonar images.
  • To enhance the robustness and performance of deep learning models in noisy, complex underwater imagery.

Main Methods:

  • Proposed the BES-YOLO network, integrating an efficient multi-scale attention (EMA) mechanism and a bi-directional feature pyramid network (Bifpn) into the YOLOv8 architecture.
  • Implemented a Shape_IoU loss function for continuous model optimization.
  • Preprocessed the dataset using 2D discrete wavelet decomposition and reconstruction to improve network robustness.

Main Results:

  • Achieved a mean average accuracy of 92.4% (mAP@0.5) and 67.7% (mAP@0.5:0.95) using the BES-YOLO network.
  • Demonstrated a significant improvement over the YOLOv8n model, with increases of 5.3% and 4.4% in mAP@0.5 and mAP@0.5:0.95, respectively.
  • The preprocessing technique enhanced network robustness.

Conclusions:

  • The BES-YOLO network effectively improves the detection accuracy and efficiency of multi-scale targets in challenging side-scan sonar images.
  • This model offers a viable solution for intelligent undersea target detection on platforms like Autonomous Underwater Vehicles (AUVs).