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Dual-Path Residual "Shrinkage" Network for Side-Scan Sonar Image Classification.

Fengxue Ruan1, Lanxue Dang1, Qiang Ge1,2

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This study introduces a novel dual-path deep residual shrinkage network (DP-DRSN) to improve underwater object detection. The DP-DRSN enhances side-scan sonar image classification accuracy and efficiency in noisy environments.

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Area of Science:

  • Marine Technology
  • Artificial Intelligence
  • Image Processing

Background:

  • Underwater environments present significant challenges for object detection due to noise and complex conditions.
  • Current seabed detection relies on sonar, but sonar images suffer from low contrast, blurred edges, and poor texture, hindering classification.
  • These image quality issues negatively impact the accuracy of automated underwater object identification systems.

Purpose of the Study:

  • To develop an effective neural network attention module for classifying side-scan sonar images.
  • To address the limitations of traditional sonar image analysis in complex underwater environments.
  • To improve the accuracy and efficiency of underwater object detection using deep learning.

Main Methods:

  • Proposed a dual-path deep residual shrinkage network (DP-DRSN) module for side-scan sonar image classification.
  • The DP-DRSN extracts multi-scale background and texture information using global pooling.
  • It integrates cross-channel information and applies a soft threshold function for denoising image features.

Main Results:

  • The DP-DRSN module demonstrated superior performance compared to existing models.
  • Achieved higher classification accuracy in side-scan sonar images.
  • Showcased improved efficiency for underwater object recognition tasks.

Conclusions:

  • The DP-DRSN is a feasible and effective method for classifying side-scan sonar images.
  • The proposed module significantly enhances the analysis of underwater acoustic imagery.
  • This advancement contributes to more reliable seabed detection and object identification.