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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network.

Yanling Han1, Bowen Zheng1, Xianghong Kong2

  • 1College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved PSPNet (IST-PSPNet) for accurate underwater fish segmentation, enhancing feature perception and reducing computational load for intelligent fisheries applications.

Keywords:
PSPNetdifferent scale featuresfishfish characteristicsunderwater fish segmentation

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Area of Science:

  • Computer Vision
  • Marine Biology
  • Artificial Intelligence

Background:

  • Accurate underwater fish segmentation is crucial for intelligent fisheries and obtaining fish morphology data.
  • Underwater image quality issues like blur, distortion, and low contrast hinder segmentation accuracy.

Purpose of the Study:

  • To propose an improved PSPNet (IST-PSPNet) for enhanced underwater fish segmentation.
  • To address challenges posed by poor image quality in underwater environments.

Main Methods:

  • Implemented an iterative attention feature fusion mechanism for multi-scale feature and context perception.
  • Utilized SoftPool for reduced parameters and computations while preserving feature information.
  • Integrated a triplet attention (TA) module for improved spatial attention and background interference suppression.
  • Employed a parameter-sharing strategy to further decrease computational load.

Main Results:

  • Achieved 91.56% Miou on the DeepFish dataset.
  • Demonstrated efficiency with 46.68M parameters and 40.27 GFLOPS.
  • Significantly improved segmentation of fish with similar colors, low contrast, and small sizes.
  • Enhanced clarity of fish edge locations in complex underwater scenes.

Conclusions:

  • The proposed IST-PSPNet method offers superior performance for underwater fish segmentation compared to existing approaches.
  • The method effectively overcomes image quality limitations, paving the way for more accurate intelligent fisheries data acquisition.
  • IST-PSPNet shows promise for advancing automated fish monitoring and analysis in marine environments.