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M-ReDet: A mamba-based method for remote sensing ship object detection and fine-grained recognition.

Xuhui Liu1, Chi Feng2, Shuran Zi1

  • 1School of Economics, Management and Law, Jilin Normal University, Siping, China.

Plos One
|August 21, 2025
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Summary

This study introduces Mamba-ReDet (M-ReDet), an improved method for ship object detection in remote sensing images. M-ReDet enhances fine-grained feature extraction and refinement, significantly boosting detection accuracy for various ship types.

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Area of Science:

  • Remote Sensing and Geospatial Analysis
  • Computer Vision and Image Processing
  • Artificial Intelligence and Machine Learning

Background:

  • Ship object detection and fine-grained recognition are crucial for applications like maritime surveillance and navigation.
  • Existing methods struggle with accurate detection of diverse ship types in complex remote sensing imagery.
  • Improving the precision of ship classification and localization is an ongoing challenge.

Purpose of the Study:

  • To enhance the accuracy of ship object detection and fine-grained recognition in remote sensing images.
  • To develop a novel method, Mamba-ReDet (M-ReDet), that improves upon existing ReDet architectures.
  • To address the limitations in extracting and refining fine-grained features for different ship categories.

Main Methods:

  • Proposed Mamba-ReDet (M-ReDet) incorporating a Mamba-ReResNet backbone for fine-grained feature extraction using Mamba's selective memory.
  • Introduced Ship Object Perception Module (SOPM) and Ship Feature Refinement Module (SFRM) to extract and fuse spatial-positional information for feature refinement.
  • Utilized KFIoU and Focal Loss for improved regression and classification accuracy during training.

Main Results:

  • Mamba-ReDet achieved a mean Average Precision (mAP0.5) of 43.29% on the FAIR1M(ship) dataset.
  • Mamba-ReDet achieved a mAP0.5 of 82.09% on the DOTAv1.0 visible light (RGB) dataset.
  • Demonstrated performance improvements of 2.78% and 3.34% over the baseline ReDet on the respective datasets.

Conclusions:

  • The proposed Mamba-ReDet (M-ReDet) effectively enhances ship object detection and fine-grained recognition in remote sensing images.
  • The integration of Mamba's selective memory and specialized modules significantly improves feature extraction and refinement capabilities.
  • The method shows superior performance compared to existing approaches, offering a valuable advancement for maritime applications.