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M4Net: Multi-level multi-patch multi-receptive multi-dimensional attention network for infrared small target

Fan Zhang1, Huilin Hu1, Biyu Zou1

  • 1School of Automation, State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the multi-level multi-patch multi-receptive multi-dimensional attention network (M4Net) for enhanced infrared small target detection. M4Net effectively preserves target details by enabling feature interaction across different network levels, outperforming existing methods.

Keywords:
Attention mechanismDeep learningInfrared small targetTarget detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Signal Processing

Background:

  • Infrared small target detection is crucial for military and civilian applications.
  • Traditional methods rely on manual features, while deep learning models can lose targets in deep layers due to downsampling.
  • Existing methods struggle with maintaining target contour and location details.

Purpose of the Study:

  • To develop a novel deep learning network, M4Net, for robust infrared small target detection.
  • To address the limitations of traditional and existing deep learning methods in preserving target information.
  • To improve the accuracy and reliability of infrared small target detection systems.

Main Methods:

  • Designed a multi-level multi-patch multi-receptive multi-dimensional attention network (M4Net).
  • Employed a multi-level feature extraction module (MFEM) with a multilayer vision transformer (ViT) within an encoder-decoder framework.
  • Introduced multi-patch attention module (MPAM), multi-receptive field module (MRFM), and multi-dimension interactive module (MDIM) for enhanced feature capture and interaction.

Main Results:

  • M4Net demonstrated effective information interaction between high-level and low-level features.
  • The proposed modules (MPAM, MRFM, MDIM) successfully captured and enhanced feature information.
  • Experimental results on an infrared small target detection dataset showed superior performance compared to other methods.

Conclusions:

  • M4Net significantly improves infrared small target detection by maintaining target contour and location details.
  • The network architecture effectively fuses multi-scale features and enhances learning capabilities.
  • The proposed method offers a promising advancement for infrared small target detection applications.