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An Improved U-Net Infrared Small Target Detection Algorithm Based on Multi-Scale Feature Decomposition and Fusion and

Xiangsuo Fan1,2, Wentao Ding1, Xuyang Li1

  • 1School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces MST-UNet, an enhanced U-Net model for infrared small target detection. It improves segmentation accuracy by minimizing feature loss and enhancing feature extraction using multi-scale fusion and attention mechanisms.

Keywords:
U-Netattention mechanismhaar wavelet transforminfrared small targetsmulti-scale feature fusion

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Signal Processing

Background:

  • Infrared small target detection is vital for military, security, and medical applications.
  • Deep learning, particularly convolutional neural networks, shows promise but struggles with small targets due to scale, weak signals, and background noise.
  • Existing methods often suffer from feature leakage and misdetection in infrared small target segmentation.

Purpose of the Study:

  • To propose an enhanced U-Net model, MST-UNet, for improved infrared small target segmentation.
  • To address the limitations of traditional convolutional neural networks in detecting small targets with weak signals and complex backgrounds.
  • To enhance feature utilization and reduce information loss during the segmentation process.

Main Methods:

  • Implemented MST-UNet, combining multi-scale feature decomposition, fusion, and attention mechanisms.
  • Replaced maximum pooling with Haar wavelet transform for downsampling to preserve feature information.
  • Introduced multi-scale residual units for enhanced contextual information extraction and feature expression.
  • Integrated a triple attention mechanism to improve feature recovery and multidimensional information utilization.

Main Results:

  • The MST-UNet model demonstrated significant improvements in target contour accuracy and segmentation precision.
  • Achieved Intersection over Union (IoU) of 80.09% and normalized IoU (nIoU) of 80.19% on the NUDT-SIRST dataset.
  • Effectively mitigated issues of feature leakage and misdetection common in infrared small target segmentation.

Conclusions:

  • MST-UNet offers a robust solution for infrared small target detection and segmentation.
  • The proposed method effectively enhances feature extraction and utilization, leading to superior segmentation performance.
  • The integration of wavelet transform and attention mechanisms provides a promising direction for future research in this domain.