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Pseudo-Sample Generation and Self-Supervised Framework for Infrared Dim and Small Target Detection.

Jinxin Guo1, Weida Zhan1, Dehua Huo1

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Summary
This summary is machine-generated.

This study introduces a new method for generating realistic infrared simulation data by modeling physical degradation processes. This approach improves deep learning models for detecting dim and small targets in real-world scenarios.

Keywords:
image degradationinformation processingpseudo-sample generationself-supervised learningtarget detection

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Area of Science:

  • Computer Vision
  • Infrared Imaging
  • Machine Learning

Background:

  • Infrared dim and small target detection is vital for long-range sensing.
  • Deep learning for this task is limited by the lack of real annotated data.
  • Current synthetic data methods do not accurately reflect real-world infrared imaging physics.

Purpose of the Study:

  • To develop a novel pseudo-sample generation paradigm for infrared target detection.
  • To address the limitations of existing synthetic data generation methods.
  • To improve the performance and generalization of infrared target detection models.

Main Methods:

  • Physics-informed degradation modeling to decouple target and background processes.
  • Information fidelity optimization for reliable degradation modeling.
  • Online grid-based high-order constraints (semantic, structural, grayscale) for dataset generation.
  • A self-supervised detection framework with custom loss functions and evaluation metrics.

Main Results:

  • Generated synthetic data shows superior authenticity compared to existing methods.
  • The proposed method significantly enhances the generalization performance of various detectors.
  • Achieved superior detection accuracy on real-world data compared to baseline models.

Conclusions:

  • The physics-informed pseudo-sample generation paradigm effectively creates high-fidelity infrared simulation data.
  • This approach overcomes the data scarcity issue in infrared dim and small target detection.
  • The method offers a promising solution for improving real-world infrared sensing applications.