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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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Published on: December 15, 2023

A Scene Detection Complexity Metric for Infrared Small Target Detection.

Zhiyuan Huang1, Zhiyong Zhang1

  • 1School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

A new Scene Detection Complexity (SDC) metric quantifies infrared small target detection difficulty. It unifies target saliency, background complexity, and target-background coupling for objective evaluation.

Keywords:
entropy weight methodinfrared small target detectionobjective weightingperformance evaluationprincipal component analysisscene detection complexity

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Signal Processing

Background:

  • Infrared small target detection is crucial for surveillance and reconnaissance.
  • Algorithm performance varies significantly with scene complexity, lacking a unified difficulty metric.
  • Existing indicators offer limited, individual perspectives on detection challenges.

Purpose of the Study:

  • To propose a Scene Detection Complexity (SDC) metric for quantifying infrared small target detection difficulty.
  • To develop a unified measure considering target saliency, background complexity, and target-background coupling.
  • To provide an objective tool for evaluating detection algorithms and assessing scene difficulty.

Main Methods:

  • Selected six indicators: statistical variance, target-background contrast, signal-to-clutter ratio, information entropy, structural similarity, and target size.
  • Applied Min-Max normalization and combined entropy weight method with principal component analysis for objective weighting.
  • Fused weighted indicators into a single SDC value ranging from 0 to 1.

Main Results:

  • The proposed SDC metric showed high correlation with subjective difficulty ratings (0.956) and detection scores (-0.902).
  • Experiments revealed traditional methods are sensitive to scene complexity, while deep learning methods are more robust.
  • The SDC metric effectively differentiates detection difficulty across various infrared scenes.

Conclusions:

  • The SDC metric offers a unified and objective approach to assess infrared small target detection difficulty.
  • It serves as a valuable tool for algorithm performance evaluation and selection.
  • The metric aids in pre-assessing scene difficulty, guiding algorithm deployment in complex environments.