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Research on an Infrared Multi-Target Saliency Detection Algorithm under Sky Background Conditions.

Shaosheng Dai1, Dongyang Li1

  • 1College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Sensors (Basel, Switzerland)
|January 18, 2020
PubMed
Summary

This study introduces a novel multi-saliency detection method for infrared images, enhancing the visibility of multiple targets against sky backgrounds. The approach significantly improves target detail and background suppression for better detection and tracking.

Keywords:
infrared multi-targetmulti-scale saliency fusionmulti-scale top-hatsky background

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Infrared images present challenges for multi-target saliency detection due to varying target sizes, low signal-to-noise ratios, and complex sky backgrounds.
  • Existing methods often struggle with incomplete saliency detection and unclear target boundaries in such conditions.

Purpose of the Study:

  • To propose an effective saliency detection method for infrared multi-target images under sky background conditions.
  • To address issues of incomplete saliency detection and unclear boundaries in infrared imagery.

Main Methods:

  • A multi-saliency detection approach combining multi-scale Top-hat transformation for noise reduction and feature extraction.
  • Frequency domain analysis, including spectral residuals and phase spectrum extraction, to generate saliency maps.
  • Quaternion feature extraction and phase spectrum reconstruction for additional saliency map generation.
  • Fusion of multiple saliency maps to achieve robust multi-target detection.

Main Results:

  • The proposed method generates infrared image saliency maps with clear target details and effective background suppression.
  • Experimental analysis using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) indices demonstrated high performance, with AUC exceeding 99%.
  • The method significantly improves multi-target saliency detection in infrared images against sky backgrounds.

Conclusions:

  • The developed saliency detection method effectively enhances the visibility and clarity of multiple targets in challenging infrared imagery.
  • The approach is beneficial for subsequent target detection and tracking tasks, offering improved performance and reliability.