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Fast and Robust Infrared Small Target Detection Using Weighted Local Difference Variance Measure.

Ying Zheng1, Yuye Zhang1, Ruichen Ding1

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Summary

A new weighted local difference variance measure (WLDVM) algorithm improves infrared search and track (IRST) systems by accurately detecting small targets in complex backgrounds. This method enhances target detection and reduces false alarms, outperforming existing techniques.

Keywords:
infrared (IR) small targetlocal difference variance measure (LDVM)new tri-layer filtering windowweighting functionwindow intensity level (WIL)

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Area of Science:

  • Computer Vision
  • Signal Processing
  • Infrared Imaging

Background:

  • Infrared search and track (IRST) systems face limitations in small-target detection due to complex backgrounds and interference.
  • Existing methods often miss targets or generate false alarms, focusing only on position and neglecting crucial shape features for target classification.
  • The inability to identify target categories hinders the full potential of IRST systems.

Purpose of the Study:

  • To address the challenges of missed detections and false alarms in infrared small-target detection.
  • To develop a method that considers both target position and shape features for improved identification.
  • To propose an efficient algorithm that guarantees a reasonable runtime for IRST applications.

Main Methods:

  • A weighted local difference variance measure (WLDVM) algorithm is introduced.
  • Image preprocessing involves Gaussian filtering and matched filter principles to enhance targets and suppress noise.
  • A tri-layer filtering window with a window intensity level (WIL) is used for complexity analysis. A local difference variance measure (LDVM) and background estimation are employed to calculate a weighting function, determining target shape. An adaptive threshold is applied to the WLDVM saliency map for final detection.

Main Results:

  • The WLDVM algorithm effectively enhances small targets against complex backgrounds.
  • The method demonstrates superior performance in reducing missed detections and false alarms compared to seven established algorithms.
  • Experimental validation on nine diverse IR small-target datasets confirms the algorithm's robustness and effectiveness.

Conclusions:

  • The proposed WLDVM algorithm significantly improves infrared small-target detection performance.
  • This method offers a viable solution for enhancing the capabilities of IRST systems.
  • The algorithm's ability to consider target shape features opens avenues for improved target classification in future research.