Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm

TL;DR

This paper introduces a novel directional infrared small target detection algorithm called ADMD, which effectively suppresses complex backgrounds and enhances target detection in infrared search systems.

Contribution

The paper presents the ADMD algorithm that incorporates directional information for improved small target detection in infrared images, along with an efficient implementation method.

Findings

Effective suppression of structural backgrounds.

Enhanced target detection accuracy.

Proven performance on real infrared images.

Abstract

Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.