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

arXiv: https://arxiv.org/abs/1810.03173 · HTML: https://mlsanity.com/paper/1810.03173/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds](https://mlsanity.com/paper/2109.14379/) (2021)
- [CSPENet: Contour-Aware and Saliency Priors Embedding Network for Infrared Small Target Detection](https://mlsanity.com/paper/2505.09943/) (2025)
- [PixelGame: Infrared small target segmentation as a Nash equilibrium](https://mlsanity.com/paper/2205.13124/) (2022)
- [CourtNet for Infrared Small-Target Detection](https://mlsanity.com/paper/2209.13780/) (2022)
- [Enhancing Evaluation Methods for Infrared Small-Target Detection in Real-world Scenarios](https://mlsanity.com/paper/2301.03796/) (2023)
- [Dynamic Background Reconstruction via MAE for Infrared Small Target Detection](https://mlsanity.com/paper/2301.04497/) (2023)
- [Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection](https://mlsanity.com/paper/2407.07289/) (2024)
- [Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection](https://mlsanity.com/paper/2407.15369/) (2024)
- [Bidirectional Temporal Information Propagation for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2508.15415/) (2025)

## Abstract (arXiv)

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.