IrisNet: Infrared Image Status Awareness Meta Decoder for Infrared Small Targets Detection

TL;DR

IrisNet introduces a meta-learning framework with a dynamic transformer-based decoder that adapts to infrared image status, significantly improving small target detection robustness across diverse scenarios.

Contribution

The paper presents a novel meta-learned infrared detection framework with a transformer-based decoder that dynamically adapts to varying infrared image conditions.

Findings

Achieves state-of-the-art performance on multiple datasets.

Effectively models inter-layer dependencies with self-attention.

Enhances perception by integrating high-frequency image components.

Abstract

Infrared Small Target Detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, complex backgrounds, and the absence of discernible target features. While deep learning-based encoder-decoder frameworks have advanced the field, their static pattern learning suffers from pattern drift across diverse scenarios (\emph{e.g.}, day/night variations, sky/maritime/ground domains), limiting robustness. To address this, we propose IrisNet, a novel meta-learned framework that dynamically adapts detection strategies to the input infrared image status. Our approach establishes a dynamic mapping between infrared image features and entire decoder parameters via an image-to-decoder transformer. More concretely, we represent the parameterized decoder as a structured 2D tensor preserving hierarchical layer correlations and enable the transformer to model inter-layer dependencies through self-attention while generating adaptive decoding patterns via cross-attention. To further enhance the perception ability of infrared images, we integrate high-frequency components to supplement target-position and scene-edge information. Experiments on NUDT-SIRST, NUAA-SIRST, and IRSTD-1K datasets demonstrate the superiority of our IrisNet, achieving state-of-the-art performance.