Asymmetric Contextual Modulation for Infrared Small Target Detection

TL;DR

This paper introduces a new dataset and a novel asymmetric contextual modulation method for infrared small target detection, significantly improving detection performance by combining global and local contextual information.

Contribution

The paper provides the first open dataset for infrared small target detection and proposes an innovative asymmetric modulation module that enhances detection accuracy.

Findings

Our method outperforms state-of-the-art approaches in experiments.

The dataset facilitates future research in infrared small target detection.

Ablation studies confirm the effectiveness of the proposed modulation module.

Abstract

Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset. In this paper, we first contribute an open dataset with high-quality annotations to advance the research in this field. We also propose an asymmetric contextual modulation module specially designed for detecting infrared small targets. To better highlight small targets, besides a top-down global contextual feedback, we supplement a bottom-up modulation pathway based on point-wise channel attention for exchanging high-level semantics and subtle low-level details. We report ablation studies and comparisons to state-of-the-art methods, where we find that our approach performs significantly better. Our dataset and code are available online.