Asymmetric Contextual Modulation for Infrared Small Target Detection
This work addresses the problem of detecting small targets in infrared images for applications like surveillance, though it is incremental as it builds on existing methods with a new module and dataset.
The paper tackles the challenge of single-frame infrared small target detection by introducing a new dataset and an asymmetric contextual modulation module, achieving significantly better performance compared to state-of-the-art methods.
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.