DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection

TL;DR

DATransNet is a novel neural network that combines dynamic attention, gradient feature extraction, and global context to improve infrared small target detection in complex backgrounds.

Contribution

The paper introduces DATransNet, integrating dynamic attention and global features, advancing small target detection beyond existing methods.

Findings

Outperforms state-of-the-art methods in infrared small target detection

Effectively extracts gradient and global features for better target preservation

Demonstrates robustness in complex background scenarios

Abstract

Infrared small target detection (ISTD) is widely used in civilian and military applications. However, ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds. To address this issue, we propose the Dynamic Attention Transformer Network (DATransNet), which aims to extract and preserve detailed information vital for small targets. DATransNet employs the Dynamic Attention Transformer (DATrans), simulating central difference convolutions (CDC) to extract gradient features. Furthermore, we propose a global feature extraction module (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the global information. We compare the network with state-of-the-art (SOTA) approaches and demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/DATransNet.