DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection
This work addresses infrared small target detection for civilian and military applications, representing an incremental improvement over existing methods.
The paper tackles the problem of detecting small, dim infrared targets obscured by complex backgrounds by proposing DATransNet, a dynamic attention transformer network that integrates gradient feature extraction and global feature modules, achieving effective performance compared to state-of-the-art methods.
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.