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.

翻译：红外小目标检测（ISTD）在民用和军事领域均有广泛应用。然而，ISTD面临若干挑战，包括弱小目标易被复杂背景淹没的倾向。为解决这一问题，我们提出了动态注意力Transformer网络（DATransNet），旨在提取并保留对小目标至关重要的细节信息。DATransNet采用动态注意力Transformer（DATrans），通过模拟中心差分卷积（CDC）来提取梯度特征。此外，我们提出了全局特征提取模块（GFEM），该模块提供全局视角，以防止网络仅关注细节而忽略整体信息。我们将该网络与当前最先进（SOTA）方法进行比较，结果表明所提方法性能优异。我们的源代码公开于 https://github.com/greekinRoma/DATransNet。