DABF-Net: A Dual-Branch Attention-Guided and Bi-Directional Feature Enhancement Network for Infrared Small-Target Detection With Air-to-Ground Benchmark

Infrared small-target detection (IRSTD) is a critical, yet challenging task with significant applications in both military and civilian domains. Despite advancements in existing methods, two major limitations remain: the difficulty of achieving an optimal balance between detection probability and false alarm rate, and the lack of specialized datasets for air-to-ground scenarios. To address these limitations, this article presents a dual-pronged solution. At the algorithmic level, we propose a novel dual-branch attention-guided and bi-directional feature enhancement network (DABF-Net). First, we design a dual-branch high-low frequency attention (DHLA), which enhances the discriminability between the target and the background by preserving high-frequency edge features and modeling low-frequency contextual information in a complementary manner. Subsequently, we construct a bi-directional fusion module (BFM) to optimize multiscale feature compatibility while suppressing redundant information propagation. Furthermore, we introduce a small-target feature enhancement branch (STEB) employing space-to-depth (SPD) convolution and a feature integration module (FIM) to amplify latent target signatures through exponentially expanding receptive fields. At the data level, we contribute the NCHU-A2G-SIRST benchmark, the first comprehensive dataset specifically designed for the air-to-ground IRSTD task. The dataset contains four different scenes with two types of annotations, enabling evaluation and training of detection models in real-world conditions. Extensive experiments on several challenging datasets, including the self-built NCHU-A2G-SIRST dataset and three public datasets (NCHU-SIRST, NUAA-SIRST, and IRSTD-1K), demonstrate that the DABF-Net can outperform many state-of-the-art competing methods. The code and dataset are publicly available at https://github.com/PCwenyue/DABF-Net