Multi-Stage Aggregation CNN–Transformer Hybrid Architecture for Infrared Small-Target Detection

Infrared small-target detection plays a vital role in applications such as imaging detection and remote sensing. Because infrared small targets are weak and lack distinctive features, they are easily submerged in cluttered backgrounds, which makes their detection challenging. Preserving spatial details, jointly modeling local and global features, and strengthening feature representation are therefore critical to improving detection accuracy and robustness. However, existing methods often fail to fully exploit multi-level feature relationships, struggle to integrate local and global complementary information, and lack mechanisms to compress redundant features while emphasizing salient information. To address these issues, we propose MACT-Net, a network designed specifically for infrared small-target detection. First, a multi-level aggregation encoder–decoder architecture with convolution-enhanced skip connections and multi-scale decoder fusion is employed to progressively abstract and reconstruct features, preserving spatial details and improving multi-scale responsiveness. Second, a CNN–Transformer hybrid feature modeling module is introduced to adaptively capture local textures and global semantic dependencies, enhancing feature representation and discriminative capability. Third, a vector quantization-based feature discretization and representation enhancement mechanism is embedded at the network bottleneck to compress redundant information and emphasize salient features, further improving robustness under low signal-to-noise ratio conditions. We evaluate MACT-Net on three public datasets: SIRST, IRSTD-1K, and NUDT-SIRST. Extensive experimental results confirm that the combination of multi-level aggregation, hybrid feature modeling, and feature discretization mechanisms effectively enhances infrared small-target detection performance.