DB-DCNet: Dual-Branch Dense Collaborative Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is crucial in aerospace and maritime rescue applications. Since imaging devices, such as satellites, are usually at a very long distance from the targets, the targets appear dim in the images. Existing IRSTD methods predominantly employ single-branch architectures with complex feature extraction modules, which paradoxically achieve limited performance improvement as small targets inherently lack sufficient features for effective characterization. To address this issue, we propose a dual-branch dense collaborative network, which integrates two specialized detection branches: one focuses on minimizing missed detections through target-sensitive feature learning and the other aims to reduce false alarms via clutter-aware optimization techniques. Both branches adopt the same densely connected network structure but use different feature extraction modules. One branch leverages small-sized kernels and hybrid attention mechanisms to segment target edges accurately, thus effectively decreasing missed detections. On the contrary, the other branch uses convolutions with multiple receptive fields and residual connections to filter out background clutter, significantly suppressing false alarms. The detection results of the two branches are further processed by a lightweight feature selection network for feature selection and fusion, leading to output results with fewer false alarms and missed detections. Comprehensive experiments on the Earth observation datasets NUDT-SIRST, IRSTD-Air, IRSTD-1k, and WideIRSTD-Full verify the superiority of our proposed method.