DBGNet: Dual-Branch Gate-Aware Network for Infrared Small Target Detection

Abstract

Infrared small target detection is crucial in military applications such as guidance, early warning, and UAV detection. Errors in infrared small target detection are classified as either miss detection (MD) or false alarm (FA). An effective detector should minimize both MD and FA. However, conventional approaches often rely on a single strategy to reduce overall detection errors, which can result in either MD or FA. To address this, we propose a dual-branch gate-aware network (DBGNet) model, that consists of two branches, each learning feature to reduce MD and FA, respectively. Specifically, a multi-scale full convolutional network (MFCN) is first applied to extract different level features to preserve the information of small infrared targets. Additionally, we introduce a multi-scale space and channel gate fusion module (MSCGFM) to ensure the independence of the two branches. Each branch is associated with its own learning objective loss function, enabling them to learn distinct discriminations while being constrained by the same category labels. Moreover, the features from both branches are fused to create a feature representation for each pixel in the image, addressing both MD and FA and minimizing MD while also reducing FA. Finally, the fused features from the two branches are passed through a classification head to generate prediction results. Extensive experimental results demonstrate that DBGNet outperforms other methods on three existing infrared small target datasets.