BSG-Mamba: A dual-branch network based on background-suppression sparse graph-enhanced Mamba for infrared small target detection

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Abstract

Infrared small target detection faces numerous challenges due to weak target features and complex backgrounds. Existing methods have drawbacks such as mixed background and target signals, imbalanced local and global feature modeling, and insufficient learning of direction-specific context. To address these issues, this paper proposes a dual-branch network based on background-suppression sparse graph-enhanced Mamba (BSG-Mamba). We build this network on a U-shaped encoder-decoder architecture. We design a background-suppression branch to initially separate the target from the background and combine it with a target-enhancing gating (TEG) mechanism to enhance target features. On this basis, we effectively capture non-local feature correlations in small targets by embedding a sparse graph encoder (SG-encoder), compensating for the shortcomings of traditional convolutional segmentation. At the deep encoder position, it constructs a Mamba module with multi-state space model (SSM) decoupling and adaptive fusion. We configure independent SSM parameters for four-direction scanning and introduce learnable fusion weights to achieve unified modeling of long-range dependencies and direction-specific features. Experimental results on the NUAA-SIRST and NUDT-SIRST datasets show that the core detection metrics of the model outperform existing state-of-the-art (SOTA) methods, achieving detection rates of 97.74% and 98.90%, respectively, on the two datasets, while maintaining a lightweight network parameter count and balancing detection accuracy and computational efficiency.

Keywords

infrared small target detection / background suppression / Unet / sparse graph / Mamba

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