DBSAM: A Dual-Branch Segment Anything Model for Infrared Small Target Detection

Infrared small target detection remains challenging due to the inherently low signal-to-noise ratio (SNR), complex background clutter, and indistinct target boundaries. To tackle these interconnected issues, we propose a novel Dual-Branch network based on Segment Anything Model (DBSAM) that couples background suppression with explicit edge structure modeling. The architecture consists of two specialized components: (1) Within the encoder, our proposed Adaptive Wavelet-based Background Suppression (AWBS) modules employ stationary wavelet transform with adaptive thresholding to simultaneously denoise encoder features and enhance target saliency through multi-scale decomposition. (2) In parallel, the Edge-Aware Fusion Branch (EAFB) processes the Sobel-derived edge maps utilizing dynamic snake-like receptive fields to precisely capture fine-grained boundary structures. Extensive experiments on four benchmark datasets demonstrate that DBSAM outperforms existing methods across multiple evaluation metrics, including detection probability(Pd), false alarm rate(Fa), and Intersection over Union(IoU). Ablation studies further validate the contributions of both AWBS and EAFB to enhance overall performance.