DBSAM: A Dual-Branch Segment Anything Model for Infrared Small Target Detection (opens in new tab)

Infrared small target detection (IRSTD) 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 the 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 backgro...