DENet: Dual-Path Edge Network With Global-Local Attention for Infrared Small Target Detection

Infrared small target detection (IRSTD) is crucial for remote sensing applications like disaster warning and maritime surveillance. However, due to the lack of distinctive texture and morphological features, infrared small targets are highly susceptible to blending into cluttered and noisy backgrounds. Existing methods often rely on fixed gradient operators (e.g., Sobel, Canny) or simplistic attention mechanisms, which are inadequate for accurately extracting target edges under low contrast and high noise. In this paper, we propose an enhanced dual-path edge network (DENet) that explicitly addresses this challenge by decoupling edge enhancement and semantic modeling into two deliberately designed processing…

• NN
  National Natural Science Foundation of China

  Awards: 61772061, 62402054, BX20250390