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 paths. The first path employs a Bidirectional-Interaction Module (BIM), which uses both Local Self-Attention and Global Self-Attention to capture multi-scale local and global feature dependencies. The global attention mechanism, based on a Transformer architecture, integrates long-range semantic relationships and contextual information, ensuring robust scene understanding. The second path introduces the Multi-Edge Refiner (Multi-ER), which enhances fine-grained edge details through multi-scale cascaded refinement. Coupled with attention-driven gating, it improves edge localization for targets of varying sizes and suppresses noise effectively. Extensive experiments on the IRSTD-1K, NUDT-SIRST and NUAA-SIRST benchmarks demonstrate that DENet significantly outperforms state-of-the-art methods, achieving superior Mean Intersection over Union and pixel-level accuracy, while maintaining lower false alarm rates. The proposed framework effectively improves infrared small target detection and localization through joint semantic modeling and edge refinement.