ISGLNet: Infrared Small Target Detection With Intrinsic Sensitivity and Guided Learning

Infrared small target detection (IRSTD) plays a critical role in both civilian and military applications, yet it still faces inherent challenges stemming from faint targets, complex noise interference, and difficulties in preserving shape integrity. Despite significant progress in detecting general small targets, existing methods often struggle to balance detection accuracy and false alarms due to limited sensitivity to low-intensity signals, inaccurate perception of confusing noise, and inadequate edge refinement. To break this dilemma, we propose ISGLNet, which centers on a U-shaped architecture specifically tailored to preserve salient target responses, along with a guided learning strategy that progressively enhances target–noise distinction while refining boundary details. Specifically, we introduce the Context-aware Local-Global Module (CLGM) as the cornerstone of the model, which incorporates multi-branch large receptive fields and multi-dimensional adaptive attention mechanisms, effectively capturing rich contexts while preserving critical target information. This ensures reliable feature modeling throughout the extraction and fusion process. Furthermore, the Multi-frequency Perception Module (MFPM) and the Edge Refinement Module (ERM) replace conventional skip connections to refine semantic patterns through guidance. Among these, the MFPM operates in the deeper layers, primarily identifying discriminative clues by evaluating and dynamically selecting multi-frequency information to amplify the distinction between targets and complex noise. The ERM further works in the shallower layers with a progressive strategy to refine uncertain target boundaries, enabling precise segmentation of fine-grained target shapes. Extensive experiments on multiple public datasets demonstrate that ISGLNet achieves superior performance in both detection and segmentation accuracy. The code is available at https://github.com/fuqingzhang/ISGLNet.