A Unified SAM-Guided Self-Prompt Learning Framework for Infrared Small Target Detection

Infrared small target detection (ISTD) aims to precisely capture the location and morphology of small targets under all-weather conditions. Compared with generic objects, infrared targets in remote fields of view are smaller in size and exhibit lower signal-to-clutter ratios. This poses a significant challenge in simultaneously preserving low-level target details and understanding high-level contextual semantics, forcing a trade-off between reducing miss detection and suppressing false alarms. In addition, most existing ISTD methods are designed for specific target types under certain infrared platforms, rather than as a unified framework broadly applicable across diverse infrared sensing scenarios. To address these challenges, we propose a unified self-prompt learning framework for ISTD under the guidance of the Segment Anything Model (SAM). Specifically, the model is incorporated with SAM in the encoding stage through a consult-guide manner, adapting the general knowledge to facilitate task-specific contextual understanding. Then, shallow-layer features are employed to generate self-derived prompts, which bidirectionally interact with encoded latent representations to complement subtle low-level details. Moreover, the semantic inconsistency during resolution recovery is mitigated by integrating a mutual calibration module into skip connections, ensuring coherent spatial-semantic fusion. Extensive experiments are conducted on four public ISTD datasets, and the results demonstrate that the proposed method consistently achieves superior performance across different infrared sensing platforms and target types. The code is released at https://github.com/fuyimin96/SAM-SPL.