PromptScaleDINO: Prompt-Stabilized and Scale-Aware Adaptation of Grounding DINO for Infrared Small Target Detection

Infrared small target detection (IRSTD) supports remote-sensing surveillance and early warning, but infrared targets often occupy only a few pixels, exhibit weak appearance cues, and resemble thermal clutter. Vision-language detectors such as Grounding DINO provide a flexible prompt-driven detection interface, but direct transfer to IRSTD faces three mismatches: a single prompt cannot describe target appearance diversity, generic decoder queries are not calibrated for weak scale-sensitive targets, and IoU-style localization losses are unstable for few-pixel boxes. We propose PromptScaleDINO as a lightweight adaptation framework for Grounding DINO. It introduces an Anchor Prompt Bank (APB) to enrich the text input while keeping supervision connected to a stable anchor phrase, a Scale-Aware Query Refinement Network (SQRN) to refine selected queries according to predicted box scale and semantic confidence, and a geometry-aware localization objective that combines Normalized Wasserstein Distance (NWD) with Generalized Intersection over Union (GIoU). Under a unified detection protocol, PromptScaleDINO achieves its largest gains on the challenging real-scene IRSTD-1k benchmark while maintaining near-ceiling performance on SIRST and NUDT-SIRST. On IRSTD-1k, it reaches 88.32% F1 and 87.40% mAP@0.5, improving the baseline by 2.56 and 4.00 percentage points, respectively. These gains mainly reflect improved weak-target recovery at comparable precision; accordingly, we position the framework as a target-sensitivity and localization adaptation rather than an explicit false-alarm suppression method.