Reliable Tiny-Airborne-Object Detection via Prior-Guided Motion-Structure Verification

Abstract

Reliable vision-based sensing of tiny-airborne objects is important for airspace surveillance and aerial monitoring. In practical scenarios, airborne objects are typically captured at long distances, occupying only a few pixels and exhibiting low signal-to-clutter ratios (SCRs) against complex backgrounds. These factors frequently cause missed detections and false alarms, thereby degrading reliable target detection and overall sensing reliability. Existing approaches seek to address these challenges by exploiting temporal cues across frames to stabilize weak object responses. However, they rarely impose explicit reliability-oriented verification on such cues, allowing clutter-induced spurious temporal responses to persist and undermine reliable detection. To address this issue, we propose motion-structure verification (MoSVer) for reliable learning, which introduces explicit joint verification of motion-derived temporal cues and structural cues to yield verified evidence for reliability-oriented supervision. Specifically, the motion-derived cue extraction (MDCE) module generates a motion support map to capture target-relevant motion evidence against the background. Meanwhile, the structure-constrained cue extraction (SCCE) module extracts a structural support map from regions exhibiting cross-frame structural consistency. Finally, the verification-guided matching (VGM) module integrates the two support maps to derive verified evidence, which is used as a reliability-aware matching prior to favor assignments supported by both cues. With RT-DETR as the base detector, MoSVer improves mAP ${}_{50:95}$ by 0.042 and 0.011 on airborne object tracking (AOT) and UAVSwarm, respectively, while reducing FP/frame@R = 0.50 by 17.8 % and 16.0 %. It also lowers MR@FP = 1 by 1.9 and 0.5 percentage points and decreases expected calibration error (ECE) by 0.025 and 0.009, providing quantitative evidence of improved detection reliability in clutter-dominated, low-SCR scenes.