Real-time multiscale UAV detection via hybrid framework in infrared imagery

Detecting small UAVs in thermal infrared imagery is challenging because targets often occupy only a few pixels, have weak contrast, and appear in cluttered backgrounds. We propose a real-time hybrid detector that runs YOLOv11-s and a multiscale Relative Local Contrast Measure (RLCM) branch in parallel, then fuses their candidate detections at the bounding-box level before lightweight temporal confirmation with a probabilistic data association filter (PDAF). The PDAF associates detections across nearby frames, suppresses isolated clutter responses, and confirms persistent targets. On Anti-UAV410, the proposed method improves AP over YOLOv11-s, with the largest gains on tiny targets, increasing AP_t from 0.47 to 0.70. On Jetson AGX Orin Industrial, the full pipeline runs in 4.53ms per frame versus 4.42ms for YOLOv11-s alone, adding only 0.11ms latency. These results show that candidate-level fusion of semantic confidence and contrast-based saliency, together with lightweight temporal confirmation, improves tiny infrared UAV detection while preserving real-time embedded performance.