Glare-Aware Resi-YOLO: Tiny-Vessel Detection with Dual-Brain Edge Deployment for Maritime UAVs

Maritime UAV perception must reliably detect and track tiny vessels under harsh specular glare. In practice, detection failures are dominated by two coupled factors: (i) vessels often occupy only a few pixels, causing small-object recall collapse and (ii) sun glint and sea-surface reflections generate over-exposed regions that trigger false positives and unstable associations. This paper presents Resi-YOLO, a system-level pipeline that improves tiny-vessel sensitivity while preserving embedded throughput on a Jetson Orin Nano. At the model level, Resi-YOLO combines a P2-enhanced feature path with CBAM-based glare suppression to strengthen high-resolution semantics and suppress glare-induced artifacts; optional SAHI-style slicing is supported for ultra-high-resolution scenes. At the system level, we adopt a heterogeneous dual-brain deployment, where the Orin Nano performs primary inference and an MCU-based safety-island tracker mitigates delay/jitter via time-stamped measurement replay and IMM-UKF updates. We further define a Glare Severity Score (GSS) to stratify robustness by illumination intensity. Experiments show that Resi-YOLO improves APsmall by 13.1 percentage points over YOLOv8n (18.4% to 31.5%), raises high-glare [email protected] from 41.2% to 53.7%, and runs at 12.8 FPS end-to-end (~100 ms latency) on Jetson Orin Nano, while TensorRT inference-only throughput exceeds 30 FPS.