Compact Visual Detection of Small Drones Through Contextual Statistics and Geometric Consistency

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Accurate detection of distant drones requires a compact network that preserves weak visual cues. We combine a separable context pyramid (SCP), dual-context energy attention (DCEA), and geometry-calibrated regression (GCR) within a YOLO11n detector. Across repeated training runs on UAV-6E, the full model reached 94.25% validation mAP@0.5 (SD 0.29%), compared with 91.13% (SD 0.59%) for YOLO11n and 93.58% (SD 0.21%) for SCP alone. SCP supplied most of the gain. In the external DUT-2200 evaluation, full-model AP@0.75 was 54.55% (SD 0.47%) versus 51.13% (SD 1.80%) for YOLO11n. The full detector used 2.33 million parameters and 5.27 GFLOPs at 640 by 640 pixel input, compared with 2.59 million and 6.44 GFLOPs for YOLO11n. The embedded implementation achieved 26.3 FPS on the Orange Pi 5 Plus RK3588 platform, supporting practical camera-based UAV monitoring.