Adaptive Enhancement Scheduler for Real-Time UAV Object Detection Under Variable Illumination

Abstract

UAV vision systems must operate under highly variable illumination, from daylight to full night, while adhering to strict real-time constraints. Low-light conditions suppress texture and contrast, compounding the difficulty of detecting small objects at high altitudes. Image enhancement can restore visibility, but applying it unconditionally incurs fixed compute cost regardless of scene content, degrading already-bright frames while benefiting dark ones. This paper proposes a lightweight three-signal scheduler that decides per frame whether to bypass, apply gamma correction, or invoke Zero-DCE enhancement, based on scene brightness, detector confidence feedback, and inter-frame motion. A motion veto suppresses enhancement on blurred frames where it adds artifacts rather than recovering detail. On a mixed-illumination VisDrone validation set, the scheduler achieves 45.0 FPS and 22.8% mAP@50 on a GTX 1080 Ti, invoking Zero-DCE on only 27.7% of frames, demonstrating that an adaptive per-frame scheduler makes enhancement-based detection pipelines viable for real-time UAV deployment.