Reliable helipad detection is essential for Autonomous Aerial Vehicle (AAV) landing, especially under GPS-denied or visually degraded conditions. While modern detectors such as YOLOv8 offer strong baseline performance, single-model pipelines struggle to remain robust across the extreme scale transitions that occur during descent, where helipads appear small at high altitude and large near touchdown. To address this limitation, we propose a scale-adaptive dual-expert perception framework that decomposes the detection task into far-range and close-range regimes. Two YOLOv8 experts are trained on scale-specialized versions of the HelipadCat dataset, enabling one model to excel at detecting small, low-resolution helipads and the other to provide high-precision localization when the target dominates the field of view. During inference, both experts operate in parallel, and a geometric gating mechanism selects the expert whose prediction is most consistent with the AAV's viewpoint. This adaptive routing prevents the degradation commonly observed in single-detector systems when operating across wide altitude ranges. The dual-expert perception module is evaluated in a closed-loop landing environment that integrates CARLA's photorealistic rendering with NASA's GUAM flight-dynamics engine. Results show substantial improvements in alignment stability, landing accuracy, and overall robustness compared to single-detector baselines. By introducing a scale-aware expert routing strategy tailored to the landing problem, this work advances resilient vision-based perception for autonomous descent and provides a foundation for future multi-expert AAV frameworks.

翻译：可靠的直升机停机坪检测对于自主空中载具（AAV）着陆至关重要，尤其是在GPS信号缺失或视觉条件退化的环境中。尽管YOLOv8等现代检测器提供了强大的基线性能，但单模型流水线在着陆过程中发生的极端尺度变换下难以保持鲁棒性——停机坪在高空时呈现为小目标，在接近触地时则变为大目标。为解决这一局限，我们提出一种尺度自适应的双专家感知框架，将检测任务分解为远距离和近距离两种状态。两个YOLOv8专家模型在HelipadCat数据集的尺度专用版本上进行训练，使一个模型擅长检测低分辨率的小型停机坪，另一个模型则在目标占据视野主导时提供高精度定位。在推理过程中，两位专家并行运行，几何门控机制会选择其预测与AAV视点最一致的专家。这种自适应路由机制避免了单检测器系统在宽高度范围内运行时常见的性能退化。该双专家感知模块在闭环着陆环境中进行评估，该环境集成了CARLA的光照真实渲染引擎与NASA的GUAM飞行动力学引擎。实验结果表明，相较于单检测器基线，该系统在对准稳定性、着陆精度和整体鲁棒性方面均有显著提升。通过引入针对着陆问题量身定制的尺度感知专家路由策略，本研究推进了基于视觉的自主下降鲁棒感知技术，并为未来多专家AAV框架奠定了基础。