用统计方法给视觉着陆系统的跑道检测加安全保险，确保出错概率可控。
Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP
- 用校准预测技术量化跑道定位的不确定性，可设定风险阈值。
- 在空中影像上验证，模型在95%置信度下对跑道定位更可靠。
- 新指标C-mAP让检测性能与可靠性直接挂钩，适合航空安全场景。
本文探索将校准预测应用于视觉着陆系统中的跑道检测，以提供统计意义上的不确定性保障。基于细调后的YOLOv5和YOLOv6模型，在航空影像上应用校准预测，量化用户定义风险水平下的定位可靠性。我们提出一种新型指标Conformal mean Average Precision（C-mAP），使目标检测性能与校准保证相一致。实验表明，校准预测能以统计严谨的方式提升跑道检测的可靠性，增强机载系统的安全性，并为机器学习系统在航空航天领域的认证铺平道路。
原文摘要 · Abstract (English)
We explore the use of conformal prediction to provide statistical uncertainty guarantees for runway detection in vision-based landing systems (VLS). Using fine-tuned YOLOv5 and YOLOv6 models on aerial imagery, we apply conformal prediction to quantify localization reliability under user-defined risk levels. We also introduce Conformal mean Average Precision (C-mAP), a novel metric aligning object detection performance with conformal guarantees. Our results show that conformal prediction can improve the reliability of runway detection by quantifying uncertainty in a statistically sound way, increasing safety on-board and paving the way for certification of ML system in the aerospace domain.
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