Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP

• URL: http://arxiv.org/abs/2505.16740v1
• Date: Thu, 22 May 2025 14:52:59 GMT
• Title: Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP
• Authors: Alya Zouzou, Léo andéol, Mélanie Ducoffe, Ryma Boumazouza,
• Abstract summary: We explore the use of conformal prediction to provide statistical uncertainty guarantees for runway detection in vision-based landing systems (VLS)<n>Using fine-tuned YOLOv5 and YOLOv6 models on aerial imagery, we apply conformal prediction to quantify localization reliability under user-defined risk levels.<n>Our results show that conformal prediction can improve the reliability of runway detection by quantifying uncertainty in a statistically sound way.
• Score: 1.3794573109655741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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