AI-Powered Runway Safety: YOLOv11-Based Detection of Foreign Object Debris
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Abstract
Runway safety is a critical aspect of aviation operations, with Foreign Object Debris (FOD) posing severe risks to aircraft during takeoff and landing. Incidents such as the Air France Flight 4590 (Concorde) crash have demonstrated the devastating impact of undetected FOD. Manual inspection methods remain the standard but are time-consuming, error-prone, and limited by environmental conditions. With annual global FOD-related losses estimated at over $22.7 billion, there is a clear need for automated, intelligent detection systems.
This study presents an AI-powered system using computer vision and deep learning to detect and classify runway FOD in real time. Leveraging the YOLOv11 model trained on a combination of the open-source FOD-A dataset and custom-collected images, the system achieves a mean Average Precision (mAP@95) of 89.3%. Data augmentation, class balancing, and annotation with CVAT further enhance model performance. The trained model is deployed via a web-based application with an Angular frontend and Flask backend, enabling rapid detection with high precision and user-friendly visualization.
While the current system is optimized for image-based detection, future work will focus on real-time video integration, edge device deployment, and airport safety system interoperability. Limitations include partial coverage of real-world conditions and small debris detection challenges, which are being addressed through ongoing dataset expansion and model refinement.
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Runway Safety, Foreign Object Debris, YOLOv11, Object Detection, Deep Learning, Aviation Safety, FOD-A Dataset, Computer Vision, PHM, Edge AI
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