An IoT System to Measure and Detect Foreign Object Debris (FOD)on Airport Runways Using AI and Computer Vision - Nguyen-Hoang, Hiep; Nguyen-Van, Quyen; Tran-Quang, Vinh (Smart Systems and Devices, 2025)
Air transportation is one of the fastest and safest modes of transport today. However, ensuring its safety and efficiency requires effective management of various risk factors, particularly foreign object debris (FOD) on airport runways. FOD can cause severe damage to aircraft engines and structures, disrupt operations, and even lead to fatal accidents. This study presents a method for detecting, classifying, and estimating the size of FOD on airport runways. The system integrates YOLOv11, a calibrated camera, Canny Edge Detection, and a LiDAR sensor and deploy on a Jetson Nano for efficient processing. Experimental results demonstrate the ability of the system to accurately classify FOD and measure its size in real time. The system achieves an accuracy ranging from 70% to 100% within a 0 to 3 metre distance between the FOD and the camera. This contributes to more efficient FOD detection and collection using robots or rovers, thereby enhancing runway safety and reducing risks in aviation operations.