A Robust Tool for 3D Rail Mapping Using UAV Data Photogrammetry, AI and CV: qAicedrone-Rail - Innes Barbero-García; Diego Guerrero-Sevilla; David Sánchez-Jiménez; David Hernández-López (Drones)
Rail systems are essential for economic growth and regional connectivity, but aging infrastructures face challenges from increased demand and environmental factors. Traditional inspection methods, such as visual inspections, are inefficient and costly and pose safety risks. Unmanned Aerial Vehicles (UAVs) have become a viable alternative to rail mapping and monitoring. This study presents a robust method for the 3D extraction of rail tracks from UAV-based aerial imagery. The approach integrates YOLOv8 for initial detection and segmentation, photogrammetry for 3D data extraction and computer vision techniques with a Multiview approach to enhance accuracy. The tool was tested in a real-world complex scenario. Errors of 2 cm and 4 cm were obtained for planimetry and altimetry, respectively. The detection performance and metric results show a significant reduction in errors and increased precision compared to intermediate YOLO-based outputs. In comparison to most image-based methodologies, the tool has the advantage of generating both accurate altimetric and planimetric data. The generated data exceed the requirements for cartography at a scale of 1:500, as required by the Spanish regulations for photogrammetric works for rail infrastructures. The tool is integrated into the open-source QGIS platform; the tool is user-friendly and aims to improve rail system maintenance and safety.