From flight to insight: An end-to-end aerial inspection framework for 3D modeling and defect detection of infrastructures

This work presents a complete inspection framework based on Unmanned Aerial Vehicles (UAVs) specifically designed for maritime environments, integrating autonomous data acquisition, high-fidelity three-dimensional (3D) reconstruction, and Artificial Intelligence (AI)-based defect detection and localization. The proposed system combines a custom aerial robotic platform equipped with onboard Light Detection And Ranging (LiDAR) and imaging sensors, a hybrid pose estimation approach that merges real-time LiDAR odometry with an offline refinement stage, and a robust processing pipeline that produces metrically accurate 3D models of vessel structures. Structural anomalies identified through deep learning-based image analysis are projected directly onto the reconstructed models, providing an intuitive spatial representation of the detected defects. The framework was experimentally validated through field trials conducted aboard real maritime vessels within the scope of the BugWright2 project. The results demonstrate accurate reconstruction and reliable defect localization even in environments without satellite-based positioning availability. • A complete aerial inspection framework tailored for maritime environments. • Laser-based odometry and offline refinement for improved 3D accuracy. • Defect detection using artificial intelligence and spatial localization onto reconstructed 3D models. • Validated through real-world field trials aboard two operational vessels. • Enables safe, efficient, and data-driven inspection in satellite-navigation-denied conditions.