Automated Spatiotemporal Tracking of Crack Evolution in Concrete Structures Using UAV and Point Clouds
Abstract
Spatiotemporal change detection of surface cracks in concrete structures is of great importance for evaluating and maintaining their structural health. The development of robotics and 3D computer vision technologies provides new solutions for key subtasks in this process, including automated data acquisition, spatial localization and quantification of cracks, and multi-temporal crack registration. This study proposes an automated UAV- and point cloud-based framework for detecting spatiotemporal changes in cracks in concrete structures. First, the proposed autonomous UAV path-planning algorithm is used to achieve data acquisition that conforms to complex structural geometries. Then, an improved SfM algorithm is employed to realize spatial crack localization and local point cloud densification. Finally, accurate registration of crack point clouds from different periods is achieved based on a two-step registration strategy. Experimental results on a real large-scale concrete structure show that the proposed path-planning algorithm can achieve complete envelope coverage conforming to the structural geometry, with an effective coverage ratio above 99.7%. The dimensional error of structural reconstruction is controlled within 20 mm. The average crack localization time is 4.00 s, and mean absolute error of crack width quantification is 0.44 mm. The average crack registration error is 0.97 mm, thereby enabling accurate tracking of crack evolution.