A Hierarchical Computer Vision Approach to Infrastructure Inspection

Currently, most infrastructure inspection standards require inspectors to visually assess structural integrity and log findings for comparison during future inspections. This process is qualitative and often inconsistent. Furthermore, changes in inspection protocols over time can create discontinuities in understanding the time-history of a structure. This paper presents a systematic technique for capturing and representing inspection data, leveraging a newly developed hierarchical computer vision methodology. This technique can be used to improve the level of accuracy in condition assessment procedures by allowing inspectors to recreate the structural inspection scenario on a computer through a high-fidelity virtual environment. The methodology presented herein utilizes adaptations of the Dense Structure from Motion (DSfM) algorithm, which reconstructs three-dimensional (3D) scenes from two-dimensional (2D) digital images. In order to produce highly-accurate and photorealistic 3D reconstructions there are four core stages: (i) an image capture plan that covers all views of the structure and emphasizes critical details, (ii) reconstruction of an initial dense point cloud to generate a geometrically accurate 3D model, (iii) reconstruction of separate, higher density point clouds of critical details or suspected deficiencies, (iv) application of robust computer vision algorithms to hierarchically register and merge the point clouds. The result of this approach is a virtual 3D model of the structure with accurate geometry and high-fidelity representation of fine details. The accuracy and adaptability of the developed technique was compared to both conventional DSfM reconstruction methods and terrestrial 3D laser scanning (TLS). The experimental validation indicates that the hierarchical technique produces denser and more comprehensive models with an accuracy of one tenth of a millimeter, an order of magnitude improvement over either conventional DSfM or TLS.