Preprint Article Version 1 This version not peer reviewed

Utilizing UAV and 3D Computer Vision for Visual Inspection of a Large Gravity Dam

Version 1 : Received: 24 January 2018 / Approved: 25 January 2018 / Online: 25 January 2018 (05:00:51 CET)

How to cite: Khaloo, A.; Lattanzi, D.; Jachimowicz, A. Utilizing UAV and 3D Computer Vision for Visual Inspection of a Large Gravity Dam. Preprints 2018, 2018010235 (doi: 10.20944/preprints201801.0235.v1). Khaloo, A.; Lattanzi, D.; Jachimowicz, A. Utilizing UAV and 3D Computer Vision for Visual Inspection of a Large Gravity Dam. Preprints 2018, 2018010235 (doi: 10.20944/preprints201801.0235.v1).

Abstract

Dams are a critical infrastructure system for many communities, but they are also one of the most challenging to inspect. Dams are typically very large and complex structures, and the result is that inspections are often time-intensive and require expensive, specialized equipment and training to provide inspectors with comprehensive access to the structure. The scale and nature of dam inspections also introduces additional safety risks to the inspectors. Unmanned aerial vehicles (UAV) have the potential to address many of these challenges, particularly when used as a data acquisition platform for photogrammetric three-dimensional (3D) reconstruction and analysis, though the nature of both UAV and modern photogrammetric methods necessitates careful planning and coordination for integration. This paper presents a case study on one such integration at the Brighton Dam, a large-scale concrete gravity dam in Maryland, USA. A combination of multiple UAV platforms and multi-scale photogrammetry was used to create two comprehensive and high-resolution 3D point clouds of the dam and surrounding environment at intervals. These models were then assessed for their overall quality, as well as their ability to resolve flaws and defects that were artificially applied to the structure between inspection intervals. The results indicate that the integrated process is capable of generating models that accurately render a variety of defect types with sub-millimeter accuracy. Recommendations for mission planning and imaging specifications are provided as well.

Subject Areas

infrastructure inspection; computer vision; structure from motion; dam inspection; 3D scene reconstruction; aerial robots; remote sensing; structural health monitoring; unmanned aerial vehicles

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