Swaying displacement measurement for structural monitoring using computer vision and an unmanned aerial vehicle

Data acquisition is the challenging and crucial step for any structural health monitoring (SHM) scheme, especially on numerous measurement locations that are typically at very high elevations or largely inaccessible areas, which are often linked to time-consuming, costly, and to some extent, dangerous sensor implementation and cable wiring. Noncontact vision-based measurement techniques have been recognized recently as a primarily feasible approach, although it is still characterized by some limitations. To address these constraints, the proposed study introduced an enhanced noncontact displacement measurement method that employed an unmanned aerial vehicle (UAV) and computer vision algorithms. Since UAV can carry cameras to approach any difficult-to-reach regions, the proposed system can overcome several bottlenecks of the state-of-the-art vision-based methods with regard to finding a stationary place for the camcorder and for mitigating the inaccuracy induced by the long distance between the camcorder and the measurement location. Guided by the schematic framework for the system, a camera was mounted on the UAV for filming of the measurement point, and then displacements on that point were determined by a key-point vision-based measurement method. Moreover, translations generated by the UAV were obtained by means of reference objects on the background. Additionally, an autonomous scheme based on Canny edge detection and Hough transform were introduced for calculation of scale factors between the pixel and engineering unit for every image frame to address the issue of very fluctuant distances from the UAV to the measurement location. Subsequently, the actual displacements of the measurement location were measured following the elimination of the UAV motions from the displacement data. The proposed system was verified on an experiment with a small-sized steel tower where the outcomes provided an initial confirmation of the approach's promising potential. (C) 2020 Elsevier Ltd. All rights reserved.