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.

对于任何结构健康监测（SHM）方案，数据采集都是具有挑战性和关键性的一步，尤其是在许多测量场所，这些测量场所通常处于很高的高度或人迹罕至的地区，这通常与耗时，昂贵且在某种程度上相关联，危险的传感器实现和电缆接线。尽管基于非接触式视觉的测量技术仍存在一些局限性，但最近已被认为是一种主要可行的方法。为了解决这些限制，建议的研究引入了一种增强的非接触式位移测量方法，该方法采用了无人飞行器（UAV）和计算机视觉算法。由于无人机可以携带摄像机来接近任何难以到达的区域，所提出的系统可以克服现有技术中基于视觉的方法的几个瓶颈，从而找到便携式摄像机的固定位置，并减轻便携式摄像机和测量位置之间的长距离引起的不准确性。在系统的示意性框架的指导下，将摄像机安装在无人机上以拍摄测量点，然后通过基于关键点视觉的测量方法确定该点上的位移。而且，由无人机产生的翻译是通过背景上的参考对象获得的。另外，引入了一种基于Canny边缘检测和Hough变换的自主方案，用于计算每个图像帧的像素和工程单位之间的比例因子，以解决从无人机到测量位置的距离波动很大的问题。随后，在从位移数据中消除了无人机运动之后，测量了测量位置的实际位移。拟议的系统在小型钢塔的实验中得到了验证，其结果为该方法的潜在潜力提供了初步确认。