Structural health monitoring (SHM) has been conducted by placing sensors on structures, so-called contact sensors, to measure displacements, strains, and accelerations of engineering structures. Contact sensing is accurate and effective, but suffers from some critical shortcomings, such as high cost, intensive labor, and traffic interruption. With the advancement and wide availability of digital video cameras, compounded with the development and enhancement of computer vision algorithms, vision-based sensing using a video camera as a sensor for SHM has become a viable alternative and a complementary method to remotely capture structural responses. Several computer vision algorithms are proposed, together with specific type of video camera, for capturing and processing the SHM video for the dynamic structural responses, e.g. displacements of selected targets. Up to date, there is a limited number of successful applications using video camera as a sensor in SHM practice. To facilitate the real-world applications of vision-based SHM, this paper presents the integrated methods, and a software tool for analyzing SHM videos using template matching algorithms along with a subpixel method that enhances the accuracy of the vision-based structural responses. The integrated approach is flexible and versatile for not only extracting displacements, strains, velocities, and accelerations, but also detecting damage of the desired multiple points or areas (templates) from videos. The methods and the software tool have been applied to a comprehensive analysis of the lab structural test, which was video-recorded by a third party without any involvement of the authors or any intension in using video camera as sensor. Good agreement for the lab test has been achieved for the structural responses (e.g. displacement and strain) and the damage detection recorded by the conventional sensors and extracted from the video by applying the integrated tool. The approach has been further validated on a highway bridge in the field, where a contact-based SHM system is permanently installed on the bridge and serves a good baseline reference for validating the proposed approach. The vision-based SHM results obtained for the highway bridge application show good correlation with the structural responses captured by conventional sensors and indicate that the developed analysis methods and software tool perform well for highway bridge test and structural health monitoring.

中文翻译：

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使用相机作为传感器的模板匹配方法开发视频分析，并将其应用于公路桥梁结构健康监测

通过将传感器放置在结构上（即所谓的接触传感器）来进行结构健康监测（SHM），以测量工程结构的位移，应变和加速度。接触感测是准确而有效的，但存在一些严重的缺点，例如高成本，密集的劳动和交通中断。随着数字摄像机的发展和广泛应用，以及计算机视觉算法的发展和增强，使用摄像机作为SHM传感器的基于视觉的传感已成为一种可行的替代方法，是一种远程捕获结构响应的补充方法。提出了几种计算机视觉算法，以及特定类型的摄像机，用于捕获和处理用于动态结构响应的SHM视频，例如 选定目标的位移。迄今为止，在SHM实践中，使用摄像机作为传感器的成功应用数量有限。为了促进基于视觉的SHM在现实世界中的应用，本文介绍了集成方法以及使用模板匹配算法和亚像素方法来分析基于SHM的视频的软件工具，从而提高了基于视觉的SHM的结构响应的准确性。集成的方法灵活且通用，不仅可以提取位移，应变，速度和加速度，还可以从视频中检测所需的多个点或区域（模板）的损坏。这些方法和软件工具已应用于实验室结构测试的综合分析，它是由第三方录制的视频，没有作者的参与，也没有使用摄像机作为传感器的意图。对于结构响应（例如位移和应变）以及由常规传感器记录并通过应用集成工具从视频中提取的损伤检测，实验室测试已达成良好协议。该方法已在野外的公路桥梁上得到了进一步验证，其中基于接触的SHM系统永久安装在桥梁上，并为验证所提出的方法提供了良好的基准。