The deflection of railroad bridges under in-service loads is an important indicator of the structure's health. Over the past decade, an increasing number of studies have demonstrated the efficacy of using vision-based approaches for displacement tracking of civil infrastructure. These studies have relied primarily on external processing of manually recorded videos of a structure's motion to estimate displacements. To date, vision-based techniques applied to long-term structural health monitoring have yet to be proven effective as an alternative to the traditional displacement measurement methods, such as linear variable differential transformers. This paper proposes a wireless SmartVision system (WSVS) that uses edge computing to directly output bridge displacements that can be sent to the end user. The system estimates displacements using both target-free and target-based approaches. A synchronized sensing framework is developed for multipoint displacement estimation using several wireless vision-based nodes for full-scale displacement-based modal analysis of structures. Pose estimation using an AprilTag, a fiducial marker, is employed with a modified algorithm for improved displacement tracking of targets installed on a bridge, yielding subpixel accuracy. The robustness of the results in field conditions is enhanced by linking a tracking quality factor to each timestamp to handle vision-related uncertainties. To meet the need for precise error metrics evaluation, an inexpensive cyber-physical setup using a synthetic testing environment is also developed in this study. Following laboratory validation, field tests on a cable-stayed pedestrian bridge were performed to demonstrate the efficacy of the proposed WSVS.

中文翻译：

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用于铁路桥梁同步位移监测的无线 SmartVision 系统

铁路桥梁在使用载荷下的挠度是结构健康状况的重要指标。在过去的十年中，越来越多的研究证明了使用基于视觉的方法对民用基础设施进行位移跟踪的有效性。这些研究主要依靠对手动记录的结构运动视频的外部处理来估计位移。迄今为止，应用于长期结构健康监测的基于视觉的技术尚未被证明可以有效替代传统的位移测量方法，例如线性可变差动变压器。本文提出了一种无线 SmartVision 系统 (WSVS)，该系统使用边缘计算直接输出可发送给最终用户的桥梁位移。该系统使用无目标和基于目标的方法估计位移。开发了一种同步传感框架，用于使用多个基于无线视觉的节点进行多点位移估计，以对结构进行基于位移的全尺寸模态分析。使用 AprilTag（基准标记）的姿势估计与改进的算法一起使用，以改进安装在桥梁上的目标的位移跟踪，从而产生亚像素精度。通过将跟踪质量因子链接到每个时间戳以处理与视觉相关的不确定性，可以增强现场条件下结果的稳健性。为了满足精确错误度量评估的需要，本研究还开发了一种使用合成测试环境的廉价网络物理设置。经过实验室验证，