Traffic signal mast arm structures must be regularly inspected for cracking, bolt loosening, and other signs of deterioration. Due to large inventories, physical inspections and/or dedicated monitoring systems can be prohibitively time-consuming and expensive to implement at a large scale. However, the growing use of vision-based methods for structural monitoring applications introduces the possibility of leveraging video footage from existing traffic cameras for this purpose. The extraction of dynamic properties (i.e., natural frequencies and damping) from this footage could be employed in detecting possible signs of deterioration. This study presents a vision-based monitoring method which uses a single traffic camera to identify the modal properties of the supporting traffic signal mast arm. This was achieved via operational modal analysis on pixel displacements obtained from a traffic camera mounted on a traffic signal mast arm in Norfolk, VA, monitored during July, 2019. First, sub-pixel displacements were extracted frame-by-frame using weighted centroid tracking of pavement markings. Then, covariance-driven stochastic subspace identification (SSI-Cov) was employed to extract the mast arm fundamental frequencies, damping ratios, and mode shapes. For validation of the vision-based results, SSI-Cov was also applied to acceleration data recorded by two high-sensitivity accelerometers mounted on the structure. In total, the processing was carried out on four different videos and ten acceleration datasets. The vision-based method was able to reliably identify the fundamental frequencies of the structure (Δ f < 0.005 Hz mean difference). The associated damping ratios were consistently overestimated but still close in structural terms (Δ ζ < 0.7 % mean difference).

中文翻译：

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通过交通摄像头监控桅杆臂：一种基于像素的模态分析方法

必须定期检查交通信号灯杆臂结构是否有裂纹、螺栓松动和其他损坏迹象。由于大量库存，大规模实施物理检查和/或专用监控系统可能非常耗时且成本高昂。然而，越来越多地将基于视觉的方法用于结构监控应用引入了利用现有交通摄像机的视频片段用于此目的的可能性。从该镜头中提取动态特性（即自然频率和阻尼）可用于检测可能的恶化迹象。本研究提出了一种基于视觉的监控方法，该方法使用单个交通摄像头来识别支持交通信号灯桅杆的模态特性。这是通过对 2019 年 7 月监测的安装在弗吉尼亚州诺福克的交通信号灯桅杆臂上的交通摄像头获得的像素位移进行操作模态分析来实现的。首先，使用加权质心跟踪逐帧提取子像素位移路面标记。然后，采用协方差驱动的随机子空间识别 (SSI-Cov) 来提取桅杆臂基频、阻尼比和模态振型。为了验证基于视觉的结果，SSI-Cov 还应用于安装在结构上的两个高灵敏度加速度计记录的加速度数据。总共对四个不同的视频和十个加速度数据集进行了处理。基于视觉的方法能够可靠地识别结构的基本频率 (Δ f < 0. 005 Hz 平均差）。相关的阻尼比一直被高估，但在结构方面仍然接近（Δ ζ < 0.7 % 平均差异）。