Cables are essential components of the cable‐stayed bridges as they serve as the main load‐bearing component. Hence, continuous monitoring of such cables becomes necessary as they are vulnerable to the fatigue damage induced by dynamic loads. Sensors are attached to the cables to examine the health of the cables; however, these contact‐based sensors can malfunction in harsh weather condition, which makes impossible to estimate the cable health in such unfavorable condition. Therefore, in this paper, we propose a completely noncontact video‐based stay‐cable tension measurement technique where the video is recorded using a moving handheld camera at a significant distance from the structure itself. Here, the cable tension is determined from vibration‐based measurement, but the vibration of the cable recorded in the video includes the true vibration of the cable along with the camera motion. Hence, we amalgamated a series of image processing techniques to nullify the camera movement. First, we detect the camera movement based on the movement of the bridge deck and pylon, which are fixed objects, using Kanade–Lucas–Tomasi (KLT) feature tracking algorithm. Then we nullify the camera movement by using the affine transformation matrix obtained by random sample consensus (RANSAC) algorithm. Subsequently from the steady video, the cable motions are estimated using the phase‐based motion estimation technique. From the time history of the cable vibration, real‐time frequency variations are estimated using Short‐Time Fourier Transform (STFT). Finally, the real‐time tension is determined from this dominant frequency variation history using the taut‐string theory. This paper shows the significant potential of camera‐based sensing techniques in structural health monitoring as the mean estimated tension and the design cable tension are found to be comparable.

中文翻译：

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使用移动式手持摄像机在杜布罗夫尼克斜拉桥中基于计算机视觉的实时电缆张力估计

电缆是斜拉桥的主要组成部分，因为它们是主要的承重组件。因此，必须对此类电缆进行连续监控，因为它们容易受到动态负载引起的疲劳损坏。传感器安装在电缆上，以检查电缆的运行状况。但是，这些基于接触的传感器可能会在恶劣的天气条件下发生故障，这使得无法在这种不利条件下估算电缆的状况。因此，在本文中，我们提出了一种完全基于非接触式视频的拉索张力测量技术，该技术使用移动的手持式摄像机在距结构本身很远的距离处记录视频。此处，电缆张力由基于振动的测量确定，但视频中记录的电缆的振动包括电缆的真实振动以及摄像机的运动。因此，我们合并了一系列图像处理技术以使相机移动无效。首先，我们使用Kanade–Lucas–Tomasi（KLT）特征跟踪算法，基于固定对象的桥面和塔架的运动来检测摄像机的运动。然后，我们使用通过随机样本共识（RANSAC）算法获得的仿射变换矩阵来使相机运动无效。随后从稳定视频中，使用基于相位的运动估计技术估计电缆的运动。根据电缆振动的时间历史记录，可使用短时傅立叶变换（STFT）估算实时频率变化。最后，实时张力是根据主要的频率变化历史使用拉紧弦理论确定的。本文显示了基于摄像头的传感技术在结构健康监测中的巨大潜力，因为平均估计张力和设计电缆张力被发现具有可比性。