As a key indicator of the structural performance of cable-stayed bridges, tensile forces in stay cables are required to be controlled for maintaining the structural integrity of bridges. In this paper, a non-contact vision-based system for cable tension monitoring is proposed. To measure the dynamic response of cables cost-effectively, a feature-based video image processing technique is developed. The Scale Invariant Feature Transform (SIFT) is adopted for the implementation of the feature-based methodology. Since the detected keypoints associated with the cable play a critical role in extracting the displacement time-history, a study on the feasibility of the feature-based detection algorithm is conducted under a variety of test scenarios within laboratory settings. The performance of the keypoint detector for tracking a vibrating cable is quantified based on a set of evaluation parameters. To extend the versatility of the keypoint detector within complex background scenarios, enhancement techniques are investigated as well. The analysis of the performance indicators demonstrates that the detector is capable of extracting sufficient dynamic information of a vibrating cable from a video image sequence. Subsequently, threshold-dependent image matching approaches are proposed, which optimize the functionality of the vision-based system under complex background conditions. The developed feature-based image processing technique is further integrated seamlessly with cable dynamic analysis for cable tension monitoring. Through experimental studies, the proposed non-contact vision-based system is validated for cable frequency identification as well as tensile force estimation.

作为斜拉桥结构性能的关键指标，需要控制斜拉索的拉力以保持桥的结构完整性。本文提出了一种基于非接触式视觉的电缆张力监测系统。为了经济有效地测量电缆的动态响应，开发了一种基于功能的视频图像处理技术。尺度不变特征变换（SIFT）被用于实现基于特征的方法。由于检测到的与电缆相关的关键点在提取位移时程中起着关键作用，因此在实验室环境下的各种测试情况下，都进行了基于特征的检测算法可行性的研究。基于一组评估参数来量化用于跟踪振动电缆的关键点检测器的性能。为了扩展关键点检测器在复杂背景情况下的多功能性，还研究了增强技术。对性能指标的分析表明，该检测器能够从视频图像序列中提取出振动电缆的足够动态信息。随后，提出了阈值相关的图像匹配方法，该方法在复杂的背景条件下优化了基于视觉的系统的功能。所开发的基于特征的图像处理技术进一步与电缆动态分析无缝集成，以监控电缆张力。通过实验研究