Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review

A literature review of next-generation smart sensing technology in structural health monitoring

Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to t...

A review of computer vision–based structural health monitoring at local and global levels:

Vision-based systems are promising tools for displacement measurement in civil structures, possessing advantages over traditional displacement sensors in instrumentation cost, installation efforts and measurement capacity in terms of frequency range and spatial resolution. Approximately one hundred papers to date have appeared on this subject, investigating topics like system development and improvement, the viability on field applications and the potential for structural condition assessment. The main contribution of this paper is to present a literature review of vision-based displacement measurement, from the perspectives of methodologies and applications. Video-processing procedures in this paper are summarised as a three-component framework: camera calibration, target tracking and structural displacement calculation. Methods for each component are presented in principle, with discussions about the relative advantages and limitations. Applications in the two most active fields, bridge deformation and cable vibration measurement, are examined followed by a summary of field challenges observed in monitoring tests. Important gaps requiring further investigation are presented, e.g. robust tracking methods, non-contact sensing and measurement accuracy evaluation in field conditions.

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Review of machine-vision based methodologies for displacement measurement in civil structures

In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM). This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large-range and multiple-target monitoring. A lot of machine vision-based structural dynamic measurement and structural state inspection methods have been proposed. Real-world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.

/pdf/a-review-of-machine-vision-based-structural-health-yfo716pcph.pdf

A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications

Current vision-based displacement measurement methods have limitations such as being in need of manual targets and parameter adjustment, and significant user involvement to reach the desired result...

Structural displacement monitoring using deep learning-based full field optical flow methods

Vision-based structural displacement measurement: System performance evaluation and influence factor analysis

In this study, a vision-based multi-point structural dynamic monitoring framework is proposed. This framework aims to solve issues in current vision-based structural health monitoring. Limitations ...

https://journals.sagepub.com/doi/pdf/10.1177/1475921718806895

Chuan-Zhi Dong

Papers

A review of computer vision–based structural health monitoring at local and global levels:

A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications

Structural displacement monitoring using deep learning-based full field optical flow methods

Vision-based structural displacement measurement: System performance evaluation and influence factor analysis

Marker-free monitoring of the grandstand structures and modal identification using computer vision methods: