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 …

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to
recent technological advancements in sensors, as well as high-speed internet and cloud …

Computer vision techniques, in conjunction with acquisition through remote cameras and
unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure …

Computer vision techniques for extracting dynamic structural displacements from videos are
gaining increasing acceptance for the purposes of system identification and structural health …

Manual visual inspection is the most common means of assessing the condition of civil
infrastructure in the United States, but can be exceedingly laborious, time-consuming, and …

This research proposes an approach for vision-based autonomous navigation planning of
unmanned aerial vehicles for the collection of images suitable for the rapid post-earthquake …

Manual visual inspection of civil infrastructure is high-risk, subjective, and time-consuming.
The success of deep learning and the proliferation of low-cost consumer robots has spurred …

Civil infrastructure condition assessment using visual recognition methods has shown
significant potential for automating various aspects of the problem, including identification …

To assess the current conditions of bridges in practice, a manual visual inspection is
commonly used for maintenance purposes. Because manual visual inspection is time …

Infrastructure scene understanding from image data aids diverse applications in construction
and maintenance. Recently, deep learning models have been employed to extract …