Abstract
Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.
Highlights
The advancements of non-contact and vision-based sensors in the field of structural health monitoring (SHM) as well as the development of optics and computer vision algorithms have led to a growing demand, among the civil and construction engineering communities, for long-term continuous and real-time vision-based SHM
Quantitative analysis was conducted on all compressed images the total gray interpolation or wavelet compression experiences reduction ofphotogrammetry pixel counts within thewith distributed
The histogram focuses only on the predominant gray values between 0 and 100 intensity any additional features that are visible after image compression
Summary
The advancements of non-contact and vision-based sensors in the field of structural health monitoring (SHM) as well as the development of optics and computer vision algorithms have led to a growing demand, among the civil and construction engineering communities, for long-term continuous and real-time vision-based SHM. Monitoring using vision-based sensors incorporates an offline camera calibration and a closed-range photogrammetry process while using either artificial markers [1,2,3,4,5,6,7] or relying on the natural features of the structure [8,9,10]. The development of the sensor system for long-term continuous and real time monitoring is still underway.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
