Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

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.

A compressive sensing method for processing and improving vision‐based target‐tracking signals for structural health monitoring

“…Thus, image compression is one way to handle and process large amount of data so that TT systems can become more practical and eventually advance to near real‐time or continuous monitoring systems. The authors have recently completed a study that evaluated the effect of different image compression techniques on the accuracy of vision‐based monitoring (Ngeljaratan & Moustafa, 2020b). Accordingly, image compression is not considered as part of this study, and the focus is on time‐domain signal compression as another alternative for faster data handling and processing.…”

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures

“…Depending on the field of view, the UAV should fly in front of the object, like in the example in Figure 1 , or above the object, as shown later in the validation test and pipeline shake-table tests. Similar to vision-based vibration SHM using steady cameras, as previously studied by the authors [ 35 , 36 , 37 , 38 , 39 ], the UAV camera should also be kept stable, and the UAV body should not drift while monitoring the tests; otherwise, they will affect the data accuracy. Therefore, this study proposes conversion and correction steps between two key steps for UAV-based seismic SHM, i.e., the computer vision-aided procedure and seismic safety measures, as shown in Figure 1 , with the details given in the next subsections.…”

Characteristics of Deformation and Stability of Ultra-Deep Pit in Plateau Alluvial–Lacustrine Gravel Strata

“…Figure 6 shows the configuration of monitoring items at the top of the pit walls. Close-range photogrammetry technology uses images obtained from close-distancetarget photography to determine the spatial positions of manual marking points [31][32][33][34]. A FUJIFILM-XT20 non-metric camera was used in the lab-scale geophysical model test to this end.…”