A Vision-Based Sensor for Noncontact Structural Displacement Measurement

By Dongming Feng, Maria Qing Feng, Ekin Ozer, Yoshio Fukuda. Italian National Conference on Sensors, 2015

440 citations recorded by the source corpus.

Conventional displacement sensors have limitations in practical applications. This paper develops a vision sensor system for remote measurement of structural displacements. An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images. By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy. The performance of the vision sensor is first evaluated through a laboratory shaking table test of a frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Satisfactory agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments. Significant advantages of the noncontact vision sensor include its low cost, ease of operation, and flexibility to extract structural displacement at any point from a single measurement.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Instrumentation

View Paper: https://www.semanticscholar.org/paper/9a9f0117f908572e428031dd29095d08c8598b01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

In Computer Vision and Pattern Recognition

In Instrumentation

Citations in the corpus

A review of computer vision–based structural health monitoring at local and global levels (2020, methodology): “Feng et al.20 developed a subpixel-level template matching for structural displacement measurement by implementing an upsampled cross-correlation (UCC) method in frequency domain.”
Feasibility of using digital image correlation for unmanned aerial vehicle structural health monitoring of bridges (2018, background): “capability of DIC to quantitatively assess bridge health and measure structural full-field displacement and strain over a number of different structures, including bridges [38-45].”
A literature review of next‐generation smart sensing technology in structural health monitoring (2019, methodology): “Feng et al.35 developed a new vision‐based sensor to measure dynamic displacements from the video images without using a target‐marker panel.”