LAVOLUTION: Measurement of Non-target Structural Displacement Calibrated by Structured Light

Displacement is an important measurement for the assessment of structural conditions, but its field measurement is often hindered by difficulties associated with sensor installation and measurement accuracy. To overcome the disadvantages of conventional displacement measurement, computer vision (CV)-based methods have been implemented due to their remote sensing capabilities and accuracy. This paper presents a strategy for non-target structural displacement measurement that makes use of CV to avoid the need to install a target on the structure while calibrating the displacement using structured light. The proposed system called as LAVOLUTION calculates the relative position of the camera with regard to the structure using four equally spaced beams of structured light and obtains a scale factor to convert pixel movement into structural displacement. A jig for the four beams of structured light is designed and a corresponding alignment process is proposed. A method for calculating the scale factor using the designed jig for tunable structured-light is proposed and validated via numerical simulations and lab-scale experiments. To confirm the feasibility of the proposed displacement measurement process, experiments on a shaking table and a full-scale bridge are conducted and the accuracy of the proposed method is compared with that of a reference laser doppler vibrometer.

Non-target Structural Displacement Measurement Using Reference Frame Based Deepflow

Structural displacement is crucial for structural health monitoring, although it is very challenging to measure in field conditions. Most existing displacement measurement methods are costly, labor intensive, and insufficiently accurate for measuring small dynamic displacements. Computer vision (CV) based methods incorporate optical devices with advanced image processing algorithms to accurately, cost-effectively, and remotely measure structural displacement with easy installation. However, non-target based CV methods are still limited by insufficient feature points, incorrect feature point detection, occlusion, and drift induced by tracking error accumulation. This paper presents a reference frame based Deepflow algorithm integrated with masking and signal filtering for non-target based displacement measurements. The proposed method allows the user to select points of interest for images with a low gradient for displacement tracking and directly calculate displacement without drift accumulated by measurement error. The proposed method is experimentally validated on a cantilevered beam under ambient and occluded test conditions. The accuracy of the proposed method is compared with that of a reference laser displacement sensor for validation. The significant advantage of the proposed method is its flexibility in extracting structural displacement in any region on structures that do not have distinct natural features.