A Target-Less Vision-Based Displacement Sensor Based on Image Convex Hull Optimization for Measuring the Dynamic Response of Building Structures

Abstract

Existing vision-based displacement sensors (VDSs) extract displacement data through changes in the movement of a target that is identified within the image using natural or artificial structure markers. A target-less vision-based displacement sensor (hereafter called “TVDS”) is proposed. It can extract displacement data without targets, which then serve as feature points in the image of the structure. The TVDS can extract and track the feature points without the target in the image through image convex hull optimization, which is done to adjust the threshold values and to optimize them so that they can have the same convex hull in every image frame and so that the center of the convex hull is the feature point. In addition, the pixel coordinates of the feature point can be converted to physical coordinates through a scaling factor map calculated based on the distance, angle, and focal length between the camera and target. The accuracy of the proposed scaling factor map was verified through an experiment in which the diameter of a circular marker was estimated. A white-noise excitation test was conducted, and the reliability of the displacement data obtained from the TVDS was analyzed by comparing the displacement data of the structure measured with a laser displacement sensor (LDS). The dynamic characteristics of the structure, such as the mode shape and natural frequency, were extracted using the obtained displacement data, and were compared with the numerical analysis results. TVDS yielded highly reliable displacement data and highly accurate dynamic characteristics, such as the natural frequency and mode shape of the structure. As the proposed TVDS can easily extract the displacement data even without artificial or natural markers, it has the advantage of extracting displacement data from any portion of the structure in the image.

Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

“…Then, the scaling factor (SF) of the physical coordinates to the pixel coordinates was obtained through the calibration process. This scaling factor could also be calculated by Equation : where, D is the physical length of the target, and d is the pixel length of the target.…”

“…Then, the scaling factor (SF) of the physical coordinates to the pixel coordinates was obtained through the calibration process. This scaling factor could also be calculated by Equation (1) 24 :…”

Review of machine-vision based methodologies for displacement measurement in civil structures

“…Camera positioning is less critical [8] when known structural dimensions are used for calibration. However, when applying the scale factor derived from the camera-to-target distance, the tilt angle of the camera optical axis is suggested to be less than 10°t hrough laboratory validation tests in short distance (B 3.7 m) [58]. Care must be taken that different scale factors are applied to different axes to measure the 2D displacement.…”

Design and Testing of a Structural Monitoring System in an Almería-Type Tensioned Structure Greenhouse

“…Non-contact radar sensors have been successfully and economically used in bridge monitoring, such as image by interferometric survey of structures (IBIS-S), especially in structural safety analysis. Allowing to relate the variation in the vibration frequencies thereof with the deterioration of materials and the appearance of damage to the girders [37], or to determine the displacement of the deck for both seaport and continental bridges [38], or to estimate the service life of bridges subject to deterioration or events such as truck impacts or earthquakes [39].The accuracy of measurements is affected by the camera-to-target distance and the optical axis inclination, although these errors can be acceptable for small angles [29,40] and the accuracy starts to decrease at a distance of one metre or more [31]. However, camera placement is not critical [32] when known structural dimensions are used for calibration, but the camera axis needs to be perpendicular to the measured target.…”

“…The accuracy of measurements is affected by the camera-to-target distance and the optical axis inclination, although these errors can be acceptable for small angles [29,40] and the accuracy starts to decrease at a distance of one metre or more [31]. However, camera placement is not critical [32] when known structural dimensions are used for calibration, but the camera axis needs to be perpendicular to the measured target.…”