Non-Target Structural Displacement Measurement Using Reference Frame-Based Deepflow

Abstract

Displacement is crucial for structural health monitoring, although it is very challenging to measure under 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.

A Multiplicative Noises and Additive Correlated Noises Cubature Kalman Filter and Its Application in Quadruped Robot

“…It can be seen that the two hypotheses of KF theory are quite ideal, which can hardly be met in the actual system. In the fields such image information processing [5], pattern recognition [6], underwater target tracking [7], aircraft tracking [8], space target tracking [9], and robot state estimation [10], there exists another type of non-ideal circumstances that in the process of signal transmission, there will be delay, distortion, attenuation or channel interference, the random uncertainty of system model parameters will be caused by modeling error, model simplification and random disturbance, the ranging noise of the sensor varies with the increase of the distance and appears as multiplicity noise [11]. These circumstances cannot be indicated by additive noise in classical systems.…”

Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System

“…One of the most representative articles regarding noncontact SHM using vison-based systems is [36], in which the performance of both the target-less and target-based systems was analyzed and validated. In the target-less systems, the displacements of distinct features of the monitored structure, such as the corners or the edges, are detected and tracked by computer-vision techniques [37,38,39].…”

“…Many vision-based structural displacement measurement systems use different characteristics of the imaging system. Most works use monocular camera systems to measure the structural displacements that are parallel to the imaging plane [39,41,43]. These works focus mainly on detecting the in-plane structural translations.…”

“…The camera is displaced, and rotates along with the bridge pier, which will affect the displacement measurements of the bridge pivot pier. In recent years, several camera compensation methods have been developed to compensate the camera movements in SHM and infer the global structural displacements [39,41,43]. However, those methods consider structural displacements to be in-plane translations, i.e., structural displacements parallel to the imaging plane.…”

Displacement Measurement Method Based on the Rotating Paraboloid Array

“…Ultra-precision displacement measurement technology has become a research focus in the field of measurement technology [1][2][3]. Displacement measurement methods have a wide range of application requirements in different fields, such as structural health monitoring [4,5], civil engineering [6][7][8], and manufacturing industries [9,10].…”