Non-contact structural displacement measurement using Unmanned Aerial Vehicles and video-based systems

Ribeiro, Diogo; Santos, Ricardo; Cabral, Rafael; Saramago, G.; Montenegro, Pedro Aires; Carvalho, Hermes; Correia, José A.F.O.; Calçada, Rui

doi:10.1016/j.ymssp.2021.107869

Cited by 46 publications

(13 citation statements)

References 34 publications

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“…It can be seen that, when the camera translates a distance d y along the vertical optical axis, the error is d y ; However, when the camera rotates slightly through an angle d θ , the error caused is L ·tan d θ , which is unacceptable. Therefore, in order to improve the accuracy of absolute displacement estimation, several camera motion subtraction techniques have been developed: (1) digital high-pass filtering (DHF) [ 15 , 129 ]; (2) background modification (BM) [ 51 , 106 , 130 ]; (3) inertial measuring unit (IMU) [ 131 ]; and (4) ego-motion compensation (EC) [ 132 ].…”

Section: Computer Vision-based Deformation Monitoring In Field Enviro...mentioning

confidence: 99%

“…An IMU consists of a DC gyroscope and accelerometers that can respond to very low frequency (almost 0 Hz) vibrations. Ribeiro et al [ 131 ] estimated the displacement and rotation of a UAV by numerical integration, and measured a static concrete structure with a peak value error of 1.47 mm (15.5% relative error) and an RMS error of 9.3%.…”

Section: Computer Vision-based Deformation Monitoring In Field Enviro...mentioning

confidence: 99%

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A Review of Computer Vision-Based Structural Deformation Monitoring in Field Environments

Easa

Chen

Jin

et al. 2022

Sensors

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Computer vision-based structural deformation monitoring techniques were studied in a large number of applications in the field of structural health monitoring (SHM). Numerous laboratory tests and short-term field applications contributed to the formation of the basic framework of computer vision deformation monitoring systems towards developing long-term stable monitoring in field environments. The major contribution of this paper was to analyze the influence mechanism of the measuring accuracy of computer vision deformation monitoring systems from two perspectives, the physical impact, and target tracking algorithm impact, and provide the existing solutions. Physical impact included the hardware impact and the environmental impact, while the target tracking algorithm impact included image preprocessing, measurement efficiency and accuracy. The applicability and limitations of computer vision monitoring algorithms were summarized.

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Section: Computer Vision-based Deformation Monitoring In Field Enviro...mentioning

confidence: 99%

Section: Computer Vision-based Deformation Monitoring In Field Enviro...mentioning

confidence: 99%

A Review of Computer Vision-Based Structural Deformation Monitoring in Field Environments

Easa

Chen

Jin

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…CV-based displacement measurement can be divided into two systems: target-and non-target-based. Target-based methods use artificial targets and tracking reference points, such as transition points between different colors (G. Lee et al, 2022;Luo et al, 2018;Ribeiro et al, 2021). For example, targets consisting of high-contrast black and white squares (e.g., a checkerboard pattern and ArUco marker; Garrido-Jurado et al, 2016;Romero-Ramirez et al, 2018) allow for accurate displacement measurement using various tracking algorithms.…”

Section: Introductionmentioning

confidence: 99%

Lavolution: Computer Vision Based Non-Target Structural Displacement Using Structured Light

et al. 2022

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“…Yoon et al [ 23 ] used triangulation to calculate the camera parameters for each UAV video frame, and the estimated camera projection matrix can be used to recover the world coordinates of the structure and obtain the true displacement curve. Ribeiro et al [ 24 ] used an embedded inertial measurement unit (IMU) to obtain information on the three-axis attitude angle of the UAV, through which the motion of the UAV can be calculated, so that the true displacement of the structure can be estimated. Hoskere et al [ 25 ] proposed a high-pass filtering method to remove the low-frequency noise caused by the hovering vibration of the UAV, but this method can only be used for the case that the structural natural frequency is much higher than the noise frequency caused by the UAV motion.…”

Section: Introductionmentioning

confidence: 99%

Comparisons of Differential Filtering and Homography Transformation in Modal Parameter Identification from UAV Measurement

Zhang

Chen

et al. 2021

Sensors

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This paper proposes a differential filtering method for the identification of modal parameters of bridges from unmanned aerial vehicle (UAV) measurement. The determination of the modal parameters of bridges is a key issue in bridge damage detection. Accelerometers and fixed cameras have disadvantages of deployment difficulty. Hence, the actual displacement of a bridge may be obtained by using the digital image correlation (DIC) technology from the images collected by a UAV. As drone movement introduces false displacement into the collected images, the homography transformation is commonly used to achieve geometric correction of the images and obtain the true displacement of the bridge. The homography transformation is not always applicable as it is based on at least four static reference points on the plane of target points. The proposed differential filtering method does not request any reference points and will greatly accelerate the identification of the modal parameters. The displacement of the points of interest is tracked by the DIC technology, and the obtained time history curves are processed by differential filtering. The filtered signals are input into the modal analysis system, and the basic modal parameters of the bridge model are obtained by the operational modal analysis (OMA) method. In this paper, the power spectral density (PSD) is used to identify the natural frequencies; the mode shapes are determined by the ratio of the PSD transmissibility (PSDT). The identification results of three types of signals are compared: UAV measurement with differential filtering, UAV measurement with homography transformation, and accelerometer-based measurement. It is found that the natural frequencies recognized by these three methods are almost the same. This paper demonstrates the feasibility of UAV-differential filtering method in obtaining the bridge modal parameters; the problems and challenges in UAV measurement are also discussed.

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Non-contact structural displacement measurement using Unmanned Aerial Vehicles and video-based systems

Cited by 46 publications

References 34 publications

A Review of Computer Vision-Based Structural Deformation Monitoring in Field Environments

A Review of Computer Vision-Based Structural Deformation Monitoring in Field Environments

Lavolution: Computer Vision Based Non-Target Structural Displacement Using Structured Light

Comparisons of Differential Filtering and Homography Transformation in Modal Parameter Identification from UAV Measurement

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