Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges

Lee, Jun Hwa; Lee, Kyoung‐Chan; Cho, Su Jin; Sim, Sung‐Han

doi:10.3390/s17102317

Cited by 73 publications

(50 citation statements)

References 52 publications

(96 reference statements)

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“…According to target markers required or not, the computer vision technologies for displacement measurement can be classified into two categories. The first category using customized target markers [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ] can increase the differentiation between the tracking target and the background to improve measurement accuracy. In the actual measurement, however, the installation of markers is labor-intensive, especially when multi-point displacements are desired.…”

Section: Introductionmentioning

confidence: 99%

An Improved Vision Method for Robust Monitoring of Multi-Point Dynamic Displacements with Smartphones in an Interference Environment

Chen

Zhou

2020

Sensors

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Current research on dynamic displacement measurement based on computer vision mostly requires professional high-speed cameras and an ideal shooting environment to ensure the performance and accuracy of the analysis. However, the high cost of the camera and strict requirements of sharp image contrast and stable environment during the shooting process limit the broad application of the technology. This paper proposes an improved vision method to implement multi-point dynamic displacement measurements with smartphones in an interference environment. A motion-enhanced spatio-temporal context (MSTC) algorithm is developed and applied together with the optical flow (OF) algorithm to realize a simultaneous tracking and dynamic displacement extraction of multiple points on a vibrating structure in the interference environment. Finally, a sine-sweep vibration experiment on a cantilever sphere model is presented to validate the feasibility of the proposed method in a wide-band frequency range. In the test, a smartphone was used to shoot the vibration process of the sine-sweep-excited sphere, and illumination change, fog interference, and camera jitter were artificially simulated to represent the interference environment. The results of the proposed method are compared to conventional displacement sensor data and current vision method results. It is demonstrated that, in an interference environment, (1) the OF method is prone to mismatch the feature points and leads to data deviated or lost; (2) the conventional STC method is sensitive to target selection and can effectively track those targets having a large proportion of pixels in the context with motion tendency similar to the target center; (3) the proposed MSTC method, however, can ease the sensitivity to target selection through in-depth processing of the information in the context and finally enhance the robustness of the target tracking. In addition, the MSTC method takes less than one second to track each target between adjacent frame images, implying a potential for online measurement.

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Section: Introductionmentioning

confidence: 99%

An Improved Vision Method for Robust Monitoring of Multi-Point Dynamic Displacements with Smartphones in an Interference Environment

Chen

Zhou

2020

Sensors

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“…Kang et al [20] extracted the boundary of a burning wood pellet using scanning lines and then calculated its volume based on radius and height, which is important to understand the wood pellet burning mechanism. Lee et al [21] proposed a vision-based method with an adaptive region-of-interest algorithm to locate the marker attached to bridge, and the structural displacements of bridge were measured based on planar homography. Zhang et al [22] measured the steel bar diameter, spacing, and quantity by sub-pixel boundary location in smartphone image and fast image stitch.…”

Section: Introductionmentioning

confidence: 99%

A Monocular Vision-Based Framework for Power Cable Cross-Section Measurement

Zhang

Yin

2019

Energies

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The measurements of the diameter of different layers, the thickness of different layers and the eccentricity of insulation layer in the cross-section of power cables are important items of power cable test, which currently depend on labor-intensive manual operations. To improve efficiency, automatic measurement methods are in urgent need. In this paper, a monocular vision-based framework for automatic measurement of the diameter, thickness, and eccentricity of interest in the cross-section of power cables is proposed. The proposed framework mainly consists of three steps. In the first step, the images of cable cross-section are captured and undistorted with the camera calibration parameters. In the second step, the contours of each layer are detected in the cable cross-section image. In order to detect the complete and accurate contours of each layer, the structural edges in the cross-section image are firstly detected and divided into individual layers, then unconnected edges are connected by arc-based method, and finally contours are refined by the proposed break detection and grouping (BDG) and linear trend-based correction (LTBC) algorithm. In the third step, the monocular vision-based cross-section dimension measurement is accomplished by placing a chessboard coplanar with the power cable cross-section plane. The homography matrix mapping pixel coordinates to chessboard world coordinates is estimated, and the diameter, thickness and eccentricity of specific layers are calculated by homography matrix-based measurement method. Simulated data and actual cable data are both used to validate the proposed method. The experimental results show that diameter, minimum thickness, mean thickness and insulation eccentricity of simulated image without additive noise are measured with root mean squared error (RMSE) of 0.424, 0.103 and 0.063 mm, and 0.002, respectively, those of simulated image with additive Gaussian noise and salt and pepper noise are measured with RMSE of 0.502, 0.243 and 0.058 mm and 0.001. Diameter, minimum thickness and mean thickness of actual cable images are measured with average RMSE of 0.768, 0.308 and 0.327 mm. The measurement error of insulation eccentricity of actual cable image is comparatively large, and the measurement accuracy should be improved.

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“…Also, the measure is carried out during short periods of time on the order of seconds or minutes. The results have been compared to accelerometers and/or LVDT [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. The measurement of the short-term dynamic response of a bridge is technologically less demanding than the one required to capture long-term changes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Laser and Video-Based Displacement Transducer to Monitor Bridge Deflections

Cabrera

González

Mínguez

et al. 2018

Sensors

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The measurement of static vertical deflections on bridges continues to be a first-level technological challenge. These data are of great interest, especially for the case of long-term bridge monitoring; in fact, they are perhaps more valuable than any other measurable parameter. This is because material degradation processes and changes of the mechanical properties of the structure due to aging (for example creep and shrinkage in concrete bridges) have a direct impact on the exhibited static vertical deflections. This paper introduces and evaluates an approach to monitor displacements and rotations of structures using a novel laser and video-based displacement transducer (LVBDT). The proposed system combines the use of laser beams, LED lights, and a digital video camera, and was especially designed to capture static and slow-varying displacements. Contrary to other video-based approaches, the camera is located on the bridge, hence allowing to capture displacements at one location. Subsequently, the sensing approach and the procedure to estimate displacements and the rotations are described. Additionally, laboratory and in-service field testing carried out to validate the system are presented and discussed. The results demonstrate that the proposed sensing approach is robust, accurate, and reliable, and also inexpensive, which are essential for field implementation.

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Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges

Cited by 73 publications

References 52 publications

An Improved Vision Method for Robust Monitoring of Multi-Point Dynamic Displacements with Smartphones in an Interference Environment

An Improved Vision Method for Robust Monitoring of Multi-Point Dynamic Displacements with Smartphones in an Interference Environment

A Monocular Vision-Based Framework for Power Cable Cross-Section Measurement

A Novel Laser and Video-Based Displacement Transducer to Monitor Bridge Deflections

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