Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method

Song, Qingsong; Wu, J.S.-S.; Wang, Haolin; An, Yi; Tang, Guangwu

doi:10.1016/j.ymssp.2022.108822

Cited by 33 publications

(11 citation statements)

References 24 publications

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“…Taking the LDS data as the benchmark, the displacement data collected from different algorithms are used to perform error analysis [32,33]. The absolute errors and the error distributions of the three algorithms under different conditions are shown in Figure 18.…”

Section: Monitoring Error Analysismentioning

confidence: 99%

Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV

Zhao

et al. 2022

Remote Sensing

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The structural condition of blades is mainly evaluated using manual inspection methods. However, these methods are time-consuming, labor-intensive, and costly, and the detection results significantly depend on the experience of inspectors, often resulting in lower precision. Focusing on the dynamic characteristics (i.e., natural frequencies) of large wind turbine blades, this study proposes a monitoring method based on the target-free DSST (Discriminative Scale Space Tracker) vision algorithm and UAV. First, the displacement drift of UAV during hovering is studied. Accordingly, a displacement compensation method based on high-pass filtering is proposed herein, and the scale factor is adaptive. Then, the machine learning is employed to map the position and scale filters of the DSST algorithm to highlight the features of the target image. Subsequently, a target-free DSST vision algorithm is proposed, in which illumination changes and complex backgrounds are considered. Additionally, the algorithm is verified using traditional computer vision algorithms. Finally, the UAV and the target-free DSST vision algorithm are used to extract the dynamic characteristic of the wind turbine blades under shutdown. Results show that the proposed method can accurately identify the dynamic characteristics of the wind turbine blade. This study can serve as a reference for assessment of the condition of wind turbine blades.

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Section: Monitoring Error Analysismentioning

confidence: 99%

Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV

Zhao

et al. 2022

Remote Sensing

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“…The rapid development of deep learning-based object detection (Girshick et al, 2014;Redmon et al, 2016) and image segmentation algorithms (L. C. Chen et al, 2018;Ronneberger et al, 2015) has led to the widespread utilization of deep learning algorithms in vision-based displacement monitoring. Song et al (2022) successfully achieved displacement monitoring of structures by combining image segmentation algorithms with traditional vision techniques. Jeong and Jo (2022) addressed environmental uncertainties, such as changes in luminous conditions and obstructed vision, through the use of a deep learning model.…”

Section: Introductionmentioning

confidence: 99%

“…Song et al. (2022) successfully achieved displacement monitoring of structures by combining image segmentation algorithms with traditional vision techniques. Jeong and Jo (2022) addressed environmental uncertainties, such as changes in luminous conditions and obstructed vision, through the use of a deep learning model.…”

Section: Introductionmentioning

confidence: 99%

A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision

Wang,

Peng,

et al. 2024

Computer aided Civil Eng

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Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1 mm when the measurement distance is less than 40 m. When the measurement distance is equal to 90 m, the measurement error is less than 5 mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities.

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“…Feng & Feng, 2017; M. Q. Feng et al, 2015;Hoskere et al, 2019;Lydon et al, 2019;Song et al, 2022;Tian et al, 2019;Weng et al, 2021;Xu & Brownjohn, 2018;Yoon et al, 2016;Zhang et al, 2021). CV-based displacement measurement can be divided into two systems: target-and non-target-based.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method

Cited by 33 publications

References 24 publications

Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV

Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV

A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision

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

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