Non-contact measurement method of beam vibration with laser stripe tracking based on tilt photography

Wu, Tong; Tang, Liang; Du, Peng; Liu, Nian; Zhou, Zhixiang; Qi, Xiaolei

doi:10.1016/j.measurement.2021.110314

Cited by 21 publications

(6 citation statements)

References 22 publications

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“…Line PQ and Q i− 1 Q i are known as ∆λ and λ i , respectively, and ∠Q i− 1 OQ i � α i . Ten, the following equations can be obtained according to the geometric relationship [34]:…”

Section: Scaling Factor Estimationmentioning

confidence: 99%

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

Tang

Zhang

et al. 2023

Structural Control and Health Monitoring

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Computer vision is becoming one of the most popular remote-sensing techniques and has been used widely in displacement monitoring and damage identification of in-service bridges. Nevertheless, several obstacles, including limited sampling rate, insufficient resolution for remote measurement, and error induced by camera tilting, restrict the application of vision-based approaches in structural health monitoring (SHM). The combination of a traditional SHM system and a modern remote-sensing technique can significantly improve the accuracy and reliability of the monitoring system. To make full use of data collected in the traditional SHM system and computer vision technique and overcome their shortcomings, we presented an improved bridge displacement estimation approach for SHM purposes by fusing camera-based and acceleration measurements. First, we estimated the scaling factor, which transfers pixel displacement to real displacement under tilt photogrammetry, by the acceleration reconstructed and camera-based displacements in the same frequency band without the actual size of the structure or the measurement parameters. Then, we extracted the low-frequency displacement from the vision-based measurement, and we fused the high-frequency displacement that was reconstructed from the acceleration measurement to achieve high-accuracy displacement estimation. The efficiency of this method was validated through dynamic load tests on a suspension model bridge in the laboratory and field tests on a highway and subway cable-stayed bridge.

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Section: Scaling Factor Estimationmentioning

confidence: 99%

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

Tang

Zhang

et al. 2023

Structural Control and Health Monitoring

Self Cite

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“…Continuity is a significant indicator for the testing center extraction algorithm, and directly affects the quality of the extraction center. 2 To verify the ability of the proposed algorithm to extract different light stripes, more than 200 images were used in the experiments. Figure 13 shows four representative line-structured lights and the center extraction results of the proposed algorithm.…”

Section: Multi-curvature Testingmentioning

confidence: 99%

“…Line-structured light is widely used in 3D measurement fields, such as computer vision, bridge tracking, construction monitoring, and unmanned vehicles 1 – 4 Generally, the structure of a light stripe can be divided into arcs, acute angles, and straight lines 5 . Existing line-structured light center extraction algorithms can be roughly divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

High-precision multi-curvature line-structured light center extraction algorithm based on a tooth model

Tang,

Jiang,

Miao

et al. 2023

Opt. Eng.

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Common line-structured light center extraction algorithms have limitations in accuracy and robustness when extracting the center of line-structured light obtained from objects with complex surface structures. In this study, we propose a high-precision center extraction algorithm for multi-curvature line-structured light. This algorithm improves the enhanced parallel thinning algorithm to eliminate redundant points and burrs and fills for missing center points at the end of the light stripe via the improved internal propulsion center extraction algorithm. Finally, sub-pixel centers are recalculated in the direction of the normal vector. A comparative experiment using three classical algorithms was conducted based on a tooth model with a complex curved surface. The experimental results show that the mean absolute error of this algorithm is <0.1, which is only half that of the best-performing dual-threshold grayscale center-of-gravity method in the classical method. Extraction of the center can be performed for multi-curvature line-structured light over 20 curvatures in the range of ð0.0001; 4.1439Þ∕pixel −1 ; moreover, the end of the light stripe and the identification of the truncation can be improved. Therefore, the proposed algorithm is suitable for contour detection of multi-curvature targets and effectively improves the accuracy of high-precision object detection.

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“…Wu et al proposed a method of tracking laser fringes to analyze bridge vibration in view of the strong dependence of traditional vision-based methods on targets and lighting conditions. By tracking the subpixel position of the centerline of laser fringes in the image, the bridge displacement time history curve can be obtained [19]. Lee et al proposed a long-term displacement measurement method based on a self-motion compensation dual-camera, in which the primary camera is used to measure the target displacement and the secondary camera measures the self-motion of the dual-camera system so that the error caused by the motion can be estimated and compensated [20].…”

Section: Introductionmentioning

confidence: 99%

Bridge Deformation Measurement Using Unmanned Aerial Dual Camera and Learning-Based Tracking Method

Jiang

Zhang

Gao

2023

Structural Control and Health Monitoring

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Bridge deformation response data are the basis for calculating the dynamic parameters of the bridge, and it is of great significance to accurately measure the deformation response of the bridge during the load test and service conditions. A bridge deformation measurement method using an unmanned aerial system (UAS) with dual cameras and a deep learning-based object tracking method is proposed to measure the bridge deformation. The contributions are as follows: (1) To address the problem that the movement of the UAS brings error to the deformation measurement results, dual cameras with telephoto and wide-angle lenses are used to simultaneously capture the deformed points and stable points on the bridge, so as to simultaneously measure the deformation of the bridge and the displacement of the UAS, and then the displacement of UAS is eliminated by using the homography relationship between the two cameras. (2) To solve the problem that the traditional digital image correlation-based displacement measurement method is easily disturbed by factors such as light changes and occlusion, a displacement calculation method based on object detection network and target tracking algorithm is proposed to achieve the stable target displacement measurement. Finally, the proposed method was verified in a laboratory test and applied to the deformation measurement of an in-service bridge to verify the practicability of the proposed method.

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Non-contact measurement method of beam vibration with laser stripe tracking based on tilt photography

Cited by 21 publications

References 22 publications

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

High-precision multi-curvature line-structured light center extraction algorithm based on a tooth model

Bridge Deformation Measurement Using Unmanned Aerial Dual Camera and Learning-Based Tracking Method

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