Ultra-precision global bridge rotation angle measurement method

Abstract

The invention discloses an ultra-precision global bridge rotation angle measurement method, and relates to the technical field of angle measurement. The method comprises the following steps: continuously shooting a bridge motion image sequence, and recording timestamps; key points on the bridge deck slab are selected through a feature detection algorithm, a spatio-temporal data matrix is constructed, and data standardization processing is carried out. Linear principal component analysis is adopted to extract global features of overall movement of the bridge deck; and extracting local nonlinear motion characteristics in the residual error through nonlinear principal component analysis. And calculating the displacement of the feature points based on the reconstruction matrix in combination with an lk optical flow method. A least square method is adopted to fit displacement data, and an angle is calculated through an arc tangent function on the basis. The method has the characteristics of high precision and global coverage, is suitable for the fields of bridge health monitoring, safety evaluation and the like, and can effectively capture the overall and local motion characteristics of the bridge.

Description

Technical Field

[0001] The invention belongs to the technical field of angle measurement, and in particular relates to an ultra-precision global bridge rotation angle measurement method. Background Art

[0002] Bridge tilt monitoring is a vital part of bridge structure health monitoring. Tilt changes directly reflect the stress and deformation state of the bridge under external loads. Bridges may tilt under the influence of earthquakes, wind loads, traffic loads or uneven settlements, and this tilt is usually an early signal of abnormal or potential failure of the bridge structure. Therefore, bridge tilt monitoring is not only an important means to ensure the safety of bridge structures, but also a key technology to improve the reliability and durability of transportation infrastructure. The full range of angle measurements is very important. In addition, bridges may produce nonlinear deformations under the influence of vehicle loads, wind loads, and seismic loads. Existing technologies are insufficient in processing nonlinear deformations.

[0003] Existing solutions include contact and non-contact. The contact measurement method includes a tilt sensor, which can achieve sub-degree or even higher precision tilt measurement, and is suitable for detecting small angle changes. The installation is complex, discrete point measurement, easily affected by temperature changes, vibration or humidity, the tilt sensor responds slowly to rapid dynamic changes (such as vibration or shock), and is not suitable for high-frequency dynamic measurement. Non-contact measurement methods include laser Doppler vibrometers, which have high accuracy but are limited to laboratory applications; optical flow analysis methods are difficult to meet actual needs due to their low sensitivity.

[0004] Vision-based image measurement methods are widely used in displacement measurement due to their low cost and easy implementation. Existing methods mainly include digital image correlation (DIC). The DIC method generally requires the arrangement of targets with random speckle patterns on the surface of the object to be measured, and is widely used in the field of structural deformation measurement. However, due to its dependence on targets, the number of spatial measurement points of the DIC method is severely limited by the number of targets; since the algorithm involves a large number of correlation calculations, the computational efficiency of the DIC method is relatively low, and it is difficult to meet the requirements of high sampling rate online measurement. DIC has low accuracy (about 0.1 pixel) and requires targets with random speckle patterns, which is difficult to achieve. Targets are easily damaged in natural environments, and full-scale target design is unrealistic. Usually, DIC can only achieve point measurement (random pattern targets are set locally on the bridge deck).

[0005] Different from the DIC method, the optical flow method utilizes the grayscale spatial changes of the natural texture on the surface of an object or the edge of an object after imaging. It can calculate the displacement of all effective pixels without a target and has high calculation efficiency.

[0006] The present invention obtains a bridge motion image sequence through a high frame rate camera, combines linear principal component analysis and nonlinear principal component analysis methods to obtain the displacement of different pixel positions of the bridge deck, and then calculates the angle, thereby optimizing the measurement accuracy and robustness of the rotation angle in real time. Summary of the invention

[0007] In order to overcome the shortcomings and deficiencies of the above-mentioned prior art, the present invention adopts the following technical solutions: An ultra-precision global bridge rotation angle measurement method, the process is as follows: Step 1: Data collection: Use a high frame rate camera with a frame rate more than 5 times higher than the bridge movement frequency to continuously shoot a sequence of bridge motion images and record the corresponding timestamps; Step 2: Data preprocessing: calibrate and correct the collected images, select feature points, calculate the optical flow of feature points, track feature points to construct a spatiotemporal data matrix, and normalize the data; Step 3: Analysis process: Perform linear principal component analysis on the spatiotemporal data matrix to extract the global characteristics of the overall movement of the bridge deck, and then perform nonlinear principal component analysis on the residuals to capture the local nonlinear movement of the bridge deck; Step 4: Displacement calculation: Use the optical flow method to calculate the displacement of all feature points in the reconstructed matrix, remove high-frequency noise and low-frequency background motion interference through a double-layer filtering algorithm, and fit the displacement data around each feature point to calculate the local angle.

[0008] Preferably, in the data collection step, the camera's resolution must be sufficient to clearly capture pixel details of the bridge deck, and the installation position must ensure that the camera can observe the bridge deck globally to avoid line of sight obstruction.

[0009] Preferably, in the data preprocessing step, a checkerboard calibration plate is used to calibrate the camera, the intrinsic parameter matrix and distortion coefficient of the camera are calculated, and a correction algorithm is used to correct the image to remove lens distortion.

[0010] Preferably, in the analysis process step, linear principal component analysis is used to extract the first r main components of the feature point displacement matrix to capture the global characteristics of the overall movement of the bridge deck. Specifically, a feature point displacement matrix is ​​constructed, in which rows represent time frames and columns represent the displacements of feature points. PCA is used to extract the first r main components: ; in, is the principal component matrix; It is the principal component projection; then the principal components are analyzed to extract the global characteristics of the overall movement of the bridge deck and capture the global linear mode; Nonlinear principal component analysis is used to extract nonlinear features from the residuals of linear principal component analysis to capture the local nonlinear motion of the bridge deck. Specifically, the residuals of linear principal component analysis are first calculated: ; Use nonlinear methods to extract nonlinear features from residuals: ; in, Reconstruction function representing nonlinear method Then perform matrix reconstruction: the final reconstructed matrix , capturing the local nonlinear motion of the bridge deck.

[0011] Preferably, in the displacement calculation step, the Lucas-Kanade optical flow method is used to calculate the displacement of the feature points, and a double-layer filtering algorithm based on the time domain and the frequency domain is used to remove high-frequency noise and low-frequency background motion interference.

[0012] Preferably, the double-layer filtering algorithm includes using fast Fourier transform to convert the displacement data in the time domain to the frequency domain, designing high-pass filters and low-pass filters to remove unnecessary frequency components, and then using inverse fast Fourier transform to convert the filtered frequency domain signal back to the time domain.

[0013] Preferably, in the displacement calculation step, 10 pixels are taken on the left and right of each feature point, and the displacement data are fitted using the least squares method. The local angle is calculated based on the fitting result. The calculation method is as follows: For each feature point, determine the set of 10 pixels on its left and right; The displacement data were fitted using the least squares method; For a given n data points , where n=21, 10 pixels on each side plus the center point, the slope a and intercept b of the line are determined by solving the following set of equations: ; ; Solve the equation to get the values ​​of slope a and intercept b, where slope a is the tangent of the local angle.

[0014] Preferably, the method for calculating the local angle includes determining the slope of the straight line and calculating the local angle by an inverse tangent function: according to the slope a, by an inverse tangent function Calculate the local angle .

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention uses the LK optical flow method to accurately track the displacement of feature points, and combines linear principal component analysis and nonlinear principal component analysis to extract the global characteristics and local nonlinear characteristics of the overall movement of the bridge deck. This method can not only capture the overall movement pattern of the bridge deck, but also reflect the complex deformation of the edge area, thereby providing high-precision data support for the comprehensive assessment of the health status of the bridge.

[0016] 2. The present invention adopts a double-layer filtering algorithm based on time domain and frequency domain to effectively remove high-frequency noise and low-frequency background motion interference, further improving the reliability of the measurement data. This combination of multiple means enables the method to operate stably in various harsh environments and ensures the accuracy of the measurement results.

[0017] 3. The present invention extracts the global features of the overall movement of the bridge deck through PCA to capture the main movement mode; then uses a nonlinear method to analyze the residuals and extract local nonlinear features, such as complex deformation of the edge area. This method can quickly extract key information while retaining detailed features, greatly reducing the computational complexity and improving data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flow chart of an ultra-precision global bridge rotation angle measurement method of the present invention is shown; Figure 2 A flow chart of displacement calculation of the present invention is shown. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0022] Example 1, see Figure 1 As shown, an ultra-precision global bridge rotation angle measurement method of this embodiment has the following process: Step 1: Data collection.

[0023] High frame rate camera settings: The camera frame rate is more than 5 times higher than the bridge movement frequency (for example, the common vibration frequency is 0.5-5Hz, and the frame rate is set to 30Hz or higher). The resolution must be sufficient to clearly capture the pixel details of the bridge deck. The installation position must ensure that the camera can observe the bridge deck globally (such as installation at the bridge head or drone photography), and avoid line of sight obstruction during shooting.

[0024] Data acquisition operation: Continuously shoot a sequence of bridge motion images and record the corresponding timestamps; at the same time, pay attention to avoid strong light, shadows or rainy and foggy weather affecting the image quality. If necessary, infrared or thermal imaging cameras can be used to improve anti-interference capabilities.

[0025] Step 2: Data preprocessing.

[0026] Prepare the calibration plate: A checkerboard calibration plate is usually used, which has a regular black and white checkered pattern. The size of the calibration plate and the size of the squares should be selected according to the actual situation. Generally speaking, the size of the squares is between a few millimeters and tens of millimeters.

[0027] Take calibration images: Place the calibration plate at different positions and angles, and use the camera to take multiple images containing the calibration plate. When taking pictures, make sure that the calibration plate is completely within the image range and the image is clear and not blurred. It is generally recommended to take between 10 and 20 images.

[0028] Detect corner points: Use image processing algorithms (such as the findChessboardCorners function in OpenCV) to detect the corner points of the chessboard in each calibration image. Corner points are the vertices of the chessboard squares, and the detected corner point coordinates will be used for subsequent calibration calculations.

[0029] Calibration calculation: Use the detected corner coordinates and the known calibration grid size to calculate the camera's intrinsic parameter matrix (including focal length, principal point coordinates, etc.) and distortion coefficients (including radial distortion coefficients and tangential distortion coefficients) through a calibration algorithm (such as Zhang Zhengyou calibration method). In Python, you can use OpenCV's calibrateCamera function for calibration calculations.

[0030] Image correction: Based on the calculated intrinsic parameter matrix and distortion coefficient, a correction algorithm (such as the undistort function in OpenCV) is used to correct the captured bridge motion image to remove lens distortion.

[0031] Select feature points: Use feature detection algorithms (such as Shi-Tomasi corner detection algorithm) to detect key points or feature areas on the bridge deck in the first frame image. These feature points should have obvious brightness changes and texture information so that they can be accurately tracked in subsequent frames.

[0032] Calculate optical flow: Use the LK optical flow method (Lucas-Kanade optical flow method) to calculate the optical flow of feature points between two adjacent frames. The LK optical flow method assumes that the movement of all pixels in a small neighborhood is consistent, and calculates the displacement of feature points by solving the optical flow constraint equation.

[0033] Tracking feature points: In each frame, the position of the feature points is updated using the calculated optical flow, and the displacement time series of the feature points is recorded. If a feature point is lost in a frame, the feature point can be re-detected using a feature detection algorithm or estimated using an interpolation method.

[0034] Construct a spatiotemporal data matrix: Combine the displacement time series of all feature points into a matrix, where the rows represent the time frames and the columns represent the displacements of the feature points, thus obtaining a spatiotemporal data matrix.

[0035] Data normalization: Standardize the feature point displacement data to eliminate the dimension effect. The formula is: ; in, is the mean; is the standard deviation.

[0036] Step 3: Analysis process.

[0037] Linear principal component analysis (PCA): Construct a feature point displacement matrix (rows represent time frames, columns represent feature point displacements), and use PCA to extract the first few main components: ; in, is the principal component matrix; is the principal component projection.

[0038] The principal components are then analyzed to extract the global features of the overall motion of the bridge deck, thereby capturing the global linear patterns, such as the main direction of the overall motion of the bridge deck.

[0039] Nonlinear principal component analysis (Kernel PCA or Autoencoder): First calculate the residuals of the linear principal component analysis: ; Then use nonlinear methods (such as Kernel PCA) to extract nonlinear features from the residuals: ; in, Reconstruction function representing nonlinear method Then the matrix is ​​reconstructed: The final reconstructed matrix: ; For Autoencoder or Kernel PCA, It is an approximate representation of nonlinear features in the original space.

[0040] This allows capturing local nonlinear motion of the bridge deck, such as complex deformations in edge areas.

[0041] Step 4: Displacement calculation.

[0042] See also Figure 2 As shown, the displacement calculation process is as follows: S41. Calculate displacement using optical flow method: Use the LK optical flow method to calculate the displacement of all feature points in the reconstructed matrix.

[0043] Specifically, based on the reconstruction matrix obtained in the previous step, the feature points for which displacement calculation is required are determined. These feature points are usually key points or feature areas on the bridge deck that have been marked in the feature extraction step.

[0044] A small neighborhood window (e.g. 3×3, 5×5, etc.) is defined for each feature point, within which the motion of pixels is assumed to be constant.

[0045] Based on the assumption of constant image brightness (i.e. the brightness of a pixel remains unchanged in two adjacent frames), an optical flow constraint equation is established. This equation describes the relationship between the speed of the pixel in the x and y directions and the gradient of the image in these two directions and the brightness change over time.

[0046] S42, filtering and denoising: After the optical flow method is calculated, a double-layer filtering algorithm based on the time domain and frequency domain is used to remove high-frequency noise and low-frequency background motion interference.

[0047] Select the filtering algorithm, determine the appropriate filtering window size according to the characteristics of the noise and the changing law of the signal, and perform time domain filtering on the feature point displacement data calculated by the optical flow method according to the selected filtering algorithm and window size.

[0048] For example: Use Fast Fourier Transform (FFT) to convert the displacement data in the time domain to the frequency domain to obtain the spectrum of the signal. Design high-pass filters and low-pass filters based on the frequency range of the bridge movement and the frequency characteristics of the noise. The high-pass filter is used to remove low-frequency background motion interference, and the low-pass filter is used to remove high-frequency noise. Apply the designed filter to the frequency domain signal to remove unnecessary frequency components. Use Inverse Fast Fourier Transform (IFFT) to convert the filtered frequency domain signal back to the time domain to obtain the denoised displacement data.

[0049] S43, local angle calculation: take 10 pixels on the left and right of each feature point, and calculate the overall angle of these points by fitting the local displacement data.

[0050] The calculation method is as follows: For each feature point, determine the set of 10 pixels on its left and right; The displacement data were fitted using the least squares method; For a given n data points (Here, n=21, 10 pixels on each side plus the center point), determine the slope a and intercept b of the line by solving the following system of equations: ; ; Solve the equation to get the values ​​of slope a and intercept b, where slope a is the tangent of the local angle.

[0051] Angle calculation: According to the slope a, through the inverse tangent function Calculate the local angle .

[0052] The beneficial effects of this embodiment include: the beneficial effects of the ultra-precision global bridge rotation angle measurement method include: high-precision acquisition and analysis, effective removal of lens distortion, accurate tracking of feature points, capturing global and local motion features through linear and nonlinear PCA, combining optical flow method and filtering denoising to improve displacement calculation accuracy, and realize accurate calculation of local angles.

[0053] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0054] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0055] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0056] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0057] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0058] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An ultra-precision global bridge rotation angle measurement method, characterized in that: The method flow is as follows: Step 1: Data collection: Use a high frame rate camera with a frame rate higher than a preset multiple of the bridge movement frequency to continuously shoot a sequence of bridge movement images and record the corresponding timestamps; Step 2: Data preprocessing: calibrate and correct the collected images, select feature points, obtain the spatiotemporal data matrix of the study area, and normalize the data; Step 3: Analysis process: Perform linear principal component analysis on the spatiotemporal data matrix to extract the global characteristics of the overall movement of the bridge deck, and then perform nonlinear principal component analysis on the residuals to capture the local nonlinear movement of the bridge deck; Step 4: Displacement calculation: Use the optical flow method to calculate the displacement of all feature points in the reconstructed matrix, remove high-frequency noise and low-frequency background motion interference through a double-layer filtering algorithm, and locally fit and optimize the displacement data around each feature point to calculate the local angle.

2. The ultra-precision global bridge rotation angle measurement method according to claim 1 is characterized in that: In the data collection step, the resolution of the camera must be sufficient to clearly capture the pixel details of the bridge deck, and the installation position must ensure that the camera can observe the bridge deck globally to avoid obstruction of the line of sight.

3. The ultra-precision global bridge rotation angle measurement method according to claim 1 is characterized in that: In the data preprocessing step, a checkerboard calibration plate is used to calibrate the camera, the intrinsic parameter matrix and distortion coefficient of the camera are calculated, and a correction algorithm is used to correct the image to remove lens distortion.

4. The ultra-precision global bridge rotation angle measurement method according to claim 1 is characterized in that: In the analysis process, linear principal component analysis is used to extract the first r main components of the feature point displacement matrix to capture the global characteristics of the overall movement of the bridge deck. Specifically, a feature point displacement matrix is ​​constructed, where rows represent time frames and columns represent the displacements of feature points. PCA is used to extract the first r main components: ； in, is the principal component matrix; It is the principal component projection; then the principal components are analyzed to extract the global characteristics of the overall movement of the bridge deck and capture the global linear mode; Nonlinear principal component analysis is used to extract nonlinear features from the residuals of linear principal component analysis to capture the local nonlinear motion of the bridge deck. Specifically, the residuals of linear principal component analysis are first calculated: ; Use nonlinear methods to extract nonlinear features from residuals: ； in, Reconstruction function representing nonlinear method Then perform matrix reconstruction: the final reconstructed matrix , capturing the local nonlinear motion of the bridge deck.

5. The ultra-precision global bridge rotation angle measurement method according to claim 1 is characterized in that: In the displacement calculation step, the Lucas-Kanade optical flow method is used to calculate the displacement of the feature points, and a double-layer filtering algorithm based on the time domain and the frequency domain is used to remove high-frequency noise and low-frequency background motion interference.

6. The ultra-precision global bridge rotation angle measurement method according to claim 1 is characterized in that: The double-layer filtering algorithm includes using fast Fourier transform to convert the displacement data in the time domain to the frequency domain, designing high-pass filter and low-pass filter to remove unnecessary frequency components, and then using inverse fast Fourier transform to convert the filtered frequency domain signal back to the time domain.

7. The ultra-precision global bridge rotation angle measurement method according to claim 1 is characterized in that: In the displacement calculation step, a preset number of pixels are taken on the left and right sides of each feature point, and the least squares fitting method is used to smooth the displacement data, further reduce noise, and calculate the local angle based on the fitting result. The calculation method is as follows: For each feature point, determine the set of a preset number of pixel points on its left and right sides; The displacement data were fitted using the least squares method; For a given n data points , where n=21, the preset number of pixels on the left and right plus the center point, the slope a and intercept b of the line are determined by solving the following set of equations: ； ； Solve the equation to get the values ​​of slope a and intercept b, where slope a is the tangent of the local angle.

8. The ultra-precision global bridge rotation angle measurement method according to claim 1 is characterized in that: The local angle calculation method includes determining the slope of the straight line and calculating the local angle by the inverse tangent function: according to the slope a, the inverse tangent function is used to calculate the local angle. Calculate the local angle .

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