A method for measuring the rotation angle of a super-precision global bridge

Abstract

The present invention discloses an ultra-precision global bridge rotation angle measurement method, which relates to the technical field of angle measurement. The method includes: continuously capturing a sequence of bridge motion images and recording timestamps. Selecting key points on the bridge deck through a feature detection algorithm, constructing a spatio-temporal data matrix and performing data normalization processing. Extracting the global features of the overall motion of the bridge deck by using linear principal component analysis; extracting the local non-linear motion features in the residuals by using non-linear principal component analysis. Calculating the displacement of the feature points based on the reconstruction matrix in combination with the LK optical flow method. Fitting the displacement data by using the least squares method, and calculating the angle based on the arctangent function on this basis. This method has the characteristics of high precision and global coverage, is applicable to fields such as bridge health monitoring and safety assessment, and can effectively capture the overall and local motion characteristics of the bridge.

Description

Technical Field

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

[0002] Bridge tilt monitoring is a crucial part of bridge structural health monitoring. The tilt change directly reflects the stress and deformation state of the bridge under external loads. The bridge may tilt under the action of earthquakes, wind loads, traffic loads, or uneven settlement, 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 the bridge structure but also a key technology to improve the reliability and durability of transportation infrastructure. Angle measurement across the entire area is very important. In addition, the bridge may produce non-linear deformation under the action of vehicle loads, wind loads, and seismic loads, and the existing technology has insufficient ability to handle non-linear deformation.

[0003] Existing solutions include contact and non-contact methods. The contact measurement method has tilt sensors, which can achieve sub-degree or even higher-precision inclination measurement and is suitable for detecting small angle changes. However, it has complex installation, discrete point measurement, is easily affected by temperature changes, vibration, or humidity, and the tilt sensor has a slow response to rapid dynamic changes (such as vibration or shock) and is not suitable for high-frequency dynamic measurement, etc. The non-contact measurement method has a laser Doppler vibrometer. Although it has high precision, its application in the laboratory has great limitations; the optical flow analysis method has low sensitivity and is difficult to meet actual requirements.

[0004] The vision-based image measurement method has been widely used in displacement measurement due to advantages such as low cost and easy implementation. The existing methods mainly include digital image correlation (DIC). The DIC method generally requires arranging a target with a random speckle pattern on the surface of the object to be measured and has been widely used in the field of structural deformation measurement. However, due to its dependence on the target, the number of spatial measurement points of the DIC method is severely limited by the number of targets; due to the large amount of correlation calculations involved in the algorithm, the calculation efficiency of the DIC method is relatively low and it is difficult to meet the requirements of high-sampling-rate online measurement. The DIC accuracy is low (about 0.1 pixel), requires a target with a random speckle pattern, and is difficult to implement. The target is easily damaged in the natural environment, and the design of a full-scale target is unrealistic. Usually, only point measurement (random pattern targets are locally set on the bridge deck) can be achieved using DIC.

[0005] Different from the DIC method, the optical flow method uses the natural texture of the object surface or the gray-scale spatial change generated by the object edge after imaging to calculate the displacement of all effective pixel points without a target, and has high calculation efficiency.

[0006] The present invention obtains a sequence of bridge motion images through a high - frame - rate camera, combines linear principal component analysis and non - linear principal component analysis methods to obtain displacements at different pixel positions of the bridge deck, and then calculates angles to optimize the measurement accuracy and robustness of the rotation angle in real time. Summary of the Invention

[0007] In order to overcome the disadvantages and deficiencies of the above - mentioned prior art, the present invention adopts the following technical solutions:

[0008] A method for measuring the rotation angle of an ultra - precision global bridge has the following process:

[0009] Step 1, data acquisition: Continuously capture a sequence of bridge motion images using a high - frame - rate camera with a frame rate more than 5 times higher than the bridge motion frequency, and record the corresponding timestamps;

[0010] Step 2, data pre - processing: Calibrate and correct the acquired images, select feature points, calculate the optical flow of the feature points, track the feature points to construct a spatio - temporal data matrix, and normalize the data;

[0011] Step 3, analysis process: Perform linear principal component analysis on the spatio - temporal data matrix to extract the global features of the overall motion of the bridge deck, and then perform non - linear principal component analysis on the residuals to capture the local non - linear motion of the bridge deck;

[0012] Step 4, displacement calculation: Use the optical flow method to calculate the displacements of all feature points in the reconstruction 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.

[0013] Preferably, in the data acquisition step, the resolution of the camera needs to meet the requirement of clearly capturing the pixel details of the bridge deck, and the installation position needs to ensure that the camera can globally observe the bridge deck to avoid line - of - sight occlusion.

[0014] Preferably, in the data pre - processing step, use a checkerboard calibration board for camera calibration, calculate the internal parameter matrix and distortion coefficient of the camera, and use a correction algorithm to correct the images to remove lens distortion.

[0015] 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 features of the overall motion of the bridge deck. Specifically, construct a feature - point displacement matrix, where the rows represent time frames and the columns represent the displacements of feature points, and use PCA to extract the first r main components:

[0016] ;

[0017] where is the main - component matrix; It is the principal component projection; furthermore, analyze the principal components to extract the global features of the overall movement of the bridge deck and capture the global linear pattern;

[0018] Nonlinear principal component analysis is used to extract the nonlinear features in the residuals of linear principal component analysis to capture the local nonlinear movement of the bridge deck. Specifically, first calculate the residuals of linear principal component analysis: ; Use a nonlinear method to extract the nonlinear features from the residuals:

[0019] ;

[0020] where, represents the reconstruction function of the nonlinear method

[0021] Then perform matrix reconstruction: The final reconstructed matrix , capturing the local nonlinear movement of the bridge deck.

[0022] Preferably, in the displacement calculation step, the Lucas-Kanade optical flow method is used to calculate the displacement of the feature points, and high-frequency noise and low-frequency background motion interference are removed through a double-layer filtering algorithm based on the time domain and the frequency domain.

[0023] Preferably, the double-layer filtering algorithm includes using the fast Fourier transform to convert the displacement data in the time domain to the frequency domain, designing a high-pass filter and a low-pass filter to remove the unwanted frequency components, and then using the inverse fast Fourier transform to convert the filtered frequency domain signal back to the time domain.

[0024] Preferably, in the displacement calculation step, 10 pixel points are taken on each side of each feature point, and the least squares method is used to fit the displacement data, and the local angle is calculated according to the fitting result. The calculation method is as follows:

[0025] For each feature point, determine the set of 10 pixel points on each of its left and right sides;

[0026] Use the least squares method to fit the displacement data;

[0027] For the given n data points , where n = 21, 10 pixel points on each of the left and right sides plus the center point, determine the slope a and intercept b of the straight line by solving the following system of equations:

[0028] ;

[0029] ;

[0030] Solve the equation to obtain the values of the slope a and intercept b, where the slope a is the tangent of the local angle.

[0031] Preferably, the calculation method of the local angle includes determining the slope of the straight line and calculating the local angle through the arctangent function: according to the slope a, through the arctangent function calculate the local angle .

[0032] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows:

[0033] 1. The present invention accurately tracks the displacement of feature points through the LK optical flow method, and combines linear principal component analysis and nonlinear principal component analysis to extract the global features and local nonlinear features of the overall movement of the bridge deck. This method can not only capture the overall movement mode of the bridge deck, but also reflect the complex deformation in the edge area, thus providing high-precision data support for the comprehensive evaluation of the bridge health state.

[0034] 2. The present invention effectively eliminates high-frequency noise and low-frequency background motion interference by adopting a double-layer filtering algorithm based on time domain and frequency domain, further improving the reliability of the measurement data. This combined method enables the present method to operate stably in various harsh environments and ensures the accuracy of the measurement results.

[0035] 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 the complex deformation in the edge area. This method can not only quickly extract key information, but also retain detailed features, greatly reducing the computational complexity and improving the data processing efficiency at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0037] Figure 1 Shows a flowchart of a method for measuring the rotation angle of a super-precision global bridge of the present invention;

[0038] Figure 2 Shows a flowchart of the displacement calculation of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to give a thorough understanding of the example embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure may be practiced omitting one or more of the specific details, or using other methods, components, steps, etc. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0041] Embodiment 1, refer to Figure 1 As shown, a method for measuring the rotation angle of a super-precision global bridge in this embodiment is as follows:

[0042] Step 1: Data acquisition.

[0043] High-frame-rate camera settings: The camera frame rate is higher than 5 times the bridge movement frequency (for example, for common vibration frequencies of 0.5 - 5 Hz, the frame rate is set to 30 Hz or higher), and the resolution needs to meet the requirement of clearly capturing the pixel details of the bridge deck; the installation position should ensure that the camera can globally observe the bridge deck (such as installing at the bridgehead or shooting with a drone), and avoid line-of-sight occlusion during shooting.

[0044] Data acquisition operation: Continuously shoot a sequence of bridge movement images and record the corresponding timestamps; at the same time, pay attention to avoiding the influence of strong light, shadows or rainy and foggy weather on the image quality, and infrared or thermal imaging cameras can be selected when necessary to improve the anti-interference ability.

[0045] Step 2: Data preprocessing.

[0046] Prepare a calibration board: Usually, a checkerboard calibration board is used, which has a regular pattern of black and white squares. The size of the calibration board and the size of the squares should be selected according to the actual situation. Generally, the size of the squares is between a few millimeters and dozens of millimeters.

[0047] Shoot calibration images: Place the calibration board at different positions and angles, and use the camera to shoot multiple images containing the calibration board. When shooting, it should be ensured that the calibration board is completely within the image range, and the images are clear and not blurred. The number of images taken is generally recommended to be between 10 and 20.

[0048] Detecting Corners: Use an image processing algorithm (such as the findChessboardCorners function in OpenCV) to detect the corners of the chessboard in each calibration image. Corners are the vertices of the chessboard squares, and the detected corner coordinates will be used for subsequent calibration calculations.

[0049] Calibration Calculation: Use the detected corner coordinates and the known size of the calibration board squares, and calculate the camera's internal 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's calibration method). In Python, the calibrateCamera function in OpenCV can be used for calibration calculations.

[0050] Image Rectification: According to the calculated internal parameter matrix and distortion coefficients, use a rectification algorithm (such as the undistort function in OpenCV) to rectify the captured bridge movement images and remove lens distortion.

[0051] Selecting Feature Points: Use a feature detection algorithm (such as the Shi - Tomasi corner detection algorithm) to detect key points or feature regions 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.

[0052] Calculating 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 of images. The LK optical flow method assumes that within a small neighborhood, the motion of all pixel points is consistent, and calculates the displacement of feature points by solving the optical flow constraint equation.

[0053] Tracking Feature Points: In each frame of the image, use the calculated optical flow to update the positions of the feature points and record the time series of the displacements of the feature points. If a certain feature point is lost in a certain frame, the feature detection algorithm can be used to re - detect the feature point or interpolation methods can be used for estimation.

[0054] Constructing the Spatiotemporal Data Matrix: Combine the time series of displacements of all feature points into a matrix, where the rows of the matrix represent time frames and the columns represent the displacements of feature points, thus obtaining the spatiotemporal data matrix.

[0055] Data Normalization: Normalize the feature point displacement data to eliminate the influence of dimensions. The formula is:

[0056] ;

[0057] where is the mean; is the standard deviation.

[0058] Step 3. Analysis process.

[0059] Linear principal component analysis (PCA): Construct a feature point displacement matrix (rows represent time frames, columns represent the displacements of feature points), and use PCA to extract the first few principal components:

[0060] ;

[0061] Among them, is the principal component matrix; is the principal component projection.

[0062] Furthermore, analyze the principal components to extract the global features of the overall movement of the bridge deck, so as to capture the global linear pattern, such as the main direction of the overall movement of the bridge deck.

[0063] Nonlinear principal component analysis (Kernel PCA or Autoencoder): First, calculate the residuals of the linear principal component analysis: ;

[0064] Then, use a nonlinear method (such as Kernel PCA) to extract the nonlinear features from the residuals:

[0065] ;

[0066] Among them, represents the reconstruction function of the nonlinear method

[0067] After that, perform matrix reconstruction:

[0068] Final reconstructed matrix: ;

[0069] For Autoencoder or Kernel PCA, is the approximate representation of the nonlinear features in the original space.

[0070] Thus, capture the local nonlinear movement of the bridge deck, such as the complex deformation in the edge area.

[0071] Step 4. Displacement calculation.

[0072] Refer to Figure 2 as shown, the displacement calculation process is as follows:

[0073] S41. Calculate displacement using optical flow method: Use the LK optical flow method to calculate the displacements of all feature points of the reconstructed matrix.

[0074] Specifically, based on the reconstructed matrix obtained in the previous steps, determine the feature points for which the displacements need to be calculated. These feature points are usually the key points or feature regions on the bridge deck that have been marked in the feature extraction step.

[0075] Define a small neighborhood window (e.g., 3×3, 5×5, etc.) for each feature point, and assume that the motion of pixels within this window is constant.

[0076] Based on the assumption of image brightness constancy (i.e., the brightness of a pixel remains unchanged in two adjacent frames), establish an optical flow constraint equation. This equation describes the relationship between the velocities of pixels in the x and y directions, the gradients of the image in these two directions, and the brightness change over time.

[0077] S42. Filtering and denoising: After calculating the optical flow method, through a double-layer filtering algorithm based on the time domain and frequency domain, eliminate high-frequency noise and low-frequency background motion interference.

[0078] Select a filtering algorithm, determine an appropriate filtering window size according to the characteristics of the noise and the variation law of the signal, and perform time-domain filtering on the displacement data of feature points obtained by the optical flow method according to the selected filtering algorithm and window size.

[0079] For example: Use the Fast Fourier Transform (FFT) to transform the displacement data in the time domain to the frequency domain to obtain the spectrum of the signal. According to the frequency range of bridge motion and the frequency characteristics of the noise, design a high-pass filter and a low-pass filter. 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 filters to the frequency-domain signal to remove unnecessary frequency components. Use the Inverse Fast Fourier Transform (IFFT) to transform the filtered frequency-domain signal back to the time domain to obtain the denoised displacement data.

[0080] S43. Local angle calculation: Take 10 pixel points on each side of each feature point, and calculate the overall angle of these points by fitting the local displacement data.

[0081] The calculation method is as follows:

[0082] For each feature point, determine the set of 10 pixel points on each of its left and right sides;

[0083] Use the least squares method to fit the displacement data;

[0084] For a given set of n data points (here, n = 21, 10 pixel points on each of the left and right sides plus the center point), determine the slope a and intercept b of the straight line by solving the following system of equations:

[0085] ;

[0086] ;

[0087] Solve the equation to obtain the values of the slope a and intercept b, where the slope a is the tangent of the local angle.

[0088] Angle calculation: Based on the slope a, the local angle is calculated through the arctangent function to obtain the local angle .

[0089] The beneficial effects of this embodiment are as follows: The beneficial effects of the ultra-precision global bridge rotation angle measurement method include high-precision acquisition and analysis, effectively removing lens distortion, accurately tracking feature points, capturing global and local motion features through linear and non-linear PCA, combining optical flow method and filtering denoising to improve the accuracy of displacement calculation, and achieving precise calculation of local angles.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0091] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0092] 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 foregoing method embodiments, and will not be repeated here.

[0093] In several embodiments provided by 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 merely illustrative. For example, the division of the units is only for some logical function divisions. In actual implementation, there may be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0094] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

Claims

1. A method for measuring the rotation angle of an ultra-precision global bridge, characterized in that, The method process is as follows: Step 1, Data acquisition: Continuously capture a sequence of bridge motion images using a high-frame-rate camera with a frame rate higher than a preset multiple of the bridge motion frequency, and record the corresponding timestamps; Step 2, Data preprocessing: Calibrate and correct the acquired images, select feature points, obtain the spatio-temporal data matrix of the research area, and perform normalization processing on the data; Step 3, Analysis process: Perform linear principal component analysis on the spatio-temporal data matrix to extract the global features of the overall movement of the bridge deck, and then perform non-linear principal component analysis on the residuals to capture the local non-linear movement of the bridge deck; 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 features of the overall movement of the bridge deck. Specifically, construct the feature point displacement matrix, where the rows represent time frames and the columns represent the displacements of feature points, and use PCA to extract the first r main components: Z = XW; where W is the principal component matrix; Z is the principal component projection; and then analyze the principal components to extract the global features of the overall movement of the bridge deck and capture the global linear pattern; Nonlinear principal component analysis is used to extract the nonlinear features in the residuals of linear principal component analysis to capture the local nonlinear motion of the bridge deck. Specifically, first calculate the residuals of linear principal component analysis: R = X - ZW T ; use a nonlinear method to extract the nonlinear features from the residuals: Z nonlinear = f nonlinear (R); where f nonlinear (R) represents the reconstruction function of the non-linear method Subsequently, matrix reconstruction is carried out: the final reconstructed matrix X reconstructed = X linear + X nonlinear , capturing the local non-linear motion of the bridge deck; Step 4, Displacement calculation: Use the optical flow method to calculate the displacements of all feature points in the reconstruction 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, and then calculate the local angle.

2. The ultra-precision global bridge rotation angle measurement method according to claim 1, characterized in that In the data acquisition step, the resolution of the camera needs to meet the requirement of clearly capturing the pixel details of the bridge deck, and the installation position needs to ensure that the camera can globally observe the bridge deck to avoid line-of-sight occlusion.

3. A method for measuring the rotation angle of a super-precision global bridge according to claim 1, characterized in that, In the data preprocessing step, use a checkerboard calibration board to calibrate the camera, calculate the internal parameter matrix and distortion coefficient of the camera, and use a correction algorithm to correct the images to remove lens distortion.

4. A method for measuring the rotation angle of a super-precision global bridge according to claim 1, characterized in that In the displacement calculation step, use the Lucas-Kanade optical flow method to calculate the displacements of feature points, and remove high-frequency noise and low-frequency background motion interference through a double-layer filtering algorithm based on the time domain and frequency domain.

5. A method for measuring the rotation angle of a super-precision global bridge according to claim 1, characterized in that, The double-layer filtering algorithm includes using the fast Fourier transform to convert the displacement data in the time domain to the frequency domain, designing a high-pass filter and a low-pass filter to remove unwanted frequency components, and then using the inverse fast Fourier transform to convert the filtered frequency domain signal back to the time domain.

6. The super-precision global bridge rotation angle measurement method according to claim 1, characterized in that In the displacement calculation step, take a preset number of pixel points on the left and right of each feature point, use the least squares fitting method to smooth the displacement data for further noise reduction, and calculate the local angle according to 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; Use the least squares method to fit the displacement data; For a given set of n data points (x i , y i ), where n = 21, a preset number of pixels on the left and right plus the center point, the slope a and intercept b of the straight line are determined by solving the following system of equations: Solve the equation to obtain the values of the slope a and the intercept b, where the slope a is the tangent of the local angle.

7. A method for measuring the rotation angle of an ultra-precision global bridge according to claim 6, characterized in that, The calculation method of the local angle includes determining the slope of the straight line and calculating the local angle through the arctangent function: According to the slope a, calculate the local angle θ through the arctangent function θ = arctan(a).

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