High-precision global quasi-static strain vision measurement method

Abstract

The invention discloses a high-precision global quasi-static strain vision measurement method, and relates to the technical field of strain vision measurement, and the method specifically comprises the steps: 1, obtaining high-resolution continuous image deformation data, correcting the geometric distortion of a deformed image through an ortho-rectification method, and carrying out the correction of the geometric distortion of the deformed image; the method comprises the following steps: step 1, acquiring high-resolution and continuous image deformation data through a high-precision image acquisition device, and accurately correcting geometric distortion existing in a deformed image by using an orthographic correction method, step 2, carrying out noise reduction processing on the image through a singular value decomposition technology, carrying out image displacement analysis on the image subjected to the noise reduction processing, and carrying out image displacement analysis on the image subjected to the image displacement analysis. The method comprises the following steps: step 1, carrying out deep noise reduction processing on an image by using an advanced singular value decomposition technology to effectively remove various noise interferences in the image, and step 2, carrying out further processing on data obtained after strain calculation is completed, and visually displaying a strain result through visual output. The invention provides an efficient and reliable solution for complex structure global strain measurement.

Description

Technical Field

[0001] The present invention belongs to the technical field of visual strain measurement, and in particular relates to a high-precision full-domain quasi-static visual strain measurement method. Background Art

[0002] In the field of engineering and materials science, accurately measuring the global strain distribution of an object under stress conditions is the key to assessing structural safety, predicting failure modes, and optimizing designs. Although traditional strain measurement technologies (such as resistance strain gauges and fiber optic sensors) have high local measurement accuracy, they have the following significant defects: they can only obtain discrete point data and cannot perform global strain distribution analysis; contact installation is prone to interfere with the surface state of the object being measured, especially for sensitive materials such as thin films and biological tissues; comprehensive measurement of complex geometric structures or large-sized objects is costly and difficult to implement.

[0003] In recent years, non-contact visual measurement technology (such as digital image correlation, DIC) has gradually become a research hotspot. DIC calculates the global strain distribution by tracking the displacement changes of feature points on the surface of an object, but its accuracy and reliability are limited by image quality, noise interference and geometric distortion correction capabilities. In existing technologies, image acquisition systems often cause accumulated strain calculation errors due to lens distortion, camera viewing angle deviation or image blur under dynamic loading; in addition, image denoising algorithms (such as mean filtering and wavelet transform) are difficult to balance between retaining details and suppressing noise, which can easily cause the loss of small deformation information.

[0004] Therefore, there is an urgent need for a high-precision global quasi-static strain visual measurement method to overcome the limitations of traditional methods in terms of accuracy, efficiency and applicability. Summary of the invention

[0005] The purpose of the present invention is to provide a high-precision global quasi-static strain visual measurement method to solve the technical problems in the prior art that are limited in accuracy, efficiency and applicability.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A high-precision full-domain quasi-static strain visual measurement method, comprising: Step 1: Obtain high-resolution continuous image deformation data and correct the geometric distortion of the deformed image by using the orthorectification method; That is, high-resolution and continuous image deformation data is obtained through high-precision image acquisition equipment, and the geometric distortion existing in the deformed image is accurately corrected using the orthorectification method; Step 2: De-noise the image by using singular value decomposition technology, and perform image displacement analysis on the image after de-noising; Step 3: Further process the data obtained after strain calculation, and intuitively display the strain results through visual output.

[0007] Furthermore, high-resolution continuous image deformation data is obtained, and the specific method is as follows: A high-resolution CMOS camera with a resolution of n×m pixels and a frame rate equal to f frames per second is selected to adapt to the dynamic deformation of the object being monitored during quasi-static loading. The camera is equipped with a high-precision lens, such as a telecentric lens or an ultra-wide-angle lens, to reduce geometric distortion and expand the field of view. The camera is fixed in front of the object to be measured through a high-precision three-dimensional adjustment bracket, and the internal and external parameters of the camera are calibrated through a high-precision calibration plate. The mapping relationship between pixel coordinates and actual physical dimensions is established to complete the configuration of the image acquisition system. During the loading process, the system continuously acquires the image sequence of the object to be measured to ensure the acquisition of continuous and comparable deformation data.

[0008] Furthermore, the geometric distortion of the deformed image is corrected by the orthorectification method. The specific method is as follows: Using the configured image acquisition system, during the quasi-static loading process, the surface images of the object are continuously captured at a preset frame rate to generate time series data. The geometric correction requires the use of multiple frames of images in the continuously captured image sequence for distortion correction. The distortion correction requires the definition of source points and target points. Four feature points are selected on the surface of the object to be measured or on the calibration plate as the source points. The coordinates of the source points are expressed as Indicated by, where i=1, 2, 3, 4. The source points are located in the four corners or specific areas of the image, and the target points corresponding to each source point are defined based on the actual physical size. The coordinates of the target points are expressed as It means that the target points constitute a rectangular area, whose coordinates are strictly aligned with the actual physical coordinate system. The perspective transformation matrix is ​​solved according to the origin and the target point. The geometric distortion residual is calculated by comparing the length of the line segment of known size on the calibration plate with the corresponding length of the corrected image. If the residual is greater than the preset tolerance, the transformation matrix H is iteratively adjusted until the accuracy requirement is met to obtain the corrected image. The corrected image sequence matrix is ​​recorded as M.

[0009] Furthermore, the perspective transformation matrix is ​​solved according to the origin and the target point. The specific method is: use Represents the formula for solving the perspective transformation matrix, where represents the scaling factor of the i-th target point, and , using the formula represents the perspective transformation matrix, where The transformation parameters to be solved of the perspective transformation matrix are represented. For each pair of source points and target points, the transformation parameters are solved by the perspective transformation matrix solution formula to obtain the corresponding linear equations. The linear equations corresponding to all source points and target points are solved, and the calculated perspective transformation matrix H is applied to each pixel point of the original image. The grayscale value of each pixel in the target image is calculated by bilinear interpolation to generate an image corrected by orthographic projection. During continuous image acquisition, the offset of the source point is detected in real time. If the offset exceeds the preset threshold a, the perspective transformation matrix H is recalculated until a perspective transformation matrix that meets the threshold requirement is obtained.

[0010] Furthermore, the image is subjected to denoising by using singular value decomposition technology. The specific method is as follows: After obtaining the corrected image, the singular value decomposition technique is used to decompose the image sequence matrix M to obtain singular values ​​and singular vectors. The singular value energy threshold Q is preset. The sum of the singular value energy is greater than or equal to the threshold Q. The singular modes that meet this condition and have the least number are selected as signal modes to form a mode set. , construct the weight matrix according to the modal set, and use the formula Perform weighted average filtering on the original gray value, where represents the filtered image sequence matrix, W represents the modality set The constructed weight matrix is ​​given by the formula express, Represents a modal set The weight matrix is ​​used to perform weighted average filtering on the original grayscale values, and then the filtered grayscale values ​​are used to replace the grayscale values ​​in the original image. The image is reconstructed using the retained singular values ​​and singular vectors to complete image denoising and obtain a filtered image sequence.

[0011] Furthermore, the image displacement analysis is performed on the image after the noise reduction process. The specific method is as follows: After image denoising, the digital image correlation method is used to perform displacement analysis on the image. The image is a two-dimensional matrix composed of pixels, and each pixel has a corresponding coordinate position. The displacement vector between the current frame and the reference frame is calculated by the digital image correlation method. The numerical interpolation method is used to interpolate each pixel of the image to obtain a smooth and continuous displacement field. The formula represents the displacement field, where (x, y) represents the coordinates of any pixel in the image. Indicates the displacement of the pixel with coordinates (x, y) in the x direction. It represents the displacement of a pixel with coordinates (x, y) in the y direction. The strain is calculated by the gradient of the displacement field. The strain amount is calculated for each point to obtain the strain distribution of the entire surface of the photographed object.

[0012] Furthermore, the strain is calculated by the gradient of the displacement field. The specific method is: use The formula for calculating strain through the gradient of the displacement field is represented, where x represents the horizontal coordinate of any pixel in the image, and y represents the vertical coordinate of any pixel in the image. Represents the displacement of each pixel in the x direction, Represents the displacement of each pixel in the y direction, represents the positive strain of each pixel in the x direction, represents the positive strain of each pixel in the y direction, Represents the shear strain at each pixel.

[0013] Furthermore, the data obtained after the strain calculation is further processed, and the strain results are intuitively displayed through visual output. The specific method is as follows: Low-pass filtering is used to remove high-frequency noise that appears in the data after strain calculation, smooth the strain data, eliminate abnormal fluctuations caused by measurement errors and noise, and obtain filtered strain field data. Then, out-of-plane correction is used to solve the geometric distortion problem on the surface of the object in the experiment. By adopting a geometric correction algorithm, the strain data is corrected out of the plane to ensure that the strain data accurately reflects the actual deformation of the object surface, and the strain results are displayed in a graphical way through visualization tools.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention achieves accurate monitoring and analysis of the deformation of the monitored object during quasi-static loading by acquiring high-resolution continuous image deformation data and correcting geometric distortion using the orthorectification method, which helps to improve the accuracy of deformation analysis. It helps to improve the accuracy of the corrected image by detecting the offset of the source point in real time and recalculating the perspective transformation matrix when necessary. 2. The present invention uses singular value decomposition to identify and distinguish signal components and noise components in the image, effectively filters out the noise mode, and realizes denoising of the image. It uses a weight matrix to perform weighted average filtering on the original grayscale value, further enhancing the denoising effect, making the filtered image sequence clearer and better retaining details. After completing the denoising process, the digital image correlation method is used to perform displacement analysis on the image, which can accurately calculate the displacement vector between the current frame and the reference frame, helping to improve data analysis efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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.

[0016] Figure 1 A step diagram of a high-precision full-domain quasi-static strain visual measurement method is shown; Figure 2 A flow chart of a high-precision global quasi-static strain visual measurement method is shown. DETAILED DESCRIPTION

[0017] 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.

[0018] Embodiment 1: Figure 1 , Figure 2 A high-precision global quasi-static strain visual measurement method is shown, which specifically includes the following steps: Step 1: Obtain high-resolution continuous image deformation data and correct the geometric distortion of the deformed image by orthorectification method.

[0019] A high-resolution CMOS camera with a resolution of n×m pixels and a frame rate equal to f frames per second is selected to adapt to the dynamic deformation of the object under quasi-static loading. The camera is equipped with a high-precision lens, such as a telecentric lens or an ultra-wide-angle lens, to reduce geometric distortion and expand the field of view. The camera is fixed in front of the object under test through a high-precision three-dimensional adjustment bracket, and the internal and external parameters of the camera are calibrated through a high-precision calibration plate. The mapping relationship between pixel coordinates and actual physical dimensions is established to complete the configuration of the image acquisition system. During the loading process, the system continuously acquires an image sequence of the object under test to ensure the acquisition of continuous and comparable deformation data. In this embodiment, n×m is set to 1000×2000 and f is set to 50; In the image acquisition and geometric correction stage, the configured image acquisition system is used to continuously capture the surface image of the object at a preset frame rate during the quasi-static loading process to generate time series data. The geometric correction requires the use of multiple frames of images in the continuously captured image sequence for distortion correction. The distortion correction requires the definition of source points and target points, including selecting four feature points on the surface of the object to be measured or on the calibration plate as the source points. The coordinates of the source points are used as Indicated by, where i=1, 2, 3, 4. The source points are located in the four corners or specific areas of the image, and the target points corresponding to each source point are defined based on the actual physical size. The coordinates of the target points are expressed as Indicates that the target points form a rectangular area, whose coordinates are strictly aligned with the actual physical coordinate system. The perspective transformation matrix is ​​solved according to the origin and the target point. The specific formula is as follows: ; in, represents the scaling factor of the i-th target point, and , H represents the perspective transformation matrix; Furthermore, the specific expression of the perspective transformation matrix is ​​as follows; ; in, Represents the transformation parameters to be solved of the perspective transformation matrix. In this embodiment, For each pair of source and target points, the transformation parameters are solved by the perspective transformation matrix solution formula to obtain the corresponding linear equations. The linear equations corresponding to all source and target points are solved, and the calculated perspective transformation matrix H is applied to each pixel of the original image. The gray value of each pixel in the target image is calculated by bilinear interpolation to generate an image corrected by orthographic projection. During continuous image acquisition, the offset of the source point is detected in real time. If the offset exceeds the preset threshold a, the perspective transformation matrix H is recalculated to ensure that the proportional error between the corrected image and the actual physical size is less than the actual error acceptance range. By comparing the length of the line segment of known size on the calibration plate with the corresponding length of the corrected image, the geometric distortion residual is calculated. If the residual is greater than the preset tolerance, the transformation matrix H is iteratively adjusted until the accuracy requirement is met to obtain the corrected image. The corrected image sequence matrix is ​​recorded as M.

[0020] Step 2: Perform denoising on the image using singular value decomposition technology, and perform image displacement analysis on the denoised image.

[0021] After obtaining the corrected image, the singular value decomposition technique is used to decompose the image sequence matrix M to obtain singular values ​​and singular vectors. The singular value energy threshold Q is preset. The sum of the singular value energy is greater than or equal to the threshold Q. The singular modes that meet this condition and have the least number are selected as signal modes to form a mode set. , construct a weight matrix according to the modal set, and use the weight matrix to perform weighted average filtering on the original grayscale value. The specific formula is as follows: ; in, represents the filtered image sequence matrix, W represents the modality set The constructed weight matrix is ​​given by the formula express, Represents a modal set The weight matrix is ​​used to perform weighted average filtering on the original grayscale values, and then the filtered grayscale values ​​are used to replace the grayscale values ​​in the original image. The image is reconstructed using the retained singular values ​​and singular vectors to complete image denoising and obtain a filtered image sequence.

[0022] After image denoising, the digital image correlation method is used to perform displacement analysis on the image. The image is a two-dimensional matrix composed of pixels, and each pixel has a corresponding coordinate position. The displacement vector between the current frame and the reference frame is calculated by the digital image correlation method. The numerical interpolation method is used to interpolate each pixel of the image to obtain a smooth and continuous displacement field. The specific expression of the displacement field is as follows: ; Among them, (x, y) represents the coordinates of any pixel in the image. Indicates the displacement of the pixel with coordinates (x, y) in the x direction. Indicates the displacement of a pixel with coordinates (x, y) in the y direction.

[0023] The strain is calculated by the gradient of the displacement field. The specific formula is as follows: ; Among them, x represents the horizontal coordinate of any pixel in the image, and y represents the vertical coordinate of any pixel in the image. Represents the displacement of each pixel in the x direction, Represents the displacement of each pixel in the y direction, represents the positive strain of each pixel in the x direction, represents the positive strain of each pixel in the y direction, Represents the shear strain at each pixel.

[0024] By calculating the above strain amount for each point in the image, the strain distribution of the entire surface of the photographed object can be obtained.

[0025] Step 3: Further process the data obtained after strain calculation, and intuitively display the strain results through visual output.

[0026] After completing the strain calculation, the data obtained may contain high-frequency fluctuations caused by noise, and these high-frequency noises need to be removed by low-pass filtering. The rule is that low-frequency signals can pass normally, while high-frequency signals exceeding the set critical value are blocked and weakened, but the amplitude of blocking and weakening will change according to different frequencies and different filtering purposes. This frequency is also the cutoff frequency. When the frequency domain is higher than this cutoff frequency, all signals are blocked or assigned a value of 0, because all low-frequency signals are allowed to pass through in this process. In this embodiment, Gaussian filtering is applied to filter signals above 1 HZ, smooth strain data, eliminate abnormal fluctuations caused by measurement errors and noise, and obtain filtered strain field data. Then, out-of-plane correction is used to solve the geometric distortion problem on the surface of the object in the experiment. By adopting a geometric correction algorithm, such as the least squares method, the strain data is corrected out of the plane to ensure that the strain data accurately reflects the actual deformation of the surface of the object, and the strain result after the above processing is recorded as the output strain result.

[0027] The strain results are displayed graphically, including thermal maps, 3D strain maps, and strain animations. These visualization tools make it easy to visually observe the strain distribution on the surface of an object. Finally, the strain data can be exported to common formats, such as CSV and Excel, for further analysis and recording.

[0028] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0029] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A high-precision global quasi-static strain visual measurement method, characterized in that: include: Step 1: Obtain high-resolution continuous image deformation data and correct the geometric distortion of the deformed image by using the orthorectification method; That is, high-resolution and continuous image deformation data is obtained through high-precision image acquisition equipment, and the geometric distortion existing in the deformed image is accurately corrected using the orthorectification method; Step 2: De-noise the image by using singular value decomposition technology, and perform image displacement analysis on the image after de-noising; Step 3: Further process the data obtained after strain calculation, and intuitively display the strain results through visual output.

2. A high-precision global quasi-static strain visual measurement method according to claim 1, characterized in that: Obtain high-resolution continuous image deformation data. The specific method is: A high-resolution CMOS camera with a resolution of n×m pixels and a frame rate equal to f frames per second is selected to adapt to the dynamic deformation of the object being monitored during quasi-static loading. The camera is equipped with a high-precision lens, such as a telecentric lens or an ultra-wide-angle lens, to reduce geometric distortion and expand the field of view. The camera is fixed in front of the object to be measured through a high-precision three-dimensional adjustment bracket, and the internal and external parameters of the camera are calibrated through a high-precision calibration plate. The mapping relationship between pixel coordinates and actual physical dimensions is established to complete the configuration of the image acquisition system. During the loading process, the system continuously acquires the image sequence of the object to be measured to ensure the acquisition of continuous and comparable deformation data.

3. A high-precision global quasi-static strain visual measurement method according to claim 1, characterized in that: The geometric distortion of the deformed image is corrected by the orthorectification method. The specific method is as follows: Using the configured image acquisition system, during the quasi-static loading process, the surface images of the object are continuously captured at a preset frame rate to generate time series data. The geometric correction requires the use of multiple frames of images in the continuously captured image sequence for distortion correction. The distortion correction requires the definition of source points and target points. Four feature points are selected on the surface of the object to be measured or on the calibration plate as the source points. The coordinates of the source points are expressed as Indicated by, where i=1, 2, 3, 4, the source points are located in the four corners or specific areas of the image, and the target point corresponding to each source point is defined based on the actual physical size. The coordinates of the target point are expressed as It means that the target points constitute a rectangular area, whose coordinates are strictly aligned with the actual physical coordinate system. The perspective transformation matrix is ​​solved according to the origin and the target point. The geometric distortion residual is calculated by comparing the length of the line segment of known size on the calibration plate with the corresponding length of the corrected image. If the residual is greater than the preset tolerance, the transformation matrix H is iteratively adjusted until the accuracy requirement is met to obtain the corrected image. The corrected image sequence matrix is ​​recorded as M.

4. A high-precision global quasi-static strain visual measurement method according to claim 3, characterized in that: Solve the perspective transformation matrix based on the origin and target point. The specific method is: use Represents the formula for solving the perspective transformation matrix, where represents the scaling factor of the i-th target point, and , using the formula represents the perspective transformation matrix, where The transformation parameters to be solved of the perspective transformation matrix are represented. For each pair of source points and target points, the transformation parameters are solved by the perspective transformation matrix solution formula to obtain the corresponding linear equations. The linear equations corresponding to all source points and target points are solved, and the calculated perspective transformation matrix H is applied to each pixel point of the original image. The grayscale value of each pixel in the target image is calculated by bilinear interpolation to generate an image corrected by orthographic projection. During continuous image acquisition, the offset of the source point is detected in real time. If the offset exceeds the preset threshold a, the perspective transformation matrix H is recalculated until a perspective transformation matrix that meets the threshold requirement is obtained.

5. A high-precision global quasi-static strain visual measurement method according to claim 1, characterized in that: The image is denoised using singular value decomposition technology. The specific method is as follows: After obtaining the corrected image, the singular value decomposition technique is used to decompose the image sequence matrix M to obtain singular values ​​and singular vectors. The singular value energy threshold Q is preset. The sum of the singular value energy is greater than or equal to the threshold Q. The singular modes that meet this condition and have the least number are selected as signal modes to form a mode set. , construct the weight matrix according to the modal set, and use the formula Perform weighted average filtering on the original gray value, where represents the filtered image sequence matrix, W represents the modality set The constructed weight matrix is ​​given by the formula express, Represents a modal set The weight matrix is ​​used to perform weighted average filtering on the original grayscale values, and then the filtered grayscale values ​​are used to replace the grayscale values ​​in the original image. The image is reconstructed using the retained singular values ​​and singular vectors to complete image denoising and obtain a filtered image sequence.

6. A high-precision global quasi-static strain visual measurement method according to claim 1, characterized in that: Perform image displacement analysis on the image that has completed noise reduction processing. The specific method is as follows: After image denoising, the digital image correlation method is used to perform displacement analysis on the image. The image is a two-dimensional matrix composed of pixels, and each pixel has a corresponding coordinate position. The displacement vector between the current frame and the reference frame is calculated by the digital image correlation method. The numerical interpolation method is used to interpolate each pixel of the image to obtain a smooth and continuous displacement field. The formula represents the displacement field, where (x, y) represents the coordinates of any pixel in the image. Indicates the displacement of the pixel with coordinates (x, y) in the x direction. It represents the displacement of a pixel with coordinates (x, y) in the y direction. The strain is calculated by the gradient of the displacement field. The strain amount is calculated for each point to obtain the strain distribution of the entire surface of the photographed object.

7. A high-precision global quasi-static strain visual measurement method according to claim 6, characterized in that: The strain is calculated by the gradient of the displacement field. The specific method is: use The formula for calculating strain through the gradient of the displacement field is represented, where x represents the horizontal coordinate of any pixel in the image, and y represents the vertical coordinate of any pixel in the image. Represents the displacement of each pixel in the x direction, Represents the displacement of each pixel in the y direction, represents the positive strain of each pixel in the x direction, represents the positive strain of each pixel in the y direction, Represents the shear strain at each pixel.

8. The high-precision global quasi-static strain visual measurement method according to claim 1 is characterized in that: The data obtained after strain calculation is further processed, and the strain results are intuitively displayed through visual output. The specific method is as follows: Low-pass filtering is used to remove high-frequency noise that appears in the data after strain calculation, smooth the strain data, eliminate abnormal fluctuations caused by measurement errors and noise, and obtain filtered strain field data. Then, out-of-plane correction is used to solve the geometric distortion problem on the surface of the object in the experiment. By adopting a geometric correction algorithm, the strain data is corrected out of the plane to ensure that the strain data accurately reflects the actual deformation of the object surface, and the strain results are displayed in a graphical way through visualization tools.

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