A long-distance displacement measurement device and measurement method based on cross-sinusoidal composite fringes

Abstract

This invention relates to a long-distance displacement measurement device and method based on intersecting sinusoidal composite fringes. The measurement device includes an intersecting sinusoidal composite fringes target, a telephoto camera, and a computer. The intersecting sinusoidal composite fringes target is attached to the object to be monitored to acquire displacement change information of the target object. The telephoto camera is used to acquire images of the attached intersecting sinusoidal composite fringes and send the acquired images to the computer for analysis. The computer performs post-processing on the intersecting sinusoidal composite fringes images to obtain the relative displacement information of the object to be measured. The proposed method and device have the characteristics of long detection distance, high detection accuracy, high sampling frequency, non-contact operation, and strong anti-interference. Therefore, this method has broad application prospects in various complex structural displacement monitoring tasks.

Description

Technical Field

[0001] This invention relates to the field of machine vision displacement measurement technology, and in particular to a long-distance displacement measurement device and method based on intersecting sinusoidal composite stripes. Background Technology

[0002] With the advancement of global urbanization and the improvement of modern infrastructure, the safety of buildings and infrastructure under construction or already in use has become a major concern. Real-time monitoring of the deformation and displacement responses of these buildings and facilities is therefore crucial.

[0003] Currently, displacement measurement largely relies on traditional measurement methods, such as trigonometric leveling represented by total stations, various contact sensors like linear variable differential transformers, and non-contact sensor measurement methods like laser Doppler vibration meters (LVDs). These methods have several drawbacks: (1) high learning costs and inability to achieve real-time monitoring and early warning around the clock. (2) limited measurement range, restrictive application scenarios, and difficult maintenance. (3) limited lifespan, high cost, and susceptibility to failure under environmental and other influences. (4) the accuracy of most long-distance measurement methods is only millimeters or higher, which cannot meet the requirements for measuring minute displacements.

[0004] With the development of computer vision, vision-based displacement measurement has been widely studied in the field of structural health monitoring. Existing common methods can be broadly classified into three types: (1) template matching (DIC), (2) feature point monitoring, and (3) optical flow. For computer vision, due to hardware limitations, as the distance between the object being measured and the camera increases, the acquired image will exhibit blurring, distortion, and reduced resolution. The lack of image information can lead to errors or failures in many computer vision-based measurement methods at long distances.

[0005] In summary, considering existing computer vision measurement methods and the problems encountered in actual shooting, our goal is to extract as much useful information as possible from a low-resolution, severely degraded image to increase measurement accuracy. Given the practical application requirements of computer vision, it is necessary to propose a displacement measurement method with higher accuracy and wider applicability. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a long-distance displacement measuring device and method based on intersecting sinusoidal composite stripes. The measuring device has the characteristics of long detection distance, high detection accuracy, high sampling frequency, non-contact, and strong anti-interference.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a long-distance displacement measurement device based on intersecting sinusoidal composite stripes, comprising an intersecting sinusoidal composite stripe target, a telephoto camera, and a computer; the intersecting sinusoidal composite stripe target is attached to the object to be monitored to acquire displacement change information of the target object; the telephoto camera is used to acquire images of the attached intersecting sinusoidal composite stripes and send the acquired images to the computer for analysis; the computer performs post-processing on the intersecting sinusoidal composite stripe images to obtain the relative displacement information of the object to be measured.

[0008] The present invention also provides a cross sinusoidal composite stripe target based on the aforementioned long-distance displacement measuring device based on cross sinusoidal composite stripes. The cross sinusoidal composite stripe target is composed of two sets of sinusoidal stripes with completely consistent amplitude frequencies and a relative angle of 90°. The edge area of ​​the cross sinusoidal composite stripe target consists of four gray blocks and a single sinusoidal stripe pattern, while the middle area consists of an oblique sinusoidal stripe pattern formed by the intersection of the edge sinusoidal stripes.

[0009] The present invention also provides a method for measuring long-distance displacement based on cross-sine composite fringes, based on the aforementioned long-distance displacement measuring device, comprising the following steps:

[0010] Step S1: A novel artificial target based on intersecting sinusoidal composite stripes was designed to collect the position information of the object being measured.

[0011] Step S2: Attach the cross sinusoidal composite stripe pattern to the object to be tested; place the telephoto camera in a suitable position, set appropriate focal length and aperture parameters, and ensure that the camera image is clear;

[0012] Step S3: When the object under test is displaced in the plane, the relative position of the stripe pattern attached to the object under test to the imaging sensor of the acquisition camera also changes; keep the camera position unchanged, and acquire a stripe image with position information through the camera.

[0013] Step S4: The collected stripe signal is transmitted to the computer, which preprocesses the stripe signal and extracts the displacement information of the object under test.

[0014] In a preferred embodiment, step S4 specifically includes the following steps:

[0015] Step S41: Preprocess the acquired stripe image, including noise reduction, sharpening, and contrast enhancement methods, to facilitate subsequent processing;

[0016] Step S42: Taking any row of the single sinusoidal region of the stripes as a reference, calculate the Pearson correlation coefficient for each of the other rows to obtain the Pearson correlation coefficient curves of the stripes on the X and Y axes;

[0017] Step S43: By cross-correlating the correlation coefficient curves before and after displacement, the relative displacement information of the object under test in the acquired images before and after displacement is obtained by indexing the maximum cross-correlation value.

[0018] In a preferred embodiment, step S42 specifically involves:

[0019] The single sinusoidal fringe region around the striped pattern is selected as the reference row. After calculating the Pearson correlation coefficient and removing the curve, a Fourier transform is performed on the curve. Then, the correlation coefficient curve is upsampled by zero-padding in the frequency domain, and finally, the time domain information is returned by inverse Fourier transform to increase the pixel resolution of the displacement. The formula for calculating the Pearson correlation coefficient is as follows:

[0020]

[0021] Where ρ X,Y The correlation coefficient is PCC; coν(X,Y) is the covariance of X and Y; σ X σ is the standard deviation of X; Y X is the standard deviation of Y; i and Y i It is the value of the i-th data point; and These are the means of X and Y, respectively.

[0022] In a preferred embodiment, step S43 specifically involves:

[0023] By cross-correlation of the correlation coefficient curves before and after displacement, the relative displacement information of the object under test in the acquired images before and after displacement is obtained through the maximum cross-correlation value index; the Pearson correlation coefficient curve before displacement is selected and autocorrelation is performed to obtain the maximum correlation value index; the Pearson correlation coefficient curves of the images before and after displacement are selected and cross-correlation is performed to obtain the maximum correlation value index; the two are subtracted to obtain the displacement distance of the object under test on the imaging sensor in pixels; after correcting the displacement value using the energy centroid method, the true displacement distance of the object under test is obtained through the scaling factor; the cross-correlation coefficient calculation formula is as follows:

[0024]

[0025] Where x[n] is the value of the reference frame signal x at time point n; y[n+k] is the value of the displacement frame signal y at time point n+k; k is the displacement value, which can be positive, negative, or zero; R xy[k] It is the cross-correlation value of signals x and y under time delay k.

[0026] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a long-distance displacement measurement device and method based on intersecting sinusoidal composite stripes. By attaching intersecting sinusoidal composite stripes to the object to be measured, acquiring motion images of the object to be measured through a telephoto camera, and finally analyzing and processing the stripe images by a computer to obtain the two-dimensional displacement of the object to be measured. Compared with existing algorithms, this calculation method has less computation, simpler device, stronger anti-interference ability, wider working range, and higher accuracy, and has strong practicality and broad application prospects. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the measuring device structure according to an embodiment of the present invention. A three-dimensional coordinate system is established at the center point of the cross sinusoidal composite stripe pattern. The camera optical axis perpendicular to the camera imaging plane is taken as the Z-axis, the horizontal direction perpendicular to the imaging optical axis is defined as the X-axis, and the vertical direction perpendicular to the imaging optical axis is defined as the Y-axis.

[0028] Figure 2 and Figure 3 This is a schematic diagram illustrating the stripe preprocessing implementation process in an embodiment of the present invention.

[0029] Figures 4 to 7 This is a schematic diagram of the measurement results in an embodiment of the present invention. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0032] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0033] like Figure 1As shown, this embodiment provides a vision-based long-distance displacement measurement device based on intersecting sinusoidal composite stripes, including an intersecting sinusoidal composite stripe target object, a telephoto camera, and a computer; the intersecting sinusoidal composite stripe target object is attached to the object to be monitored to obtain displacement change information of the target object; the telephoto camera is used to acquire images of the attached intersecting sinusoidal composite stripes and send the acquired images to the computer for analysis; the computer performs post-processing on the intersecting sinusoidal composite stripe images to obtain the relative displacement information of the object to be measured.

[0034] In this embodiment, image processing is implemented by a computer. The computer is pre-installed with a stripe information processing program, and the camera is connected to the computer via a data cable, enabling real-time monitoring of the unique information of the stripes.

[0035] This embodiment provides a design method for a cross-sine target based on the aforementioned device. The target is characterized by consisting of two sets of sinusoidal stripes with identical amplitude frequencies and a relative angle of 90°. The edge region of the artificial target comprises four gray blocks and a single sinusoidal stripe pattern, while the center features an oblique sinusoidal stripe pattern formed by the intersection of the edge sinusoidal stripes. After decoding using the measurement method proposed in this invention, the proposed stripe-based artificial target exhibits better noise resistance and accuracy compared to similar artificial targets in long-distance displacement measurement; it can achieve micrometer-level displacement measurement under long-distance monitoring. Furthermore, the stripe-based artificial target proposed in this invention has good scalability. Depending on different application scenarios, the target can achieve micrometer-level displacement measurement under long-distance monitoring through various signal processing methods such as filtering and windowing.

[0036] This embodiment also provides a long-distance displacement measurement method based on the above-mentioned device and cross sinusoidal composite stripes, including the following steps:

[0037] Step S1: Attach the cross-sine composite stripe pattern to the object to be measured; place the telephoto camera in a suitable position, and set appropriate parameters such as focal length and aperture to ensure clear image capture. A schematic diagram of the measuring device structure is shown below. Figure 1 A three-dimensional coordinate system is established at the center point of the cross sinusoidal composite stripe pattern. The camera optical axis, which is perpendicular to the camera imaging plane, is taken as the Z-axis. The horizontal direction, which is perpendicular to the imaging optical axis, is defined as the X-axis, and the vertical direction, which is perpendicular to the imaging optical axis, is defined as the Y-axis.

[0038] Step S2: As the object under test undergoes in-plane displacement, the relative position of the stripe pattern attached to the object to the imaging sensor of the acquisition camera also changes. Keeping the camera position constant, the camera acquires a stripe image containing positional information.

[0039] Step S3: The collected stripe signal is transmitted to the computer, which preprocesses the stripe signal and extracts the displacement information of the object under test.

[0040] In this embodiment, step S3 specifically includes the following steps:

[0041] Step S31: Preprocess the acquired stripe image, including methods such as noise reduction, sharpening, and contrast enhancement, to facilitate subsequent processing;

[0042] Step S32: Select the single sinusoidal fringe region around the striped pattern as the reference row and calculate the Pearson correlation coefficient curve. After performing a Fourier transform on the Pearson curve, the correlation coefficient curve is upsampled by zero-padding in the frequency domain, and then the time domain information is returned by inverse Fourier transform to increase the pixel resolution of the shift and improve the pixel resolution. The formula for calculating the Pearson correlation coefficient is as follows:

[0043]

[0044] Where ρ X,Y σ is the correlation coefficient PCC; coν(X,Y) is the covariance of X and Y. X σ is the standard deviation of X. Y X is the standard deviation of Y. i and Y i It is the value of the i-th data point. and These are the means of X and Y, respectively.

[0045] Step S33: By cross-correlating the correlation coefficient curves before and after displacement, the relative displacement information of the object under test in the acquired images before and after displacement is obtained through the maximum cross-correlation value index. The Pearson correlation coefficient curve before displacement is selected, and autocorrelation is performed to obtain the maximum correlation value index; the Pearson correlation coefficient curves of the images before and after displacement are selected, and cross-correlation is performed to obtain the maximum correlation value index; the two are subtracted to obtain the displacement distance of the object under test on the imaging sensor in pixels. After correcting this displacement value using the energy centroid method, the true displacement distance of the object under test is obtained through a scaling factor. The cross-correlation coefficient calculation formula is as follows:

[0046]

[0047] Where x[n] is the value of the reference frame signal x at time point n. y[n+k] is the value of the displacement frame signal y at time point n+k. k is the displacement value, which can be positive, negative, or zero. R xy[k] It is the cross-correlation value of signals x and y under time delay k.

[0048] Figure 2 and Figure 3 This is a schematic diagram illustrating the system principle of the present invention. Wherein, Figure 2 (a) is a schematic diagram of the cross sinusoidal composite stripes used in this method; Figure 2 (b) is the gray value of a single-sided stripe, which is a combination curve of a sine curve in the middle of two straight lines. Figure 2 (c) is a schematic diagram of the Pearson correlation coefficient calculation curve of the striped image line by line along the Y-axis; Figure 3 It is a cross-correlation curve of the coefficients before and after displacement.

[0049] Figure 3 and Figure 4 These are the experimental results obtained through displacement measurement experiments in this embodiment. In the experiment, an industrial camera (PointGray, maximum frame rate 165 frames / s) with a resolution of 1920×1200 pixels was used to acquire images of the composite stripes, and a telephoto lens (JARAY, focal length 420mm~800mm F8.3) was used for imaging. The displacement platform used was THORLAB: MTS25 / M-Z8. The composite stripe image was attached to a one-dimensional displacement platform, and the known displacement was generated by the displacement platform to simulate the movement of the measured object. To verify the performance of this measurement system in displacement measurement at different distances, the displacement platform with the stripe pattern was placed at distances of 20m and 50m from the imaging camera for experiments.

[0050] Before performing displacement measurements, the camera should be calibrated. After setting up the measurement system, the displacement platform is set to move continuously. The displacement platform is set to perform a continuous step-down motion, with single descent distances of 1mm, 0.1mm, 50μm, 25μm, and 10μm, for a total of 5 sedimentation measurements. A telephoto camera is used to capture the data during the motion, and the displacement results are shown in the figure. The experimental results show that when the test distance is 20m, the method presented in this paper has good measurement performance for step displacements of 1mm, 0.1mm, and 50μm. There is obvious noise when measuring the 10μm step displacement, but the displacement curve can still be clearly distinguished. When the test distance is 50m, the method presented in this paper can still measure a single 25μm displacement.

[0051] Figure 5 These are the experimental results obtained through vibration measurement experiments in this embodiment. The algorithm presented in this paper has vibration analysis capabilities and is suitable for vibration monitoring of various types of buildings. The displacement platform was replaced with a vibrator, outputting vibrations with an amplitude of 0.5 mm and frequencies of 1 Hz and 10 Hz, respectively. The system performance was verified by comparing it with an eddy current displacement sensor (ECDS). The experimental results show that the amplitude and frequency of the vibration generated by the vibrator were accurately recorded. Figure 6 These are the vibration curves and spectra at 1Hz and 10Hz at a measurement distance of 20m. Figure 7 These are the vibration curves and spectra at 1Hz and 10Hz at a measurement distance of 50m.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A long-distance displacement measurement device based on intersecting sinusoidal composite fringes, characterized in that, The system includes a cross-sine composite stripe target, a telephoto camera, and a computer. The cross-sine composite stripe target is attached to the object to be monitored to acquire information on the displacement changes of the target object. The telephoto camera is used to acquire images of the attached cross-sine composite stripes and send the acquired images to the computer for analysis. The computer performs post-processing on the cross-sine composite stripe images to obtain the relative displacement information of the object to be monitored. The system also includes a cross-sine composite stripe target, which consists of two sets of sinusoidal stripes with completely identical amplitude frequencies and a relative angle of 90°. The edge area of ​​the cross-sine composite stripe target consists of four gray blocks and a single sinusoidal stripe pattern, while the center consists of an oblique sinusoidal stripe pattern formed by the intersection of the edge sinusoidal stripes.

2. A method for measuring long-distance displacement based on intersecting sinusoidal composite fringes, based on the long-distance displacement measuring device based on intersecting sinusoidal composite fringes as described in claim 1, characterized in that, Includes the following steps: Step S1: A novel artificial target based on intersecting sinusoidal composite stripes was designed to collect the position information of the object being measured. Step S2: Attach the cross sinusoidal composite stripe pattern to the object to be tested; place the telephoto camera in a suitable position, set appropriate focal length and aperture parameters, and ensure that the camera image is clear; Step S3: When the object under test is displaced in the plane, the relative position of the stripe pattern attached to the object under test to the imaging sensor of the acquisition camera also changes; keep the camera position unchanged, and acquire a stripe image with position information through the camera. Step S4: The collected stripe signal is transmitted to the computer, which preprocesses the stripe signal and extracts the displacement information of the object under test.

3. The long-distance displacement measurement method based on intersecting sinusoidal composite fringes according to claim 2, characterized in that, Step S4 specifically includes the following steps: Step S41: Preprocess the acquired stripe image, including noise reduction, sharpening, and contrast enhancement methods, to facilitate subsequent processing; Step S42: Taking any row of the single sinusoidal region of the stripes as a reference, calculate the Pearson correlation coefficient for each of the other rows to obtain the Pearson correlation coefficient curves of the stripes on the X and Y axes; Step S43: By cross-correlating the correlation coefficient curves before and after displacement, the relative displacement information of the object under test in the acquired images before and after displacement is obtained by indexing the maximum cross-correlation value.

4. The long-distance displacement measurement method based on intersecting sinusoidal composite fringes according to claim 3, characterized in that, Step S42 specifically involves: The single sinusoidal fringe region around the striped pattern is selected as the reference row. After calculating the Pearson correlation coefficient and removing the curve, a Fourier transform is performed on the curve. Then, the correlation coefficient curve is upsampled by zero-padding in the frequency domain, and finally, the time domain information is returned by inverse Fourier transform to increase the pixel resolution of the displacement. The formula for calculating the Pearson correlation coefficient is as follows: ,in It is the correlation coefficient PCC; yes and covariance; yes Standard deviation; yes Standard deviation; and It is The value of each data point; and They are and The mean.

5. The long-distance displacement measurement method based on intersecting sinusoidal composite fringes according to claim 3, characterized in that, Step S43 specifically involves: By cross-correlation of the correlation coefficient curves before and after displacement, the relative displacement information of the object under test in the acquired images before and after displacement is obtained through the maximum cross-correlation value index; the Pearson correlation coefficient curve before displacement is selected, autocorrelation is performed, and the maximum correlation value index is obtained; the Pearson correlation coefficient curves of the images before and after displacement are selected, cross-correlation is performed, and the maximum correlation value index is obtained. Subtracting the two values ​​yields the displacement distance of the object under test on the imaging sensor, expressed in pixels. After correcting this displacement value using the energy centroid method, the true displacement distance of the object under test is obtained using a scaling factor. The cross-correlation coefficient is calculated as follows: ,in It is the value of the reference frame signal x at time point n; It is the displacement frame signal y at time point The value of ; k is the displacement value, which can be positive, negative, or zero; R xy[k] It is the cross-correlation value of signals x and y under time delay k.

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