Fracture three-way measurement research method and system based on multi-coordinate system fusion

Abstract

The invention provides a crack three-way measurement research method and system based on multi-coordinate system fusion, and relates to the technical field of earth and rockfill dam monitoring. The method comprises the following steps: shooting concentric circle target images at multiple angles through a monocular camera, extracting feature points through preprocessing, and correcting projection distortion of non-parallel shooting; establishing a mapping relation between pixels and a three-dimensional coordinate system, and optimizing camera pose parameters; reconstructing a three-dimensional coordinate point cloud based on the multi-view data, screening outliers and fitting plane optimization data; and registering the three-dimensional point cloud to a world coordinate system through a coordinate system conversion algorithm, and comparing two measurement results to calculate the three-way displacement of the crack. According to the method, concentric circle target constraint feature precision is utilized, projection correction and plane fitting are combined to compensate monocular vision depth deficiency, high-precision displacement detection is achieved, and engineering monitoring requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of earth-rock dam monitoring, and particularly to a research method and system for three-way measurement of cracks based on the fusion of multiple coordinate systems. Background Art

[0002] As a main evaluation index of structural safety, information collection of cracks often adopts manual collection, and it is difficult to guarantee the accuracy of measurement results, and the time and labor costs are high. With the development of computer technology, three-dimensional (3D) measurement technology has begun to be applied to the long-term detection of cracks to judge the development status of cracks. Zhang Haoyu et al. established a three-dimensional model through deep learning to achieve three-way measurement of cracks; to improve efficiency, Xue Zhilin proposed a stereo vision three-dimensional displacement measurement method based on a dual coordinate system, and Hu Henan integrated motion features and depth information by extracting time-domain features of long and short ranges, and used deep learning to obtain the three-way displacement of an object; Huang Youchao used a binocular camera to establish an equivalent model and constructed a crack measurement system. These methods either have requirements for the sample size and long operation time, or have requirements for equipment and shooting postures, and cannot be well adapted to on-site crack measurement. Summary of the Invention

[0003] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a research method and system for three-way measurement of cracks based on the fusion of multiple coordinate systems. By specifying the world coordinate system, it aims to establish the connection between it and the pixel coordinate system, solve the relative pose between cameras, and perform three-dimensional reconstruction to avoid the problem of missing depth information in monocular vision. By establishing an equivalent model of three-way displacement of cracks, observe the change of the sub-board coordinates in the world coordinate system to achieve three-way displacement measurement of cracks.

[0004] To achieve the above object, the present invention provides the following solutions: A research method for three-way measurement of cracks based on the fusion of multiple coordinate systems, comprising: Taking two target layout photos from different positions by the same camera to obtain initial images; Performing binarization and filtering processing on the initial images to obtain preprocessed images; Extracting concentric circle edge information from the preprocessed images, and obtaining an initial feature point set through a density-based spatial clustering algorithm; Performing distortion correction on the initial feature point set based on the cross-ratio invariance to eliminate the elliptical projection error caused by non-parallel shooting, and obtaining a corrected feature point set; Preliminarily calculating the rotation matrix and translation vector of the two shootings, and optimizing the reprojection error to below the threshold of 0.1 in combination with the Levenberg-Marquardt method, and outputting a projection matrix; Based on the internal parameter matrix of the camera calibration and the projection matrix, use the least squares method to solve the initial three-dimensional coordinate point set in the camera coordinate system; Pre-screen the three-dimensional point cloud data in the initial three-dimensional coordinate point set through the Random Sample Consensus (RANSAC) algorithm, combine the principal component analysis to eliminate outliers, iteratively fit the plane and project it to generate an optimized three-dimensional coordinate point set; Adopt the Iterative Closest Point (ICP) algorithm optimized based on the mean square error, perform point cloud registration according to the optimized three-dimensional coordinate point set, convert the three-dimensional coordinates in the camera coordinate system to the world coordinate system, and calculate the three-way displacement change of the crack through the difference between the two measurement results.

[0005] Preferably, the target is a preset concentric circle target, and the center point set of the concentric circles is used as the feature point set.

[0006] Preferably, initially calculate the rotation matrix and translation vector of the two shootings, and combine the Levenberg-Marquardt method to optimize the reprojection error to less than 0.1, and output the projection matrix, including: Establish the mapping relationship between the world coordinate system and the pixel coordinate system through the EPnP algorithm, and solve the rotation matrix R l 、 R r and translation vector t l 、 t r ; Substitute R l 、 R r 、 t l 、 t r into the formula to correlate the left and right camera coordinate systems, and obtain the relative rotation matrix and translation vector R and translation vector t between the left and right camera coordinate systems; Iteratively optimize R and t through the Levenberg-Marquardt method until the reprojection error is less than 0.1, and output the projection matrix M.

[0007] Preferably, the expression of the mapping relationship between the world coordinate system and the pixel coordinate system is: where 、 and are the , , coordinates of point D( XAxis, Y axis and Z coordinate values of the axis, u and v are respectively the u , v ) of point P in the pixel coordinate system X axis and Y axis coordinate values, Z c is the coordinate value of the Z axis in the camera coordinate system, K is the internal parameter matrix of the camera, P is the feature point set.

[0008] Preferably, the step of iteratively fitting the plane includes: Randomly select three points using the RANSAC algorithm to establish an initial plane model, and screen the inliers within a distance threshold of 0.5; Re-fit the plane parameters based on the least squares method and complete the preliminary fitting after ten iterations; Eliminate the outliers with excessive adjacent point distances through the PCA algorithm, and re-project them onto the fitted plane until the distance threshold of all points in the plane is less than 0.3 or the iteration diverges.

[0009] Preferably, use the iterative closest point algorithm optimized based on the mean square error. Perform point cloud registration according to the optimized three-dimensional coordinate point set, convert the three-dimensional coordinates in the camera coordinate system to the world coordinate system, and calculate the three-way displacement change of the crack through the difference between the two measurement results, including: Based on the generated optimized three-dimensional coordinate point set , calculate the centroids and of the three-dimensional points in the camera coordinate system and the target world coordinate system respectively; According to the centroids and , perform de-centering processing on each point to obtain the de-centered coordinates and ; where, and , and are respectively the actually observed two-dimensional image points; Construct the covariance matrix , and perform singular value decomposition on to obtain the decomposition result; where, ; The formula for the decomposition result is: ; U and V are respectively the left singular vector and the right singular vector; Calculate the optimal rotation matrix R’Translation vector t’ ; Among them, the calculation formula for the optimal rotation matrix R' is: Translation vector ; Using the optimal rotation matrix R’ Translation vector t’ , the three-dimensional coordinate point set in the camera coordinate system obtained by solving is transformed into the world coordinate system to obtain the transformed coordinate point set ; Calculate the optimized three-dimensional coordinate point set and the transformed coordinate point set The root mean square error ; ; Among them, , is the distance between the i th point in the source point cloud and the corresponding point in the target point cloud, n is the total number of matching point pairs; Iterate the optimal rotation matrix R’ and translation vector t’ , until the RMSE is less than the threshold 0.1 or the iteration diverges, and finally output the crack displacement change in the world coordinate system.

[0010] Preferably, the calculation formula for the crack displacement change is: Among them, is the crack displacement change, , and are respectively the X axis, Y axis and Z axis coordinate values of the crack displacement change, , and are respectively the three-dimensional coordinates of the crack after change; , and are respectively the three-dimensional coordinates of the crack before change.

[0011] A crack three-way measurement research system based on multi-coordinate system fusion includes: An image input unit for taking two target layout photos from different positions through the same camera to obtain an initial image; An image preprocessing unit for performing binarization and filtering processing on the initial image to obtain a preprocessed image A density clustering unit, configured to extract concentric circle edge information from the preprocessed image and obtain an initial set of feature points through a density-based spatial clustering algorithm; A center correction unit, configured to perform distortion correction on the initial set of feature points based on cross-ratio invariance to eliminate the elliptical projection error caused by non-parallel shooting, and obtain a corrected set of feature points; A pose solving unit, configured to preliminarily calculate the rotation matrix and translation vector for two shootings, and optimize the reprojection error to below the threshold of 0.1 in combination with the Levenberg-Marquardt method, and output a projection matrix; A coordinate solving unit, configured to solve the initial three-dimensional coordinate point set in the camera coordinate system by using the least squares method based on the internal parameter matrix of camera calibration and the projection matrix; A plane fitting unit, configured to pre-screen the three-dimensional point cloud data in the initial three-dimensional coordinate point set through the random sample consensus algorithm, eliminate outliers in combination with principal component analysis, iteratively fit a plane and project it to generate an optimized three-dimensional coordinate point set; A coordinate system conversion unit, configured to perform point cloud registration according to the optimized three-dimensional coordinate point set by using the iterative closest point algorithm optimized based on the mean square error, convert the three-dimensional coordinates in the camera coordinate system to the world coordinate system, and calculate the three-way displacement change of the crack through the difference between the two measurement results.

[0012] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention provides a method and system for three-dimensional measurement of cracks based on fusion of multiple coordinate systems. The method comprises: taking two target arrangement photos from different positions by the same camera to obtain an initial image; binarizing and filtering the initial image to obtain a preprocessed image; extracting the concentric circle edge information in the preprocessed image, and obtaining an initial feature point set by a density-based spatial clustering algorithm; performing distortion correction on the initial feature point set based on cross ratio invariance to eliminate the elliptical projection error caused by non-parallel shooting, and obtaining a corrected feature point set; preliminarily calculating the rotation matrix and translation vector of the two shots, and combining the Levenberg-Marquardt method Optimize the reprojection error to below the threshold of 0.1, and output the projection matrix; based on the camera calibration internal parameter matrix and the projection matrix, use the least squares method to solve the initial three-dimensional coordinate point set in the camera coordinate system; pre-screen the three-dimensional point cloud data in the initial three-dimensional coordinate point set by the random sampling consensus algorithm, remove outliers in combination with principal component analysis, iteratively fit the plane and project, and generate an optimized three-dimensional coordinate point set; use the iterative nearest point algorithm based on mean square error optimization, perform point cloud registration according to the optimized three-dimensional coordinate point set, convert the three-dimensional coordinates in the camera coordinate system to the world coordinate system, and calculate the three-dimensional displacement change of the crack by the difference between the two measurement results. The present invention aims to establish a connection between the world coordinate system and the pixel coordinate system by specifying the world coordinate system, solve the relative position between the cameras, and perform three-dimensional reconstruction to avoid the problem of missing depth information in monocular vision. In the way of establishing an equivalent model of the three-dimensional displacement of the crack, the change of the coordinates of the sub-plate in the world coordinate system is observed to achieve the three-dimensional displacement measurement of the crack. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] 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 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 labor.

[0014] Figure 1 A flow chart of a method provided by an embodiment of the present invention; Figure 2 An algorithm flow chart provided for an embodiment of the present invention; Figure 3 A plane fitting result diagram provided by an embodiment of the present invention; Figure 4 A target arrangement diagram provided for an embodiment of the present invention; Figure 5 Iterative flow chart for solving posture problems provided by an embodiment of the present invention; Figure 6The triaxial displacement change value in the X-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 7 The triaxial error value in the X-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 8 The X-axis movement stability in the X-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 9 The triaxial displacement change value in the Y-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 10 The triaxial error value in the Y-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 11 The Y-axis movement stability in the Y-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 12 The triaxial displacement change value in the Z-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 13 The triaxial error value in the Z-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 14 The Z-axis movement stability in the Z-axis unidirectional movement test result provided by the embodiment of the present invention; Figure 15 The triaxial displacement change value in the test result when moving the X, Y, and Z axes simultaneously provided by the embodiment of the present invention; Figure 16 The triaxial error value in the test result when moving the X, Y, and Z axes simultaneously provided by the embodiment of the present invention. Detailed implementation manners

[0015] 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.

[0016] The purpose of the present invention is to provide a research method and system for three-way crack measurement based on multi-coordinate system fusion. By specifying the world coordinate system, it aims to establish the connection between it and the pixel coordinate system, solve the relative pose between cameras, and perform three-dimensional reconstruction to avoid the problem of missing depth information in monocular vision. By establishing an equivalent model of three-way crack displacement, observe the change of the secondary plate coordinates in the world coordinate system to achieve three-way crack displacement measurement.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a three-dimensional crack measurement research method based on multi-coordinate system fusion, including: Step 100: Take two target arrangement photos from different positions using the same camera to obtain an initial image; Step 200: binarizing and filtering the initial image to obtain a preprocessed image; Step 300: extracting the edge information of concentric circles in the preprocessed image, and obtaining an initial feature point set by a density-based spatial clustering algorithm; Step 400: performing distortion correction on the initial feature point set based on the cross ratio invariance to eliminate the elliptical projection error caused by non-parallel shooting, and obtaining a corrected feature point set; Step 500: Preliminarily calculate the rotation matrix and translation vector of the two shots, and optimize the reprojection error to below a threshold of 0.1 by combining the Levenberg-Marquardt method, and output the projection matrix; Step 600: Based on the camera calibration intrinsic parameter matrix and projection matrix, the initial three-dimensional coordinate point set in the camera coordinate system is solved by the least square method; Step 700: pre-screening the 3D point cloud data in the initial 3D coordinate point set by using a random sampling consensus algorithm, removing outliers in combination with principal component analysis, iteratively fitting the plane and projecting it, and generating an optimized 3D coordinate point set; Step 800: Using an iterative closest point algorithm based on mean square error optimization, perform point cloud registration according to the optimized three-dimensional coordinate point set, transform the three-dimensional coordinates in the camera coordinate system into the world coordinate system, and calculate the three-dimensional displacement change of the crack by the difference between the two measurement results.

[0019] Specifically, Figure 2 As shown, the present invention provides a three-dimensional crack measurement research method based on multi-coordinate system fusion, comprising the following steps: 1) Image input: Two target layout photos are taken from different positions using the same camera; 2) Image preprocessing: first perform image binarization, image filtering and other preprocessing; 3) Density clustering: The canny operator is used to extract the edge information of the concentric circles for density clustering, and the DBSCAN density clustering is used to find the center of the concentric circles to form the initial feature point set P'; 4) Center correction: Since on-site shooting is often not parallel shooting, the circle on the object plane will appear as an ellipse when projected onto the image plane, resulting in an error between the center of the circle and the actual projection point, that is, the eccentricity error. To eliminate the eccentricity error, in this embodiment, the cross-ratio invariance is used to correct the distortion of the center of the circle. After sorting, the feature point set can be formed. ; 5) Solving the relative position of the computer: First, the rotation matrix R and the translation vector t required for coordinate system conversion are initially obtained through the EPnP algorithm, and pi, tl, Rr, and tr for the two shootings are obtained. Then, the Levenberg-Marquardt method is used to optimize the rotation matrix R and the displacement t vector of the camera until the reprojection error value is less than the given threshold of 0.1, and the iteration stops, and the projection matrix M is output.

[0020] 6) Coordinate solving: According to the obtained feature point set , and the internal parameter matrix K obtained by camera calibration and the projection matrix M obtained in step 5, the three-dimensional coordinate point set in the camera coordinate system is solved using the least squares method.

[0021] 7) Plane fitting: Since the pose derivation is based on the ideal situation, after three-dimensional reconstruction, the obtained coordinate point set must be optimized to eliminate the influence of interference items such as noise. Considering that the target plane is basically horizontal, the theoretically solved three-dimensional point set should also be located on the same plane. Therefore, plane fitting is used to compensate for image distortion, and is projected onto the fitting plane to form a new data point set , and then is transformed into the world coordinate system through the ICP algorithm and iterative optimization based on the mean square error, and finally the optimized three-dimensional coordinate point set is obtained. The plane fitting result is as shown in Figure 3 .

[0022] 8) Coordinate system conversion: The ICP algorithm based on iterative optimization of the mean square error is used for point cloud registration to solve the pose, and the conversion relationship between the coordinates in the camera coordinate system and the coordinates in the world coordinate system is obtained. And the coordinates in the world coordinate system are obtained. According to the right target coordinates measured before and after, are subtracted, and the three-dimensional displacement change of the crack can be known.

[0023] Among them, is the displacement change amount of the crack, , and are respectively the X axis, Y axis and Z of the displacement change amount of the crack.The coordinate values ​​of the axis, , and They are the three-dimensional coordinates of the crack after the change; , and are the three-dimensional coordinates of the crack before the change.

[0024] Further, in step 1), the target layout photo is taken. In this embodiment, a concentric circle target is designed independently, and the concentric circle center point set is used as the feature point set. On the one hand, the feature point extraction accuracy can be improved by the constraints of the concentric circle. On the other hand, the error caused by the feature point matching error can be eliminated by sorting. The target layout diagram is shown in FIG. Figure 4 .

[0025] Furthermore, in step 5), when the camera is imaging, given an arbitrary point P in space, the two views in the left and right camera coordinate systems can be used By associating them, we can get the following relationship: in: , Respectively represent the lower points of the left and right camera coordinate systems location; and or( and ) represents the distance from the camera to the space point on the left camera (right camera) The rotation matrix and translation vector of and That is, the relative rotation matrix and translation vector between the left and right camera coordinate systems; , , , Substituting it into the equation can solve the pose relationship and thus form the projection matrix .

[0026] Further, in step 6), the world coordinate system point D( , , ) and the coordinate transformation relationship of point P(u,v) in the pixel coordinate system is as follows: Where K is the intrinsic parameter matrix of the camera, and Zc is the coordinate of the Z axis in the camera coordinate system.

[0027] Further, in step 6), to prevent the plane fitting from falling into a local optimum, in this embodiment, before the plane fitting, the RANSAC algorithm is used for pre-fitting to screen the point cloud data. First, a plane is established by randomly selecting three points in the point cloud data, and the distances from all points in the point cloud set to the plane model are calculated. A threshold of 0.5 is used to judge the boundary. When the number of inlier points meets the requirements, all points on the plane (inlier points) are used to recalculate the plane parameters based on the least squares principle to complete the preliminary fitting by iterating ten times in this way.

[0028] Further, after the RANSAC pre-fitting in step 7), referring to the true distance set by the target, after removing the points with relatively large distances from adjacent points in the three-dimensional point cloud using the PCA algorithm, the plane fitting is performed again. After the fitting is completed, the originally removed points are re-projected onto the fitting plane. Iterate until the distances from all points in the plane to the plane are less than the set threshold of 0.3 or the iteration diverges. After the fitting is completed, a new coordinate point set can be obtained .

[0029] Further, in step 8), to solve the pose, the specific steps are as follows: 1) Calculate the centroids of the 3D points in two different coordinate systems respectively and ; 2) Calculate the centroid-removed coordinates of each point , ; 3) Define the matrix , and then perform the SVD decomposition on the matrix ; 4) When W is full rank, calculate and obtain , ; 5) Calculate the root mean square error: where is the distance between the i-th point in the source point cloud and the corresponding point in the target point cloud, and n is the total number of matching point pairs.

[0030] 6) Set the root mean square threshold to 0.1, and iterate the transformation matrix until the threshold is less than 0.1 or the iteration diverges and stop the iteration. The iteration flowchart is as shown in Figure 5 .

[0031] Further, compared with the existing solutions, the crack three-way measurement research method based on multi-coordinate system fusion proposed by the present invention has the characteristics of high detection accuracy and wider applicability.

[0032] As an alternative embodiment, this embodiment uses a camera to take multi-angle shots to simulate the shooting process of a binocular camera. After obtaining two pictures, they are input into a computer terminal to calculate the required test parameters, and finally the test results are obtained. The camera model is Nikon D3100, with 14.2 million pixels, effective pixels of 4608×3702, a sensor size of 23.1×15.4mm, an image processor EXPEED 2, and a focal length of 18 - 55mm To verify the accuracy and stability of the algorithm, based on a fixed sliding table, this embodiment designs a three-way displacement accuracy and stability detection device, which consists of a horizontal rail, a longitudinal rail, a vertical rail, and a dial indicator. The left and right test targets are respectively fixed on the mobile end and the fixed end, and an electronic dial indicator is arranged in the sliding horizontal direction. The target is moved using the rail to simulate the three-way changes of the crack. The details of the test equipment are as Figure 6 shown

[0033] During the test, the camera is used to take multi-angle shots of the test equipment, and try to place the target at the center during shooting. In this embodiment, by moving the target on the mobile end, the relative displacement on both sides of the crack is simulated, and the reading of the electronic dial indicator is used as the reference value of the displacement. During the test process, the displacement amount each time is controlled to be 1mm, and it is moved a total of ten times. After each movement, the image is collected for data processing. The difference between the three-dimensional coordinates obtained and the coordinates at the start of shooting is the change situation of the crack. The test results are shown in Figures 6 to 16 . From Figures 6 to 8 it can be seen that when moving the X-axis unidirectionally, the error values between the measured displacement and the actual displacement are all controlled within ±0.4mm. At the same time, the X-direction error range is within ±0.4mm, indicating that the accuracy and stability of the algorithm are reliable, and the error volatility does not increase with the increase of the reference displacement. From Figures 9 to 11 it can be seen that when moving the Y-axis unidirectionally, the error values between the measured displacement and the actual displacement are all controlled within ±0.4mm. At the same time, the Y-direction error range is within ±0.4mm, indicating that the accuracy and stability of the algorithm are reliable, and the error volatility does not increase with the increase of the reference displacement. From Figures 12 to 14 it can be seen that when moving the Z-axis unidirectionally, the error values between the measured displacement and the actual displacement are all controlled within ±0.4mm. At the same time, the Z-direction error range is within ±0.4mm, indicating that the accuracy and stability of the algorithm are reliable, and the error volatility does not increase with the increase of the reference displacement. Since in actual situations, cracks will not only have unidirectional displacement, the three-axis movement test is also necessary Figure 15 and Figure 16 are the test results in the case of simultaneously moving the X, Y, and Z axes. From Figures 15 to 16It can be seen that when moving in three axes, the error values between the measured displacement and the actual displacement are all controlled within ±0.4 mm. At the same time, the three-direction error range is within ±0.4 mm, indicating that the accuracy and stability of the algorithm are reliable, and the error change ranges of each axis are within the same level, indicating that the measurement effect of the algorithm is good.

[0034] Analyzing the above test results, it can be seen that in the single-axis movement test, the displacement distance of the moving axis measured by the algorithm shows a linear change trend with the actual moving distance, and the absolute error of the fixed axis remains at a level similar to that of the measurement axis, fluctuating within ±0.4 mm, indicating that the algorithm can accurately judge the changes of the moving axis. In the three-axis movement test, the error still remains within ±0.4 mm.

[0035] The algorithm of this embodiment is analyzed as follows: Table 1 Algorithm Resolution Analysis As can be seen from Table 1, the fluctuation ranges of the three-axis measurements of the algorithm are all within 0.6 mm, proving that the resolution of the algorithm is within the controllable range.

[0036] Table 2 Algorithm Accuracy Analysis As can be seen from Table 2, during the test, the absolute errors of the three axes are all within ±0.4 mm, meeting the requirements for crack measurement in the "Technical Code for Safety Monitoring of Earth-Rock Dams" (±0.5 mm).

[0037] Table 3 Algorithm Repeatability Analysis As can be seen from Table 3, the maximum deviation of the algorithm is 0.4 mm that appears on the x-axis, proving that the repeatability of this embodiment is reliable.

[0038] Corresponding to the above method, this embodiment also provides a crack three-direction measurement research system based on the fusion of multiple coordinate systems, including: An image input unit, configured to take two target arrangement photos from different positions through the same camera to obtain an initial image; An image preprocessing unit, configured to perform binaryzation and filtering processing on the initial image to obtain a preprocessed image A density clustering unit, configured to extract the concentric circle edge information in the preprocessed image and obtain an initial feature point set through a density-based spatial clustering algorithm; A center correction unit, configured to perform distortion correction on the initial feature point set based on the cross-ratio invariance to eliminate the elliptical projection error caused by non-parallel shooting and obtain a corrected feature point set; The pose solving unit is used to preliminarily calculate the rotation matrix and translation vector of the two shots, and optimize the reprojection error to below the threshold of 0.1 by combining the Levenberg-Marquardt method, and output the projection matrix; A coordinate solving unit, used to solve an initial three-dimensional coordinate point set in a camera coordinate system by using a least square method based on an intrinsic parameter matrix of camera calibration and the projection matrix; A plane fitting unit is used to pre-screen the three-dimensional point cloud data in the initial three-dimensional coordinate point set by using a random sampling consensus algorithm, remove outliers in combination with principal component analysis, iteratively fit the plane and project it, and generate an optimized three-dimensional coordinate point set; The coordinate system conversion unit is used to adopt an iterative closest point algorithm based on mean square error optimization, perform point cloud registration according to the optimized three-dimensional coordinate point set, convert the three-dimensional coordinates in the camera coordinate system to the world coordinate system, and calculate the three-dimensional displacement change of the crack by the difference between two measurement results.

[0039] The beneficial effects of the present invention are as follows: (1) First, by specifying the world coordinate system, the EPnP algorithm establishes the connection between the world coordinate system and the pixel coordinate system, solves the relative position between the cameras, and performs three-dimensional reconstruction, thereby avoiding the problem of missing depth information in monocular vision. This solves the problem that traditional crack detection methods have high requirements for equipment and shooting posture and cannot be well adapted to on-site crack measurement.

[0040] (2) Secondly, the present invention independently designs a concentric circle target and uses the concentric circle center point set as the feature point set. On the one hand, the feature point extraction accuracy can be improved by the constraints of the concentric circle itself, and on the other hand, the error caused by the feature point matching error can be eliminated by sorting.

[0041] (3) The present invention designs a concentric dot matrix target and establishes a crack equivalent displacement model, which converts the three-dimensional change measurement problem of the crack into the relative change measurement problem of the target main plate and the sub-plate. The feature point set is obtained by camera shooting, and the sub-plate point set is transferred to the world coordinate system through three-dimensional reconstruction and coordinate system conversion. The three-dimensional change of the crack can be obtained by observing the change of the sub-plate coordinates. This solves the problem of lack of depth information in monocular vision measurement, and the problem that it cannot be used to quickly and accurately obtain the three-dimensional displacement of the crack.

[0042] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0043] In this text, specific examples are used to illustrate the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A three-dimensional crack measurement research method based on multi-coordinate system fusion, characterized in that: include: Take two target arrangement photos from different positions with the same camera to obtain an initial image; Binarizing and filtering the initial image to obtain a preprocessed image; Extracting the concentric circle edge information in the preprocessed image, and obtaining an initial feature point set by a density-based spatial clustering algorithm; Based on the cross ratio invariance, the initial feature point set is subjected to distortion correction to eliminate the elliptical projection error caused by non-parallel shooting, thereby obtaining a corrected feature point set; The rotation matrix and translation vector of the two shots are preliminarily calculated, and the reprojection error is optimized to below the threshold of 0.1 by combining the Levenberg-Marquardt method, and the projection matrix is ​​output; Based on the camera calibration intrinsic parameter matrix and the projection matrix, the least square method is used to solve the initial three-dimensional coordinate point set in the camera coordinate system; Pre-screening the three-dimensional point cloud data in the initial three-dimensional coordinate point set by using a random sampling consensus algorithm, removing outliers in combination with principal component analysis, iteratively fitting the plane and projecting it, and generating an optimized three-dimensional coordinate point set; An iterative closest point algorithm based on mean square error optimization is used to perform point cloud registration according to the optimized three-dimensional coordinate point set, the three-dimensional coordinates in the camera coordinate system are converted to the world coordinate system, and the three-dimensional displacement change of the crack is calculated by the difference between the two measurement results.

2. The method for three-dimensional crack measurement based on multi-coordinate system fusion according to claim 1 is characterized in that: The target is a preset concentric circle target, and the center point set of the concentric circles is used as the feature point set.

3. The method for three-dimensional crack measurement based on multi-coordinate system fusion according to claim 1 is characterized in that: The rotation matrix and translation vector of the two shots are preliminarily calculated, and the reprojection error is optimized to below the threshold of 0.1 by combining the Levenberg-Marquardt method, and the projection matrix is ​​output, including: The EPnP algorithm is used to establish the mapping relationship between the world coordinate system and the pixel coordinate system, and the rotation matrix in the left and right camera coordinate systems is solved. R l , R r and translation vectors t l , t r ; Will R l , R r , t l , t r Substitute into the formula Associate the left and right camera coordinate systems to obtain the relative rotation matrix and translation vector between the left and right camera coordinate systems R and translation vector t; The Levenberg-Marquardt method is used to iteratively optimize R and t until the reprojection error is less than 0.1, and the projection matrix M is output.

4. The method for three-dimensional measurement of cracks based on fusion of multiple coordinate systems according to claim 3 is characterized in that: The mapping relationship between the world coordinate system and the pixel coordinate system is expressed as: in, , and They are respectively the point D( , , )of X axis, Y Axis and Z The coordinate values ​​of the axis, u and v They are respectively the point P( u , v )of X Axis and Y The coordinate values ​​of the axis, Z c In the camera coordinate system Z The coordinate values ​​of the axis, K is the camera’s intrinsic parameter matrix, P is a set of feature points.

5. The method for three-dimensional crack measurement based on multi-coordinate system fusion according to claim 1 is characterized in that: The step of iteratively fitting the plane comprises: The RANSAC algorithm is used to randomly select three points to establish the initial plane model, and the in-place points within the distance threshold of 0.5 are selected; The plane parameters were refitted based on the least squares method, and the preliminary fitting was completed after ten iterations; The PCA algorithm is used to remove outliers whose distances to adjacent points exceed the limit, and then reproject them to the fitting plane until the distance threshold of all points in the plane is less than 0.3 or the iteration diverges.

6. The method for three-dimensional measurement of cracks based on fusion of multiple coordinate systems according to claim 1 is characterized in that: The iterative closest point algorithm based on mean square error optimization is used to perform point cloud registration according to the optimized three-dimensional coordinate point set, and the three-dimensional coordinates in the camera coordinate system are converted to the world coordinate system. The three-dimensional displacement change of the crack is calculated by the difference between the two measurement results, including: Based on the generated optimized three-dimensional coordinate point set , calculate the centroid of the three-dimensional point in the camera coordinate system and the target world coordinate system respectively and ; According to the centroid and , perform de-centroiding on each point to obtain the de-centroided coordinates and ;in, and , and are the actually observed two-dimensional image points and the obtained two-dimensional image points respectively; Constructing the covariance matrix , and Perform singular value decomposition to obtain the decomposition result; among them, ; The formula for the decomposition result is: ; U and V are the left singular vector and the right singular vector respectively; Calculate the optimal rotation matrix based on the decomposition result R’ and translation vectors t’ ; Among them, the calculation formula of the optimal rotation matrix R' is: and translation vectors ; Using the optimal rotation matrix R’ and translation vectors t’ , transform the three-dimensional coordinate point set in the camera coordinate system to the world coordinate system, and obtain the transformed coordinate point set ; Calculate the optimized three-dimensional coordinate point set With the transformed coordinate point set The root mean square error between ; ;in, , It is the first i The distance between a point and the corresponding point in the target point cloud, n is the total number of matching point pairs; Iterate the optimal rotation matrix R’ and translation vectors t’ , until the RMSE is less than the threshold value of 0.1 or the iteration diverges, and finally the crack displacement change in the world coordinate system is output.

7. The method for three-dimensional measurement of cracks based on fusion of multiple coordinate systems according to claim 6 is characterized in that: The calculation formula of the crack displacement change is: in, is the displacement variation of the crack, , and are the crack displacement changes respectively. X axis, Y Axis and Z The coordinate values ​​of the axis, , and They are the three-dimensional coordinates of the crack after the change; , and are the three-dimensional coordinates of the crack before the change.

8. A three-dimensional crack measurement research system based on multi-coordinate system fusion, characterized in that: include: An image input unit, used for taking two target arrangement photos from different positions by using the same camera to obtain an initial image; The image preprocessing unit is used to perform binarization and filtering on the initial image to obtain a preprocessed image. A density clustering unit, used to extract the edge information of concentric circles in the preprocessed image and obtain an initial feature point set by a density-based spatial clustering algorithm; A circle center correction unit, used for performing distortion correction on the initial feature point set based on cross ratio invariance to eliminate the ellipse projection error caused by non-parallel shooting, and obtain a corrected feature point set; The pose solving unit is used to preliminarily calculate the rotation matrix and translation vector of the two shots, and optimize the reprojection error to below the threshold of 0.1 by combining the Levenberg-Marquardt method, and output the projection matrix; A coordinate solving unit, used to solve an initial three-dimensional coordinate point set in a camera coordinate system by using a least square method based on an intrinsic parameter matrix of camera calibration and the projection matrix; A plane fitting unit is used to pre-screen the three-dimensional point cloud data in the initial three-dimensional coordinate point set by using a random sampling consensus algorithm, remove outliers in combination with principal component analysis, iteratively fit the plane and project it, and generate an optimized three-dimensional coordinate point set; The coordinate system conversion unit is used to adopt an iterative closest point algorithm based on mean square error optimization, perform point cloud registration according to the optimized three-dimensional coordinate point set, convert the three-dimensional coordinates in the camera coordinate system to the world coordinate system, and calculate the three-dimensional displacement change of the crack by the difference between two measurement results.

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