CN115511878A - Side slope earth surface displacement monitoring method, device, medium and equipment - Google Patents
Side slope earth surface displacement monitoring method, device, medium and equipment Download PDFInfo
- Publication number
- CN115511878A CN115511878A CN202211378045.3A CN202211378045A CN115511878A CN 115511878 A CN115511878 A CN 115511878A CN 202211378045 A CN202211378045 A CN 202211378045A CN 115511878 A CN115511878 A CN 115511878A
- Authority
- CN
- China
- Prior art keywords
- target
- slope
- targets
- coordinate
- camera
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30204—Marker
- G06T2207/30208—Marker matrix
Abstract
The invention provides a method, a device, a medium and equipment for monitoring the displacement of a side slope earth surface, comprising the following steps: collecting a plurality of image data of a target slope; determining a plurality of target targets and obtaining area codes and pixel coordinates corresponding to the target targets by analyzing the image data; acquiring space coordinates of four corner points of each target in a plurality of target targets and a mass center coordinate of each target; respectively aiming at each target in a plurality of target targets, calculating the diagonal vector of each target according to the space coordinate and the centroid coordinate of the target to obtain the direction vector of each target plane, wherein the direction vector has 3-direction freedom degrees; monitoring the target slope through the pixel coordinate, the space coordinate, the centroid coordinate and the direction vector to obtain a monitoring result; compared with the prior art, the target side slope is monitored by the aid of the degree of freedom in the directions of 3 displacement degrees of freedom and 3, and the precision of side slope monitoring is improved.
Description
Technical Field
The invention relates to the field of slope monitoring, in particular to a method, a device, a medium and equipment for monitoring slope surface displacement.
Background
The monitoring of the displacement of the earth surface of the side slope is a hot spot problem in the monitoring research of the side slope, the displacement of the earth surface of the side slope is monitored, the real-time change of the displacement of the earth surface of the side slope is mastered, the overrun condition is pre-warned, and the loss caused by the geological disaster of the side slope can be reduced or avoided to a certain extent.
Common side slope earth surface displacement monitoring methods include geodetic methods, GPS methods, machine vision methods and the like. The traditional monitoring method usually only pays attention to single monitoring index characteristics, has large workload and high monitoring cost, cannot reflect the whole condition of the side slope, and is difficult to apply to the measurement of large-scale mountains.
Most of the existing common machine vision monitoring is based on monocular vision, and accurate three-dimensional slope surface displacement information is difficult to provide. In addition, most of the current schemes are based on simple targets with single mark points, and have the defects of mismatching, weak anti-interference capability and the like in the field environment. In addition, the schemes only monitor 3 displacement degrees of freedom of the mark points, and are difficult to monitor 3 direction degrees of freedom, so that the slope ground surface displacement monitoring precision is not high.
Disclosure of Invention
The invention provides a method, a device, a medium and equipment for monitoring the displacement of a side slope earth surface, and aims to overcome the defect of low monitoring precision of the displacement of the side slope earth surface in the prior art.
In order to achieve the above object, the present invention provides a method for monitoring displacement of a side slope surface, comprising:
step 1, acquiring a plurality of image data of a target slope, wherein the plurality of image data are obtained by acquiring images of the target slope from different angles;
step 3, acquiring the space coordinates of four corner points of each target in the plurality of target targets and the mass center coordinates of each target;
and 5, monitoring the target slope through the pixel coordinate, the space coordinate, the centroid coordinate and the direction vector to obtain a monitoring result.
Further, step 2 comprises:
carrying out binarization processing on the image data according to an adaptive threshold segmentation algorithm and extracting the outline of the image data to obtain a plurality of suspected slope displacement monitoring targets;
processing the suspected slope displacement monitoring targets one by one, and analyzing the region codes of the suspected slope displacement monitoring targets;
judging whether the area code of the suspected slope displacement monitoring target is legal or not;
if the regional coding of the suspected slope displacement monitoring target is legal, determining the suspected slope displacement monitoring target as a target;
and performing sub-pixel optimization on four corner points of the target to obtain the region codes and the pixel coordinates corresponding to the target.
Further, step 3 comprises:
aiming at each target in a plurality of target targets, establishing a space coordinate relation equation through a conversion matrix and pixel coordinates according to a triangulation principle and solving to obtain space coordinates of four corner points of the target, wherein the conversion matrix is obtained by calculation according to a camera internal reference matrix and a camera external reference matrix;
and carrying out mean value calculation on the space coordinates of the four corner points of the target to obtain the mass center coordinates of the plurality of target targets.
Further, step 4 comprises:
respectively measuring coordinate values of the target targets under a coordinate system of the total station aiming at each target in the plurality of target targets;
respectively aiming at each target in the multiple target targets, calculating the mass center coordinates of the target under a total station coordinate system and a camera coordinate system;
respectively aiming at each target in a plurality of target targets, calculating two diagonal vectors of the target according to the space coordinates of four corner points of the target;
and respectively aiming at each target in the plurality of target targets, performing cross multiplication on two diagonal vectors of the target to obtain a cross multiplication result, and dividing the cross multiplication result by the modulus of the two diagonal vectors to obtain a direction vector of a target plane, wherein the direction vector has 3 direction degrees of freedom.
Further, a plurality of image data of the target slope are acquired by a binocular camera;
before step 1, the method further comprises the following steps:
calibrating a camera internal parameter matrix, a distortion coefficient and a camera external parameter matrix of the binocular camera, wherein the camera internal parameter matrix is as follows:
wherein K is a camera internal reference matrix, f x Is the focal length in the x direction, f y Is the focal length in the y direction, dX is the physical length corresponding to one pixel in the x direction, dY is the physical length corresponding to one pixel in the y direction, u 0 Is the center coordinate of the x-direction 0 Is the coordinate of the photosensitive center in the y direction, and theta is the angle deviation generated when the photosensitive plate is assembled;
the distortion coefficient is:
wherein x ', y' are normalized image coordinates of real imaging, x, y are normalized image coordinates of real imaging, and r is pixel point-to-imagingDistance of center point, k 1 ，k 2 ，k 3 For radial distortion coefficients of each order, p 1 ，p 2 Is the tangential distortion coefficient of each order;
further, the camera external parameter matrix calibration method comprises the following steps:
converting image data in a world coordinate system into a camera coordinate system;
processing and optimizing the centroid coordinates of the target under the total station coordinate system and the camera coordinate system to obtain a rotation matrix from the camera coordinate system to the total station coordinate system;
carrying out centroid transformation on the rotation matrix to obtain a translation vector;
re-projecting the image data in the left camera onto the imaging plane of the right camera by using the rotation matrix and the displacement vector, and calculating a re-projection error;
and calculating camera external parameter matrixes of the left camera and the right camera through the reprojection error.
Further, after step 5, the method comprises:
carrying out accumulated calculation on the slope surface displacement of the monitoring result of each target, and judging whether the slope surface displacement exceeds a preset early warning value or not;
and when the slope surface displacement exceeds the early warning value, an alarm is given out.
The invention also provides a side slope earth surface displacement monitoring device, which is used for realizing the side slope earth surface displacement monitoring method and comprises the following steps:
the acquisition module is used for acquiring a plurality of image data of the target slope, and the plurality of image data are obtained by acquiring images of the target slope from different angles;
the analysis module is used for determining a plurality of target targets and obtaining area codes and pixel coordinates corresponding to the target targets by analyzing the image data;
the first calculation module is used for acquiring the space coordinates of four corner points of each target in the plurality of target targets and the mass center coordinates of each target;
the second calculation module is used for calculating the diagonal vector of the target according to the space coordinate and the centroid coordinate of the target to obtain the direction vector of the target plane, wherein the direction vector has 3 direction degrees of freedom;
and the monitoring module is used for monitoring the target slope through the pixel coordinate, the space coordinate, the centroid coordinate and the direction vector to obtain a monitoring result.
The invention also provides a computer readable storage medium on which a computer program is stored, which, when executed, is used for implementing the slope surface displacement monitoring method.
The invention also provides a side slope earth surface displacement monitoring device, which comprises a memory and a processor;
the memory is used for storing computer programs and intermediate data during program processing;
the processor is used for executing a computer program to realize the slope surface displacement monitoring method.
The scheme of the invention has the following beneficial effects:
compared with the prior art, the method has the advantages that the target is determined by acquiring the image data of the target slope from different angles and analyzing the image data, the slope is monitored by calculating and acquiring the pixel coordinates, the spatial coordinates and the centroid coordinates of four corner points and the direction vector with three directional degrees of freedom, the target slope is monitored with 3 displacement degrees of freedom and 3 directional degrees of freedom, single-mark monitoring is improved to multi-mark monitoring, two-dimensional monitoring is improved to three-dimensional monitoring, the changes of the whole and local displacements of the slope in space and time are fully reflected, and the precision of the slope monitoring is improved; the progress of a machine vision algorithm is exerted, the instrument cost and the labor cost for monitoring the displacement of the slope surface per square meter are reduced, and the displacement of the slope surface with a larger area can be monitored at lower cost.
Other advantages of the present invention will be described in detail in the detailed description that follows.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a schematic diagram of a binocular camera according to an embodiment of the present invention;
FIG. 3 is a schematic view of a target according to an embodiment of the invention;
FIG. 4 is a diagram illustrating slope surface displacement accuracy verification according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a triaxial displacement long-time monitoring result according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted", "connected" and "connected" are to be construed broadly, e.g., as being either a locked connection, a detachable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides a method, a device, a medium and equipment for monitoring the displacement of the earth surface of a side slope, aiming at the existing problems, and the method, the device, the medium and the equipment are used for monitoring the displacement of the earth surface of a side slope of Guizhou through a binocular camera.
As shown in fig. 1, an embodiment of the present invention provides a slope surface displacement monitoring method, including:
step 1, acquiring a plurality of image data of a target slope, and acquiring images of the target slope from different angles through a binocular camera to obtain the plurality of image data;
step 3, acquiring the space coordinates of four corner points of each target in the plurality of target targets and the mass center coordinates of each target;
and 5, monitoring the target slope through the pixel coordinate, the space coordinate, the centroid coordinate and the direction vector to obtain a monitoring result.
Specifically, before step 1, the method further comprises:
selecting 10 monitoring points on a target slope, numbering and erecting 10 coding targets by 0-9 respectively, erecting a binocular holder camera at a reasonable position in front of the target slope, wherein the position needs to ensure that the binocular camera can observe the 10 coding targets simultaneously; the method comprises the steps that the binocular camera is connected with the control equipment through an IP interface, data transmission is carried out through a socket method, and the binocular camera is circularly focused among 10 monitoring points through control instructions of the binocular camera preset in the control equipment, so that the displacement of the monitoring points is observed.
Specifically, the camera internal reference matrix, the distortion coefficient and the camera external reference matrix of the binocular camera need to be calibrated, the camera internal reference matrix represents the mapping relationship from the real world three-dimensional coordinates to the camera coordinate system, and the camera internal reference matrix is as follows:
wherein K is a camera internal reference matrix, fx is a focal length in the x direction, fy is a focal length in the y direction, dX is a physical length corresponding to one pixel in the x direction, dY is a physical length corresponding to one pixel in the y direction, and u is 0 Is the center coordinate of the x-direction 0 Is the coordinate of the photosensitive center in the y direction, and theta is the angle deviation generated when the photosensitive plate is assembled;
the distortion coefficient of the camera represents the distortion degree of the real imaging model of the camera compared with the ideal imaging model, and the distortion coefficient is as follows:
wherein, x 'and y' are expressed as the normalized image coordinates of the real imaging, x and y are the normalized image coordinates of the real imaging, r is the distance from the pixel point to the imaging central point, and k 1 ，k 2 ，k 3 For radial distortion coefficients of each order, p 1 ，p 2 Is the tangential distortion coefficient of each order; distortion coefficient vectors are often D = [ k ] 1 ,k 2 ,p 1 ,p 2 ,k 3 ]D is a distortion coefficient vector.
In the embodiment of the present invention, a Zhang Zhengyou calibration method is adopted as a method for solving a camera internal reference matrix and a camera distortion coefficient, a Zhang Zhengyou calibration method is 2D plane target-based camera calibration, a checkerboard calibration board is utilized, after an image of the calibration board is obtained, a corresponding image detection algorithm can be utilized to obtain a pixel coordinate (u, v) of each corner point, a world coordinate system is fixed on the checkerboard, a physical coordinate of any point on the checkerboard is equal to 0, since the world coordinate system of the calibration board is artificially defined in advance, the size of each grid on the calibration board is known, and a physical coordinate (x, y, z = 0) of each corner point under the world coordinate system can be calculated.
This information will be utilized: and calibrating the camera by using the pixel coordinates (u, v) of each corner point and the physical coordinates (x, y, z = 0) of each corner point in a world coordinate system to obtain a camera internal reference matrix and a distortion parameter.
The camera external reference matrix comprises a 3 multiplied by 3 rotation matrix R and a 3 multiplied by 1 displacement vector t, represents the space relative pose relation between a left camera and a right camera of a binocular camera, and is calibrated based on a method for minimizing the reprojection error, and the method comprises the following steps:
taking a point P in the image data as an example, converting the point P in the world coordinate system into a point P in the camera coordinate system through a conversion matrix, wherein the specific algorithm is as follows:
P i ＝[R i t i ]P w (3)
wherein, P w Is the coordinate of point P in the world coordinate system, P i Is the coordinate of point P in the left camera coordinate system, R i Is a rotation matrix from the world coordinate system to the left camera coordinate system, t i Is a translation matrix from the world coordinate system to the left camera coordinate system.
And (3) re-projecting the image data in the left camera onto an imaging plane of the right camera by using the rotation matrix R and the displacement vector t, wherein the specific algorithm is as follows:
wherein the content of the first and second substances,
for the left camera and the right camera, P l Is the coordinate of the point P in the left camera coordinate system, and R is the rotation matrix from the left camera coordinate system to the left camera coordinate systemT is the translation matrix from the world coordinate system to the left camera coordinate system, K r Is the internal reference matrix of the right camera.
Calculating a re-projection error, and optimizing and calculating the camera external parameter matrixes of the left camera and the right camera by minimizing the re-projection error, wherein the calculation formula of the re-projection error is as follows:
wherein err is the reprojection error,
for the left camera point, p, the reprojected point in the right camera i Is the imaging point in the right camera.
Specifically, step 2 includes:
carrying out binarization processing on image data according to an adaptive threshold segmentation algorithm, extracting a contour, and determining a plurality of suspected slope displacement monitoring targets, wherein the specific steps are as follows:
firstly, processing an original image acquired on site by using an adaptive threshold segmentation algorithm to obtain a binary image;
secondly, extracting the binary image to obtain a complete closed outline;
finally, the contour areas of all the suspected rectangles are reserved, and the contour areas of all the suspected rectangles are determined as suspected slope displacement monitoring targets;
processing the suspected slope displacement monitoring targets one by one, and analyzing the region codes of the suspected slope displacement monitoring targets;
judging whether the area code of the suspected slope displacement monitoring target is legal or not;
if the area coding of the suspected slope displacement monitoring target is legal, determining the suspected slope displacement monitoring target as a target;
performing sub-pixel optimization on four corner points of a target to obtain a region code and a pixel coordinate corresponding to the target, and specifically comprising the following steps:
firstly, carrying out perspective transformation on all suspected slope displacement monitoring targets to obtain a square image;
secondly, carrying out Ossu thresholding on the square image to obtain a separated binary image;
then, carrying out region division on the binary image to obtain n multiplied by n small regions;
then, counting the number of black pixels and white pixels in each small region, and taking the number of the absolute majority pixels as the coding of the region, wherein black is 0 and white is 1, as shown in fig. 2;
then, checking whether the codes of the region are legal or not, checking the obtained region codes, abandoning the illegal coding region, reserving the legal coding region, and determining the legal coding region as the target;
finally, sub-pixel optimization is carried out on four corner points of the target to obtain sub-pixel-level pixel coordinates (u) of the four corner points i ,v i ) And its corresponding code.
Specifically, step 3 includes:
the method comprises the steps of firstly matching target targets with the same numbers of a left camera and a right camera, continuously sampling for 20 times, taking an average value, reducing accidental error risks existing in single sampling, and improving the stability of a monitoring result.
Because images of a plurality of target targets need to be obtained through the rotation of the visual angle of the camera, a camera coordinate system is unstable, the camera coordinate system is calibrated by obtaining the coordinates of the target targets under a world coordinate system, and therefore a conversion matrix M from a binocular camera world coordinate system to camera pixel coordinates needs to be calculated according to a camera internal reference matrix (K, D) and an external reference matrix (R, t), wherein the conversion matrix M represents the conversion relation from the world coordinate system to the pixel coordinate system, and the conversion expression is as follows:
wherein X, Y and Z are space coordinates of the target in a world coordinate system, u i ，v i Is a targetIn the left camera pixel coordinates, M is the transformation matrix of the world coordinate system to the left camera pixel coordinates.
The transformation matrix M is calculated by the camera internal reference matrix (K, D) and the external reference matrix (R, t), and the calculation formula is as follows:
wherein f is x Focal length in x direction, f y Is focal length in y direction, x 0 The coordinate of the center of the light-sensing in the x direction, y 0 Is the coordinate of the center of the light sensing plate in the y direction, s is the error term caused by the assembly deviation of the light sensing plate, R ij For rotating the elements of the ith row and jth column of the matrix, T 0i Is the ith element of the displacement vector, M is the conversion matrix from the world coordinate system coordinate to the camera pixel coordinate, M ij Is the element of the ith row and the jth column of the transformation matrix.
Then, for each target of the plurality of target targets, as shown in fig. 3 according to the principle of triangulation, a spatial coordinate relation equation is established and solved by converting the matrix M and the pixel coordinates, so as to obtain spatial coordinates of four corner points of the target.
Specifically, the spatial coordinate relationship equation is as follows:
wherein the content of the first and second substances,
is the element of the ith row and the jth column of the kth transformation matrix, X, Y and Z are the space coordinates of the target in the world coordinate system, u 1 ，v 1 Is the pixel coordinate of the target in the left camera, u 2 ，v 2 Is the pixel coordinates of the target in the right camera.
And finally, calculating the mean value of the space coordinates of the four corner points of the target to obtain the mass center coordinates of the plurality of target targets.
Specifically, step 4 includes:
respectively aiming at each target in the plurality of target targets, calculating a conversion matrix after a camera coordinate system is converted into a total station coordinate system by using the pixel coordinates of the target under the camera coordinate system and the coordinates of the target measured by the total station, and specifically, the method comprises the following steps:
firstly, measuring coordinate values of more than 8 target targets at different positions in a total station coordinate system;
calculating the centroid coordinates of the target under a total station coordinate system and a camera coordinate system, and performing centroid removing operation on the centroid coordinates of the two groups of point clouds to obtain a centroid-removed point cloud coordinate value, wherein the calculation formula is as follows:
secondly, optimizing the mean square error functions of the two groups of point clouds by using a least square method, and taking the optimized result as a rotation matrix R from a camera coordinate system to a total station coordinate system * The specific optimization equation is as follows:
according to a rotation matrix R * And obtaining a translation vector t through centroid transformation:
t＝p 1 -Rp 2 (12)
wherein p is 1 、p 2 Is the centroid coordinate of the point clouds in the 1 st and 2 nd groups,
is the space coordinate of the ith point of the 1 st and 2 nd groups of point clouds, R is the rotation matrix from the left camera coordinate system to the left camera coordinate system,
the centroid-removing space coordinates of the ith point of the 1 st and 2 nd group point clouds are obtained.
Using a rotation matrix R * And converting the coordinates of the target under the camera coordinate system into the coordinates under the total station coordinate system.
And finally, according to the space coordinates of the four corner points of the plurality of target targets, calculating two diagonal vectors of each target as follows:
n 1 ＝p 1 -p 3 (13)
n 2 ＝p 2 -p 4 (14)
wherein n is 1 、n 2 As diagonal vector, p 1 Is the 1 st corner point, p 2 Is the 2 nd corner point, p 3 Is the 3 rd corner point, p 4 The 4 th corner point.
Respectively aiming at each target in a plurality of target targets, performing cross multiplication on two diagonal vectors of the target and dividing the cross multiplication by the modulus of the two diagonal vectors to obtain a direction vector of a target plane, wherein the direction vector has 3 direction degrees of freedom, and the calculation formula is as follows:
wherein n is a direction vector, n 1 、n 2 Is a diagonal vector.
Specifically, step 5 includes: and obtaining a 6-degree-of-freedom monitoring data result of 3 displacement directions +3 dip directions by combining the obtained pixel coordinates of the target, the spatial coordinates of the four corner points and the coordinates of the mass center with the direction vector of the target.
The verification of the horizontal displacement accuracy and the verification of the vertical displacement accuracy obtained by the embodiment of the invention through the method are shown in fig. 4 (a) and fig. 4 (b), and the long-time monitoring result of z displacement, the long-time monitoring result of x displacement and the long-time monitoring result of y displacement obtained in the embodiment are shown in fig. 5 (b) and fig. 5 (c).
Specifically, step 5 is followed by:
transmitting the monitoring results of the target targets to a back-end server in real time, setting an early warning value in the back-end server, and recording the 6-freedom degree monitoring results of 10 monitoring points through the back-end server; and the monitoring result of each target is subjected to accumulated calculation of the slope earth surface displacement, whether the slope earth surface displacement exceeds an early warning value or not is judged, and an alarm is given out when the slope earth surface displacement exceeds the early warning value.
The embodiment of the invention also provides a side slope earth surface displacement monitoring device, which is used for realizing the side slope earth surface displacement monitoring method and comprises the following steps:
the acquisition module is used for acquiring a plurality of image data of the target slope, and the plurality of image data are obtained by acquiring images of the target slope from different angles;
the analysis module is used for determining a plurality of target targets and obtaining area codes and pixel coordinates corresponding to the target targets by analyzing the image data;
the first calculation module is used for acquiring the space coordinates of four corner points of each target in the plurality of target targets and the mass center coordinates of each target;
the second calculation module is used for calculating the diagonal vector of the target according to the space coordinate and the centroid coordinate of the target to obtain the direction vector of the plane of the target, wherein the direction vector has 3 directional degrees of freedom;
and the monitoring module is used for monitoring the target slope through the pixel coordinate, the space coordinate, the centroid coordinate and the direction vector to obtain a monitoring result.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed, the computer program is used for implementing the slope surface displacement monitoring method.
The embodiment of the invention also provides a side slope earth surface displacement monitoring device, which comprises a memory and a processor;
the memory is used for storing computer programs and intermediate data during program processing;
the processor is used for executing a computer program to realize the slope surface displacement monitoring method.
Compared with the prior art, the method and the device have the advantages that the target is determined by acquiring the image data of the target slope from different angles and analyzing the image data, the slope is monitored by calculating and acquiring the pixel coordinate of the target, the spatial coordinates and the centroid coordinate of four corner points and the direction vector with three directional degrees of freedom, the target slope is monitored by 3 displacement degrees of freedom and 3 directional degrees of freedom, single-mark monitoring is improved to multi-mark monitoring, two-dimensional monitoring is improved to three-dimensional monitoring, the changes of the whole and local displacements of the slope in space and time are fully reflected, and the precision of the slope monitoring is improved; the progress of a machine vision algorithm is exerted, the instrument cost and the labor cost for monitoring the slope ground surface displacement per square meter are reduced, and the monitoring of the slope ground surface displacement in a larger area can be realized at lower cost.
While the foregoing is directed to the preferred embodiment of the present invention, it will be appreciated by those skilled in the art that various changes and modifications may be made therein without departing from the principles of the invention as set forth in the appended claims.
Claims (10)
1. A side slope earth surface displacement monitoring method is characterized by comprising the following steps:
step 1, acquiring a plurality of image data of a target slope, wherein the plurality of image data are obtained by acquiring images of the target slope from different angles;
step 2, analyzing the image data to determine a plurality of target targets and obtain area codes and pixel coordinates corresponding to the target targets;
step 3, acquiring the space coordinates of four corner points of each target in the plurality of target targets and the mass center coordinates of each target;
step 4, respectively aiming at each target in the plurality of target targets, calculating diagonal vectors of the target targets according to the space coordinates and the mass center coordinates of the target targets to obtain direction vectors of each target plane, wherein the direction vectors have 3 direction degrees of freedom;
and 5, monitoring the target slope through the pixel coordinate, the space coordinate, the centroid coordinate and the direction vector to obtain a monitoring result.
2. The slope surface displacement monitoring method according to claim 1, wherein the step 2 comprises:
carrying out binarization processing on the image data according to an adaptive threshold segmentation algorithm and extracting the outline of the image data to obtain a plurality of suspected slope displacement monitoring targets;
processing the suspected slope displacement monitoring targets one by one, and analyzing the area codes of the suspected slope displacement monitoring targets;
judging whether the area code of the suspected slope displacement monitoring target is legal or not;
if the area code of the suspected slope displacement monitoring target is legal, determining the suspected slope displacement monitoring target as a target;
and performing sub-pixel optimization on four corner points of the target to obtain a region code and a pixel coordinate corresponding to the target.
3. The slope surface displacement monitoring method according to claim 1, wherein the step 3 comprises:
aiming at each target in the plurality of target targets, according to a triangulation principle, establishing a spatial coordinate relation equation through the conversion matrix and the pixel coordinates, and solving to obtain spatial coordinates of four corner points of the target, wherein the conversion matrix is obtained by calculation according to a camera internal parameter matrix and a camera external parameter matrix;
and carrying out mean value calculation on the space coordinates of the four corner points of the target to obtain the mass center coordinates of the target targets.
4. The slope surface displacement monitoring method according to claim 3, wherein the step 4 comprises:
respectively measuring coordinate values of the target under a total station coordinate system aiming at each target in a plurality of target targets;
calculating, for each of a plurality of the target targets, a centroid coordinate of the target in the total station coordinate system and in the camera coordinate system, respectively;
respectively aiming at each target in a plurality of target targets, calculating two diagonal vectors of the target according to the space coordinates of four corner points of the target;
and respectively performing cross multiplication on two diagonal vectors of the target aiming at each target in the plurality of target targets to obtain a cross multiplication result, and dividing the cross multiplication result by the modulus of the two diagonal vectors to obtain a direction vector of the target plane, wherein the direction vector has 3 direction degrees of freedom.
5. The slope surface displacement monitoring method according to claim 1,
a plurality of the image data of the target slope are acquired by a binocular camera;
before step 1, the method further comprises:
calibrating a camera internal reference matrix, a distortion coefficient and a camera external reference matrix of the binocular camera, wherein the camera internal reference matrix is as follows:
wherein K is a camera internal reference matrix, f x Is the focal length in the x direction, f y Is the focal length in the y direction, and dX is the physical length corresponding to one pixel in the x directiondY is the physical length corresponding to one pixel in the y-direction, u 0 Is the center coordinate of the x-direction 0 Is the coordinate of the photosensitive center in the y direction, and theta is the angle deviation generated when the photosensitive plate is assembled;
the distortion coefficient is:
wherein, x 'and y' are expressed as the normalized image coordinates of the real imaging, x and y are the normalized image coordinates of the real imaging, r is the distance from the pixel point to the imaging central point, and k 1 ，k 2 ，k 3 For radial distortion coefficients of each order, p 1 ，p 2 The tangential distortion coefficients of each order.
6. The slope surface displacement monitoring method according to claim 5, wherein the camera external reference matrix calibration method comprises:
converting image data in a world coordinate system into a camera coordinate system;
processing and optimizing the centroid coordinates of the target under the total station coordinate system and the camera coordinate system to obtain a rotation matrix from the camera coordinate system to the total station coordinate system;
performing centroid transformation on the rotation matrix to obtain a translation vector;
utilizing the rotation matrix and the displacement vector to re-project the image data in the left camera to the imaging plane of the right camera, and calculating a re-projection error;
calculating camera external parameter matrixes of the left camera and the right camera through the reprojection error.
7. The slope surface displacement monitoring method according to claim 6, wherein after the step 5, the method comprises the following steps:
performing accumulated calculation of slope surface displacement on the monitoring result of each target, and judging whether the slope surface displacement exceeds a preset early warning value;
and when the slope surface displacement exceeds the early warning value, an alarm is given out.
8. A slope surface displacement monitoring device for implementing the slope surface displacement monitoring method of any one of claims 1 to 7, comprising:
the acquisition module is used for acquiring a plurality of image data of the target slope, and the plurality of image data are obtained by acquiring images of the target slope from different angles;
the analysis module is used for determining a plurality of target targets and obtaining area codes and pixel coordinates corresponding to the target targets by analyzing the image data;
the first calculation module is used for acquiring the space coordinates of four corner points of each target in the plurality of target targets and the mass center coordinates of each target;
the second calculation module is used for calculating the diagonal vector of the target according to the space coordinate and the centroid coordinate of the target to obtain the direction vector of the target plane, wherein the direction vector has 3 directional degrees of freedom;
and the monitoring module is used for monitoring the target slope through the pixel coordinate, the space coordinate, the centroid coordinate and the direction vector to obtain a monitoring result.
9. A computer-readable storage medium on which a computer program is stored, which when executed, implements a method of slope surface displacement monitoring according to any one of claims 1 to 7.
10. A side slope earth surface displacement monitoring device is characterized by comprising a memory and a processor;
the memory is used for storing computer programs and intermediate data during program processing;
the processor is used for executing the computer program to realize the slope surface displacement monitoring method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211378045.3A CN115511878A (en) | 2022-11-04 | 2022-11-04 | Side slope earth surface displacement monitoring method, device, medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211378045.3A CN115511878A (en) | 2022-11-04 | 2022-11-04 | Side slope earth surface displacement monitoring method, device, medium and equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115511878A true CN115511878A (en) | 2022-12-23 |
Family
ID=84512269
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211378045.3A Pending CN115511878A (en) | 2022-11-04 | 2022-11-04 | Side slope earth surface displacement monitoring method, device, medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115511878A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116778094A (en) * | 2023-08-15 | 2023-09-19 | 深圳眸瞳科技有限公司 | Building deformation monitoring method and device based on optimal viewing angle shooting |
| CN117490579A (en) * | 2024-01-03 | 2024-02-02 | 苏州大学 | Foundation pit displacement monitoring system based on image vision processing |
-
2022
- 2022-11-04 CN CN202211378045.3A patent/CN115511878A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116778094A (en) * | 2023-08-15 | 2023-09-19 | 深圳眸瞳科技有限公司 | Building deformation monitoring method and device based on optimal viewing angle shooting |
| CN116778094B (en) * | 2023-08-15 | 2023-11-24 | 深圳眸瞳科技有限公司 | Building deformation monitoring method and device based on optimal viewing angle shooting |
| CN117490579A (en) * | 2024-01-03 | 2024-02-02 | 苏州大学 | Foundation pit displacement monitoring system based on image vision processing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108416791B (en) | Binocular vision-based parallel mechanism moving platform pose monitoring and tracking method | |
| CN109559354B (en) | Method and device for measuring tower clearance | |
| CN115511878A (en) | Side slope earth surface displacement monitoring method, device, medium and equipment | |
| EP2847741B1 (en) | Camera scene fitting of real world scenes for camera pose determination | |
| CN105700525B (en) | Method is built based on Kinect sensor depth map robot working environment uncertainty map | |
| CN111255636A (en) | Method and device for determining tower clearance of wind generating set | |
| CN110084785B (en) | Power transmission line vertical arc measuring method and system based on aerial images | |
| CN113592989A (en) | Three-dimensional scene reconstruction system, method, equipment and storage medium | |
| CN113052881A (en) | Automatic registration method for extracting pole point in indoor three-dimensional point cloud | |
| CN107729893A (en) | A kind of vision positioning method of clapper die spotting press, system and storage medium | |
| Gerke | Using horizontal and vertical building structure to constrain indirect sensor orientation | |
| CN109341668A (en) | Polyphaser measurement method based on refraction projection model and beam ray tracing method | |
| CN110702028B (en) | Three-dimensional detection positioning method and device for orchard trunk | |
| CN111179335A (en) | Standing tree measuring method based on binocular vision | |
| CN108195736A (en) | A kind of method of three-dimensional laser point cloud extraction Vegetation canopy clearance rate | |
| CN111798507A (en) | Power transmission line safety distance measuring method, computer equipment and storage medium | |
| CN114529615B (en) | Radar calibration method, device and storage medium | |
| CN110992463B (en) | Three-dimensional reconstruction method and system for sag of transmission conductor based on three-eye vision | |
| CN116399820A (en) | Method, device, equipment and medium for verifying authenticity of vegetation remote sensing product | |
| WO2017131547A1 (en) | A method and apparatus for single camera optical measurements | |
| CN102542563A (en) | Modeling method of forward direction monocular vision of mobile robot | |
| CN115937243A (en) | Downstream river image deformation monitoring method based on point cloud three-dimensional data | |
| CN112734712B (en) | Imaging detection method and system for health state of ship vibration equipment | |
| CN114648575A (en) | Track slope displacement binocular vision detection method and system based on ORB algorithm | |
| Dikmen | Development of star tracker attitude and position determination system for spacecraft maneuvering and docking facility |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |