CN107292876B - DSCM (differential space-time non-uniformity) analysis method based on deformation space-time non-uniform characteristics of rock and soil materials - Google Patents
DSCM (differential space-time non-uniformity) analysis method based on deformation space-time non-uniform characteristics of rock and soil materials Download PDFInfo
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Abstract
The invention discloses a DSCM (differential spatial-temporal uniformity) analysis method based on deformation space-time non-uniform characteristics of geotechnical materials, which can improve the image analysis efficiency by dynamically adjusting the search range of pixel measuring points. The method is based on the characteristics of spatio-temporal non-uniform deformation of the geotechnical materials, and determines the search ranges of all pixel measuring points in the image according to the analysis result of the reference grid. The method is realized in software through programming, is suitable for digital speckle correlation analysis of various rock and soil materials, can greatly reduce the number of pixel points for image correlation analysis, and effectively improves the image analysis speed of the digital speckle correlation method. The invention can improve the calculation precision and the image analysis speed by combining with the precision optimization method of the digital speckle correlation analysis considering the rock fracture influence. The invention can effectively inhibit measuring point displacement analysis errors or mistakes generated by image noise, thereby improving the digital speckle correlation measurement precision of the deformation of the rock and soil material.
Description
Technical Field
The invention relates to a digital speckle correlation analysis method, in particular to a digital speckle correlation analysis method which improves the image analysis efficiency by dynamically adjusting the search range of pixel measuring points based on the deformation space-time non-uniform characteristics of rock and soil materials.
Background
The digital speckle correlation method (DSCM for short) is a measurement method that takes a digital camera or a CCD camera or other tools as a digital image acquisition device, and calculates and tracks coordinate changes of geometric points of a digital speckle image in different stages by using a digital image processing and analyzing technology, thereby realizing displacement measurement and deformation analysis. The DSCM as a light measurement method has the characteristics of full field property, non-contact property, high precision and the like, is suitable for visual measurement of a deformation process of geotechnical materials, and has a universal and unique effect in geotechnical engineering model experiments. In recent years, with the rapid development of digital photographing devices and computer hardware, DSCM has been widely used in experimental research in various fields including geotechnical engineering. For example, in the railway track deformation research, displacement data which is difficult to obtain by a conventional method can be obtained due to the measurement characteristic of the technology; in the research of a landslide deformation field, the DSCM is used for calculating the direction and displacement variation of each point on a landslide body to obtain a landslide displacement distribution map of a measuring point; the three-dimensional observation system utilizing the technology can effectively observe the rock deformation and damage mechanism, and provides an important reference for the research of the macroscopic and microscopic deformation and damage mechanism of the rock and soil medium. The wide application of the digital speckle correlation method in various fields shows the outstanding advantages of the method. Generally, a DSCM system is composed of two parts, namely hardware and software, and two core functions, namely image acquisition and image analysis, are respectively realized, and under the current experimental conditions, hardware equipment can basically meet the requirements of each experiment on image acquisition, so that a DSCM rapid analysis method is provided, and a set of efficient and complete analysis software which can meet the requirements of users becomes the key point for popularization of the DSCM method.
Improving the speed of image analysis and the accuracy of deformation measurement are two of the most critical research contents for optimizing the DSCM system.
The research on improving the image analysis speed of the DSCM system by dynamically adjusting the search range of the pixel measurement points can be currently summarized into two representative ideas: the method has the advantages that under the condition that the search radius is not changed, the search range is narrowed through adjusting the search angle, the search number of pixel points in the correlation analysis is reduced, and therefore the analysis speed is improved; secondly, the search range is narrowed by adjusting the search radius, so that the analysis speed is improved. Regarding to the first idea, in the 8 th year in 2015, the digital speckle correlation optimization analysis method based on the characteristic of the progressive deformation of the rock and soil provides a rapid analysis method based on the characteristic of the progressive deformation of the rock and soil, which is a local directional search method, and improves the image analysis speed. Regarding to the second concept, in the 3 rd year in 2013, the application of the predictive search algorithm in image correlation, introduces a method of dividing a speckle pattern into a plurality of sub-regions, predicting the displacement of an unknown region by using the weighting of the displacement between adjacent sub-regions, searching for a region with the maximum correlation coefficient in a small range near the predicted region, and further improving the calculation speed. However, the method adopts a sub-region division weighting calculation mode for prediction, certain errors exist in the rock and soil materials with complex deformation conditions, the analysis speed is improved, and the analysis precision is reduced to a certain extent.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a rapid DSCM analysis method based on the deformation space-time non-uniform characteristics of rock and soil materials. The method of the invention realizes dynamic search by dividing the reference grid based on the idea of adjusting the search radius to narrow the search range and taking the maximum displacement of the grid unit node as the search radius of the measuring point in the grid unit. The method takes the maximum displacement of the grid unit node as the search radius of the measuring point in the grid unit, and has advantages in the aspect of measurement precision compared with a method for determining the search radius of the pixel measuring point in a weighting mode.
The invention improves the image analysis efficiency by dynamically adjusting the search range of the pixel measuring points aiming at the problem of the image analysis speed of the DSCM system based on the time-space non-uniformity deformation characteristics of the geotechnical materials, namely the deformation of the geotechnical materials is usually obviously different at different time points or stages and different space regions. The invention realizes the aim by programming the method in software.
The technical scheme for realizing the purpose of the invention is as follows:
a DSCM analysis method based on the deformation space-time nonuniform characteristics of rock and soil materials is shown in figure 1, and comprises the following specific steps:
step 1, in a sequence image to be analyzed, selecting an image measuring point range larger than that of subsequent analysis on a 1 st image, and then selecting a pixel measuring point distance larger than that in the actual analysis process to divide an initial grid measuring point so as to reduce the image analysis time of the grid measuring point. The images refer to sequence photographs of surface displacement fields, strain fields and overall deformation of the geotechnical materials, which are acquired by image acquisition equipment in the DSCM test process.
And 2, selecting the same search radius and the search step length of 1 pixel, performing measurement point analysis on all sequence images, and obtaining deformed measurement point grid data corresponding to each image to be used as a reference grid for subsequent image analysis.
And 3, reestablishing an image analysis project by using the sequence image to be analyzed, and reading the sequence image to be analyzed into a computer memory by an image analysis program before formal analysis by setting the storage position of the reference grid data file on a computer hard disk in the image analysis parameter and option setting.
And 4, dividing an actual measuring point grid in the image range covered by the reference grid on the 1 st image by using a grid measuring point interval, wherein the actual measuring point grid is a grid divided when the image is formally analyzed after the reference grid data is obtained.
And 5, scanning and analyzing all reference grid data in a computer memory by adopting a general geometric discrimination method of judging whether a point is in the interior or the boundary of a quadrangle for any grid node Pi (namely a measuring point) in the actual measuring point grid, and finding out a reference grid unit where the measuring point Pi is located (such as 4 nodes of the reference grid, P1, P2, P3, P4, P1, P2, P3 and P4).
And 6, calculating the maximum displacement value dmax (dmax = max { dx1, dy1, dx2, dy2, dx3, dy3, dx4 and dy4} of the 4 nodes of the reference grid unit where Pi is located in the x direction and the y direction on the N +1 th image, and then taking the displacement value as the search radius of the Nth image Pi. Where (x, y) is the pixel coordinate of the measuring point in the analysis image, x represents the abscissa, y represents the ordinate, dx1, dy1, dx2, dy2, dx3, dy3, dx4, and dy4 are the displacement values of the 4 node coordinates of the grid cell where Pi is located.
And 7, analyzing and obtaining the position coordinates of the measuring point Pi on the (N + 1) th image by adopting a conventional digital speckle correlation method, and calculating the displacement of the measuring point Pi by comparing the position coordinate difference values of the measuring point Pi on the (N + 1) th image and the (N + 1) th image. The conventional digital speckle correlation method is an image analysis method with the same search radius for measuring points.
And 8, circulating the step 5 to the step 7 until all the grid measuring points in the actual measuring point grid are analyzed, and obtaining the displacement data of all the measuring points.
The method of the invention dynamically adjusts the search range according to the reference grid, searches the dynamic range of the measuring points one by one, and can effectively improve the analysis speed of the digital speckle correlation method; the method is suitable for digital speckle correlation analysis of various rock and soil materials, and the analysis speed is obviously improved; the method can be combined with a precision optimization method of digital speckle correlation analysis considering the influence of the rock mass fracture, effectively eliminates errors generated by the rock mass fracture, improves the calculation precision, and can also improve the speed of image analysis; the method can effectively inhibit measuring point displacement analysis errors or mistakes generated by image noise, thereby improving the DSCM measurement precision of the deformation of the rock and soil material.
Drawings
FIG. 1 is a block diagram of the programming of the method of the present invention.
FIG. 2 is a functional option diagram of the method of the present invention in a digital photo deformation measurement software system PhotoIndor.
FIG. 3 is a digital speckle correlation analysis grid map (kth image) of the present invention.
FIG. 4 is a digital speckle correlation analysis grid map (k +1 th image) of the present invention.
FIG. 5 is a comparison graph of the calculation speed of the present invention and the existing analysis method using the same search radius for all the measurement points.
FIG. 6 is a comparison graph of the calculation speed of the error correction method in consideration of the influence of rock mass fracture.
Detailed Description
The invention is further described below with reference to the figures and examples.
The invention is realized by programming PhotoInfo in a digital photographic deformation measurement software system. The function of the present invention is named "Point-by-Point dynamic Range search method (PDSS)" in software, as shown in FIG. 2.
The image analysis of the invention is generally divided into two steps, firstly, a grid (referred to as a reference grid for short) with larger measuring point intervals covering the actual analysis range is divided on the image, the first step of analysis is the same as the traditional method in the aspect of measuring point search radius, all measuring points adopt the same search radius, the second step of analysis utilizes the analysis result of the first step as the reference grid of the actual measuring point grid analysis, the maximum displacement of the reference grid unit node is taken as the search radius of the measuring point in the grid unit, and then the rapid analysis is carried out. The concrete description is as follows: firstly, analyzing all pixel measuring points on the image by adopting the same search range to obtain reference grid data. As shown in fig. 3, assuming that there is a measuring point Pi on the kth experimental image, under the action of an external load, the measuring point Pi moves to the position of the (k + 1) th image as shown in fig. 4 along with the deformation of the image area, and the generated displacement is ds; then, a geometric discrimination method of 'whether points are in the quadrangle or on the boundary' is adopted to determine a reference grid P1P2P3P4 where Pi is located, and maximum displacement values in x and y directions of four nodes of P1, P2, P3 and P4 are taken as search range values of Pi on the kth image.
In the implementation process, three common rock and soil test materials, namely rock, sandy soil and similar materials, are selected, and an ordinary method (an image analysis method without using a reference grid and adopting the same search radius for all measuring points), an error correction method considering the influence of rock fractures and a PDSS method are applied for analysis and comparison. The computer test software and hardware environment comprises a 64-bit Windows10 operating system, a core i7-4770CPU (3.4 GHZ), a 16GB memory, a 1TB hard disk and an integrated video card. In the image analysis process of three geotechnical materials, 5000 points and 10000 points are respectively taken as measuring points, 21 pixels multiplied by 21 pixels are uniformly taken as pixel block sizes, and sub-pixel parameters are all taken as 1 pixel. In the test process, a single lens reflex is used as image acquisition equipment to acquire sequential images of a surface displacement field, a strain field and overall deformation of rocks, sandy soil and similar materials in the test process, and the sequential images are converted into a BMP format for image processing by a PhotoIndor program. The sequence photographs for rock analysis were 12 in total, for sand analysis were 12 in total, and for similar material analysis were 16 in total.
Example the basic process of applying the PDSS process of the invention is divided into the following 8 steps:
(1) in a sequence image to be analyzed, an image measuring point range which is larger than that of a subsequent actual analysis is selected from a 1 st image, and then a larger pixel measuring point distance is selected to divide an initial grid measuring point, so that the image analysis time of the grid measuring point is reduced. In the embodiment, the pixel measuring point distances of reference grids of rock, sandy soil and similar materials are respectively selected to be 25 pixels, 45 pixels and 50 pixels, the measuring point distances of 5000 measuring points and 10000 measuring point analyses of rock materials are respectively 10 pixels and 7 pixels in the actual analysis process, the measuring point distances of 5000 measuring points and 10000 measuring point analyses of sandy soil materials are respectively 19 pixels and 13 pixels, and the measuring point distances of 5000 measuring points and 10000 measuring point analyses of similar materials are respectively 20 pixels and 14 pixels.
(2) In the process of carrying out measuring point digital speckle correlation analysis, the same search radius and the search step length of 1 pixel are selected (the search radius of rock materials, sandy soil materials and similar materials in the test process is respectively 15 pixels, 16 pixels and 16 pixels), then the measuring point analysis of all sequence images is carried out, and the deformed measuring point grid data corresponding to each image obtained in this way is called as the reference grid of the subsequent actual image analysis.
(3) And after the reference grid analysis is finished, reestablishing an image analysis project by using the sequence image to be analyzed, and reading the sequence image to be analyzed into a computer memory by an image analysis program before formal analysis by setting the storage position of the reference grid data file on a computer hard disk in the image analysis parameter and option setting.
(4) On the 1 st image, an actual measuring point grid is divided by a certain grid measuring point distance in the image range covered by the reference grid.
(5) For any grid node Pi (i.e. a measured point) in the actual measured point grid, scanning and analyzing all reference grid data in a computer memory by adopting a general geometric discrimination method of 'whether the point is in the interior or the boundary of a quadrangle', and finding a reference grid unit (such as P1P2P3P 4) where the measured point Pi is located.
(6) The maximum displacement values dmax (dmax = max { dx1, dy1, dx2, dy2, dx3, dy3, dx4, dy4} in the x and y directions of the 4 nodes of the reference grid cell where Pi is located on the N +1 th image are calculated, and then the displacement values are taken as the search radius of the nth image Pi.
(7) And analyzing and obtaining the position coordinates of the measuring point Pi on the (N + 1) th image by adopting a digital speckle correlation method through correlation analysis, and calculating the displacement of the measuring point Pi by comparing the position coordinate difference values of the measuring point Pi on the (N + 1) th image and the (N + 1) th image.
(8) And (5) circulating the step (5) to the step (7) until all the grid measuring points in the actual analysis grid are analyzed, and obtaining displacement data of all the measuring points.
FIG. 5 illustrates the comparison of the image analysis speed of the present invention applied to three commonly used geotechnical test materials-rock, sandy soil and similar materials-with the conventional method (the analysis method in which all the measuring points use the same search radius). As can be seen from FIG. 5, the analysis speed of the three rock-soil experimental materials is increased by 8-16 times by applying the method, and the method can effectively solve the problem of digital speckle correlation rapid analysis of the rock-soil materials with the time-space non-uniform deformation characteristics.
FIG. 6 illustrates the calculated velocity change of the error correction method in combination with consideration of the influence of rock mass fracture. The method is characterized in that a rock material is selected for testing, the number of test points is 5000, the size of a square pixel block is 21 x 21 pixels, the sub-pixel parameter is calculated by taking 1, the analysis speed of the combination of the PDSS method and the error correction method considering the rock mass fracture influence is improved by 5 times on the basis of the common method calculation time without considering the fracture influence and with the same search radius adopted by all the test points, although the PDSS is not used alone, the improvement multiple is high (13 times), the image analysis speed is still improved obviously (5.4 times) compared with the error correction method (minus 0.4 times) used alone.
The problem of the measurement precision in the specific implementation process of the invention is represented by the following two points: (1) in the DSCM measurement analysis process, local image noise caused by the influence of the illumination environment is usually difficult to completely avoid, which is a main reason for displacement analysis errors or errors of image points near a common noise area. Some small white spots appearing on the image are the most common image noise appearance form, when 4 nodes of a reference unit grid are not influenced by noise (namely, the displacement is accurate), and a white spot (a noise area) is just positioned in the unit grid, as the search range of actual measuring points in the grid mainly depends on the correct displacement of the 4 nodes of the reference unit grid, the small white spots cannot be directly influenced by white spot noise like a common method, and therefore, the small white spots can play an effective 'inhibiting' role in measuring point displacement errors or errors generated by the noise. (2) The use premise of the invention requires that the displacement of the measuring point in the grid unit is not more than the displacement of 4 nodes in the unit, and the test tests show that the image measuring point area of rock, sandy soil and similar materials can meet the condition on the whole. For the situation that the individual area of the geotechnical material does not meet the condition (except for error situation caused by noise), a certain range can be properly expanded on the basis of the measuring point searching range determined by the PDSS, and the problem that the measuring point displacement in the reference grid is larger than the unit node displacement is solved.
Claims (1)
1. A DSCM analysis method based on rock-soil material deformation space-time nonuniform characteristics comprises the following specific steps:
step 1, in a sequence image to be analyzed, selecting an image measuring point range larger than that of subsequent analysis on a 1 st image, and then selecting a pixel measuring point distance larger than that in the actual analysis process to divide an initial grid measuring point so as to reduce the image analysis time of the grid measuring point; the images refer to sequence photographs of surface displacement fields, strain fields and overall deformation of the geotechnical materials, which are acquired by image acquisition equipment in the DSCM test process;
step 2, selecting the same search radius and the search step length of 1 pixel, performing measurement point analysis on all sequence images, and obtaining deformed measurement point grid data corresponding to each image to be used as a reference grid for subsequent image analysis;
step 3, reestablishing an image analysis project by using the sequence image to be analyzed, and reading the sequence image to be analyzed into a computer memory by an image analysis program before formal analysis by setting the storage position of the reference grid data file on a computer hard disk in the image analysis parameter and option setting;
step 4, dividing actual measuring point grids within the image range covered by the reference grids on the 1 st image by using a grid measuring point interval, wherein the actual measuring point grids are divided when the reference grid data are acquired and the image is formally analyzed;
step 5, adopting a geometric discrimination method of 'whether a point is in the interior of a quadrangle or on a boundary' for any grid node Pi in the actual measuring point grid, scanning and analyzing all reference grid data in a memory of a computer, and finding a reference grid unit where the measuring point Pi is located;
step 6, calculating the maximum displacement values dmax, dmax = max { dx1, dy1, dx2, dy2, dx3, dy3, dx4 and dy4} of the 4 nodes of the reference grid unit where Pi is located in the x direction and the y direction on the N +1 th image, and then taking the displacement values as the search radius of the Nth image Pi; wherein (x, y) is the pixel coordinate of the measuring point in the analysis image, x represents the abscissa, y represents the ordinate, dx1, dy1, dx2, dy2, dx3, dy3, dx4, dy4 are the displacement values of 4 node coordinates of the grid unit where Pi is located;
step 7, analyzing and obtaining the position coordinates of the measuring point Pi on the (N + 1) th image by adopting a conventional digital speckle correlation method, and calculating the displacement of the measuring point Pi by comparing the position coordinate difference values of the measuring point Pi on the (N + 1) th image and the measuring point Pi on the (N + 1) th image; the conventional digital speckle correlation method is an image analysis method with the same search radius adopted by measuring points;
and 8, circulating the step 5 to the step 7 until all the grid measuring points in the actual measuring point grid are analyzed, and obtaining the displacement data of all the measuring points.
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