CN108986070B

CN108986070B - Rock crack propagation experiment monitoring method based on high-speed video measurement

Info

Publication number: CN108986070B
Application number: CN201810540379.3A
Authority: CN
Inventors: 陈鹏; 童小华; 高飒; 赵程; 谢欢; 刘世杰; 金雁敏; 柳思聪; 许雄; 汪本康
Original assignee: Tongji University
Current assignee: Tongji University
Priority date: 2018-05-30
Filing date: 2018-05-30
Publication date: 2021-06-04
Anticipated expiration: 2038-05-30
Also published as: CN108986070A

Abstract

The invention relates to a rock crack propagation experiment monitoring method based on high-speed video measurement, which comprises the following steps of: 1) acquiring and storing sequence images of the fracture process of the rock to be measured under uniaxial compression by two high-speed cameras in a binocular vision measurement system; 2) in the sequence image analysis process, obtaining sequence homonymous image point coordinates in a speckle interest area through a sub-pixel level tracking matching method, and obtaining time sequence three-dimensional point cloud data of the surface of the rock to be measured under uniaxial compression through a photogrammetric analysis algorithm; 3) and aiming at the time-space sequence analysis of the sequence three-dimensional point cloud, acquiring three-dimensional deformation parameters of the rock crack to be detected, including parameters such as displacement, speed, acceleration and a strain field. Compared with the prior art, the method has the advantages of feasibility, effectiveness, flexibility, reliability and the like.

Description

Rock crack propagation experiment monitoring method based on high-speed video measurement

Technical Field

The invention relates to a non-contact high-speed video measurement scheme for a rock crack propagation experiment, in particular to a rock crack propagation experiment monitoring method based on high-speed video measurement.

Background

In the engineering field, the material properties and safety factors of a material are monitored by testing experiments such as stretching, compression, collision and the like before the material is put into use. In the aspect of measuring the crack extension of the rock, the traditional contact-type measuring instrument is gradually replaced by a non-contact measuring method due to the defects of limited measuring range, few measuring point positions, increased model quality, time and labor consumption in installation and the like. In the research work of optical measurement, a digital correlation algorithm has become a mainstream solution method for mechanical calculation. Many scholars have begun to use digital speckle correlation techniques for the resolution of displacement and strain fields. However, in most experiments, the displacement change of the two-dimensional plane is measured, and the measurement error caused by the non-parallel of the photosensitive plane and the measured object surface can seriously image the measurement result, which is almost uncontrollable. In addition, the three-dimensional digital correlation technology has been widely studied in recent years, but many similar experiments are performed by shooting with a common camera or a high-speed camera with a relatively low performance, and the mutation condition of the measured object cannot be recorded in detail, so that the complete spatial three-dimensional information change of the measurement point position cannot be provided, and the measurement effect of the experiment is affected.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a rock crack propagation experiment monitoring method based on high-speed video measurement.

The purpose of the invention can be realized by the following technical scheme:

a rock crack propagation experiment monitoring method based on high-speed video measurement comprises the following steps:

1) acquiring and storing sequence images of the fracture process of the rock to be measured under uniaxial compression by two high-speed cameras in a binocular vision measurement system;

2) in the sequence image analysis process, calculating sequence homonymous image point coordinates in a speckle interest area by a sub-pixel level tracking matching method, and acquiring time sequence three-dimensional point cloud data of the surface of the rock to be detected under uniaxial compression by a photogrammetric analysis algorithm;

3) and aiming at the time-space sequence analysis of the sequence three-dimensional point cloud, obtaining three-dimensional deformation parameters of the rock crack to be detected, including displacement, speed, acceleration and a strain field.

In the step 1), the high-speed camera in the binocular vision measuring system is an industrial camera, and the synchronization of image acquisition is kept through a synchronization controller.

The two high-speed cameras are horizontally arranged and shoot in an intersection shooting mode, so that the purpose of increasing the image overlapping coverage rate is achieved.

In the step 1), the speckle region is sprayed on the rock to be detected, which specifically comprises the following steps:

the method comprises the steps of polishing the surface of a rock to be detected to be flat, spraying white matte paint on the observation surface, air-drying, randomly and uniformly spraying black matte paint or black ink on the observation surface to form speckles, taking the whole speckle area as a speckle interest area, and taking the speckles as target points.

The step 2) specifically comprises the following steps:

21) three-dimensional calibration of a high-speed camera: simultaneously acquiring an inner orientation element and an outer orientation element of the high-speed camera by a calibration method based on a plane plate;

22) preprocessing a speckle image: selecting a speckle interest area from the sequence image and determining a target point in the interest area;

23) matching points with the same name: taking images simultaneously acquired by two high-speed cameras as matching objects, determining rough point locations of a whole pixel level through normalization correlation coefficients, and determining precise point locations of a sub-pixel level through a least square matching method;

24) target tracking and matching: taking front and rear frame images of the sequence image collected by the high-speed camera as matching objects, and acquiring time sequence two-dimensional image point coordinates of a target point in the sequence image;

25) reconstructing three-dimensional point cloud: and acquiring time sequence three-dimensional point position coordinates of the homonymous target points in the sequence images through forward intersection based on a collinear equation according to the matched image point coordinates of each pair of homonymous points and the calibrated internal orientation element and external orientation element.

In the step 23), the least square matching method takes the maximum normalized correlation coefficient as a target function, considers an affine distortion model between the left and right images, and performs adjustment processing by using the gray information and the position information in the window to finally obtain the accurate matching point.

And in the step 3), displacement data is obtained through the difference of the time-series three-dimensional point position coordinates, and the displacement data is subjected to primary differentiation and secondary differentiation in time to respectively obtain speed data and acceleration data.

Compared with the prior art, the invention has the following advantages:

the invention combines the digital speckle matching method with the high-speed video measurement, can constantly acquire the three-dimensional morphological change of the rock to be measured in a tiny time interval, further provides a set of complete non-contact three-dimensional deformation measurement scheme, and can provide various deformation parameters for quantitative research and analysis of rock crack expansion.

Drawings

Fig. 1 is a technical route diagram of the present invention.

Fig. 2 is a schematic diagram of target point sampling.

Fig. 3 is a diagram illustrating homonym matching.

FIG. 4 is a schematic diagram of tracking and matching the target point in the sequence of images.

FIG. 5 is a high-speed camera network layout

Fig. 6 is a normalized correlation coefficient statistical histogram.

Fig. 7 shows a three-dimensional reconstruction of the rock surface, where fig. 7a shows the three-dimensional point cloud distribution at the initial time and fig. 7b shows the three-dimensional point cloud distribution at 8.285 s.

Fig. 8 shows the three-dimensional displacement field at 8.285s for the rock sample, where fig. 8a shows the X-direction displacement, fig. 8b shows the Y-direction displacement, and fig. 8c shows the Z-direction displacement.

FIG. 9 is the strain field at 8.285s for the rock sample, where FIG. 9a shows Exx strain, FIG. 9b shows Eyy strain, and FIG. 9c shows Exy strain.

Detailed Description

The invention is described in detail below with reference to the figures and specific embodiments.

Examples

A set of detailed high-speed video measuring method is formulated for a rock crack propagation experiment. In the present embodiment, two high-speed cameras are used for stereo observation, and therefore, the hardware device and the related measurement theory may be collectively referred to as a binocular vision measurement system. The system needs hardware such as a high-speed camera, a synchronous controller, a high-speed data acquisition card and the like. The high-speed camera is an important component of the system, belongs to one type of industrial cameras, has the unique advantages of high stability, high frame frequency, high transmission capability, high anti-interference capability and the like, and is particularly suitable for the fields of automatic optical detection, three-dimensional measurement, semiconductor detection, machine vision and the like. In addition, the synchronous controller is used for keeping the synchronous performance of the cameras in the joint measurement. In subsequent data processing, when the three-dimensional spatial coordinates of a certain target point at a certain time are calculated, the homonymous image shot at the time needs to be obtained. If the two cameras are not synchronously controlled, the three-dimensional data can not be resolved in the later period, so that the synchronous control is very important.

The network construction of the binocular vision measuring system also needs to be designed according to the experimental environment. In order to increase the image overlap coverage, the two high-speed cameras should use an intersection photography method. The two high speed cameras should be placed as horizontally as possible just in front of the rock specimen, but the relative attitude in the horizontal plane should not be too inclined. This is because if the tilt posture is too large, the relative perspective distortion of the left and right images increases accordingly, which reduces the sub-pixel image matching accuracy in the later stage. Therefore, before the experiment is carried out, the positions and the postures of the two cameras can be adjusted according to the visual field range of the shot images.

The technical route of the invention is shown in figure 1.

The invention specifically comprises the following steps:

1. target matching and tracking

1.1 image preprocessing

Before image matching, the extraction of interest area and the determination of target point are required, wherein the process of selecting target point is similar to the process of sampling, and the sampling interval can be set according to the requirement of experiment. For example, the first image (i.e., the first frame image) captured by the left camera is used as a reference image, the region of interest is manually selected, and then the target point is selected through a certain length interval (sampling interval). The smaller the sampling interval, the larger the number of target points, and the schematic diagram is shown in fig. 2.

1.2 homologous Point matching

The experiment needs sub-pixel high-precision matching results, so the patent uses the most conventional matching strategy from coarse to fine. The rough matching determines integer pixel matching rough point positions by calculating normalized correlation coefficients, and the fine matching determines sub-pixel precise point positions by a least square matching method. The least square matching method takes the maximum normalized correlation coefficient as a target, takes the deformation of the left image and the right image as affine transformation, and performs adjustment processing by utilizing the gray information and the position information in the window, so that the matching precision of 1/10 or even 1/100 pixels can be achieved. In order to speed up the matching, a local search area needs to be determined. Because the relative tilt postures of the two cameras in the experiment are small, the range difference of the interest areas in the left image and the right image is not large. As shown in fig. 3, the image matching search area may be determined by determining the position relationship of a target point in the left interest area of the left image, and then calculating the approximate range of the same-name point in the right interest area. In addition, the point location of each target point has been determined, so the window size of the target image block and the window size of the search area can be set according to the requirements of the experiment.

1.3 object tracking matching

The target tracking matching is to obtain the image coordinates of each target point sequence, and the sub-pixel level matching method is similar to the matching of the homologous points. The difference is that the matching object is not the left and right images, but the sequence images stored by each camera. Since the homonym matching process has provided the target image blocks, these image blocks should also be used as target images in target tracking matching, and the search area of the next frame can be determined by the target position of the previous frame, and the tracking matching diagram is shown in fig. 4.

Through the process, each target point can obtain the sequence image coordinates, and because the two high-speed cameras acquire and store the shot images synchronously, the homonymous points obtained on the first frame of image still keep the homonymous relation on the time sequence. After three-dimensional reconstruction, the rock surface can form three-dimensional point cloud data under each time scale.

2. Binocular vision three-dimensional reconstruction

2.1 three-dimensional calibration

Use of this patentThe calibration method based on the plane board synchronously acquires the inner orientation element and the outer orientation element of the camera, wherein the inner orientation element not only needs to consider the image principal point (C)_x,C_y) And image distance (f), and taking into account the distortion parameters of the lens and the actual size (S) of the image elements_x,S_y). The distortion parameters of the lens mainly include radial distortion (K)₁,K₂,K₃) And tangential distortion (P)₁,P₂). In addition, the stereo calibration needs to determine not only the inner orientation elements of each camera, but also the outer orientation elements of the cameras. Elements of exterior orientation (α, β, γ, t)_x,t_y,t_z) The conversion relationship between the camera coordinate system and the world coordinate system is mainly reflected. The three-dimensional calibration is the most critical step of the experiment, because the precision of the calibration orientation influences the final experiment result. Therefore, the detection points can be scribed on the plane calibration plate to further verify the calibration precision.

2.2 three-dimensional reconstruction

In the experimental process, the calibrated high-speed camera cannot move, otherwise, the calibration needs to be carried out again. In section 2.1, the inside and outside orientation elements of each camera are determined by stereo calibration, so that three-dimensional point positions of a pair of image point coordinates of a same-name point can be solved by forward intersection based on a collinear equation when the coordinates of the image points of the same-name point are obtained in sequence images acquired by the two cameras. And a large amount of point cloud data is obtained through accumulation in space and time. The collinear condition equation for close-range photogrammetry is as follows:

wherein R ═ a₁ b₁ c₁；a₂ b₂ c₂；a₃ b₃ c₃]。

Under a binocular vision measurement system, 4 equations can be listed for a pair of homonymous points, and 3 unknowns need to be solved, so that adjustment calculation can be carried out according to the principle of least square.

3. Deformation parameter calculation

In the foregoing data processing process, three-dimensional point cloud data at any time can be acquired. Thereby, various deformation parameters such as displacement, speed, acceleration, strain and the like can be formed.

3.1 Displacement, velocity, acceleration

The displacement refers to a distance difference between a current position of the target point at a certain time in the time series and an initial time of the target point. This makes it possible to know that the displacement of the tracking point at the initial time is 0. For example, the displacement formula of the three-dimensional point displacement data is as follows:

wherein the content of the first and second substances,

and

respectively representing the displacement of the target point in the X and Y directions at time n; x₁，Y₁And Z₁Respectively representing coordinate values of the target point in X, Y and Z directions at the initial moment; x_n，Y_nAnd Z_nCoordinate values of the target point at time n in the X, Y and Z directions are respectively indicated.

Since the acquisition frame rate of the high-speed camera is fixed and unchanged, the time difference between two adjacent frames can be obtained. If the displacement data are subjected to first differentiation and second differentiation on time respectively, corresponding speed data and acceleration data can be obtained. Therefore, all target points on the rock surface are processed to form a displacement field, a velocity field and an acceleration field.

3.2 strain value

In the strain analysis, the strain value of a certain point can be calculated by surrounding displacement data, so that each target point can be taken as the center, and a displacement window is selected around the target point to carry out strain value calculation. Within this displacement window, its displacement profile can be considered linear. The relationship between the displacement and the coordinate can be expressed as:

where u (i, j) and v (i, j) are the displacement values of point (i, j) under the displacement window, a_i＝0,1,2And b_i＝0,1,2Is the undetermined coefficient of the polynomial.

In the case of small deformations, the strain component can be solved by:

the size of the displacement window can be determined according to requirements, and if the window size is larger, a high-order polynomial can be used for representing displacement distribution. However, generally, the size of the displacement window should be chosen moderately by the experimental requirement, and from the equation (3) and the expression (4), the plane coordinates of at least three points are known to solve the strain component. Under uniaxial compression, more attention is paid to the deformation that occurs in the rock surface. Therefore, in the three-dimensional point cloud generated in the previous stage, only the strain in the spatial plane thereof may be considered.

The observation object of the experiment is a rock sample block for carrying out crack propagation measurement, the sample block is prepared by mixing medical gypsum and water according to a certain proportion, and the sample block is processed into a cube with the side length of 70mm through a die. In order to meet the measurement requirement, speckles need to be sprayed to increase observation points, and the specific process is as follows: (1) polishing the observation surface of the sample block to be flat; (2) spraying white matte paint on the observation surface, and air-drying; (3) the speckle image is formed by randomly and uniformly spraying black matte paint or black ink on the observation surface. In addition, as shown in fig. 5, the two high-speed cameras form an intersection photogrammetry mode, and a high-power halogen lamp is used for supplementing light to the experimental object, so that the shooting quality of the image is ensured.

In the experiment, two high-speed cameras form binocular vision to carry out three-dimensional measurement on the rock sample block, the frame frequency is set to be 400 frames/second, and dynamic data with the frequency of 40Hz can be accurately measured, namely, the form change of the sample is described by 10 times of image data. The size of the image taken by the high-speed camera is 2304 multiplied by 1720 pixels, and the pixel size is 7 um. For precise measurement, the high-speed camera is equipped with a 50mm fixed-focus lens.

By calibration, the inner orientation element and the opposite outer orientation element of the two high-speed cameras can be obtained, and the results are shown in table 1. Through the detection point check defined on the plane plate, the calibration and orientation precision can reach 0.01mm, and the back projection error is superior to 0.2 pixel.

TABLE 1 interior orientation element and exterior orientation element of high-speed camera

As described above, image preprocessing is required before matching, and the sampling interval of the target point in the interest region is 5 pixels, so that 17423 target observation points are generated and are regularly arranged according to the number of 131 columns and 133 rows. A target image block is determined for all target point locations, with the size of the target window set to 30 pixels and the size of the homonymous search window set to 50 pixels. After the homonymous points are matched, a normalized correlation coefficient statistical graph can be obtained, as shown in fig. 6, it can be known from the graph that the correlation coefficient of 14084 for the homonymous points is in the value range of 0.9-1.0, and the correlation coefficient of 3315 for the homonymous points is in the value range of 0.8-0.9. Because the value of the correlation coefficient above 0.8 is the high image correlation, almost all target points can be matched with the same name point, and the matching precision is high. However, the value of some point correlation coefficients is small, which is caused by low matching accuracy due to less texture information of the individual target image blocks. This case can be solved for these bad points by means of numerical interpolation.

After tracking and matching are carried out on all target points, the coordinates of image points with the same name in the sequence can be obtained, and then three-dimensional point cloud data of the rock surface at any time can be obtained through three-dimensional reconstruction. The three-dimensional deformation state of the rock surface can be visually seen from the three-dimensional point cloud data as shown in fig. 7.

Three-dimensional displacement fields and strain fields can be formed by deformation parameter calculation, as shown in fig. 8 and 9, respectively. In the compression process, cracks are generated at the top of the rock, so that the rock is damaged, and large deformation is generated. Furthermore, as is evident from the strain field, the tendency of the rock to fracture will extend downward.

The experiment for measuring the rock crack extension by using a high-speed camera with the frame frequency of 400 frames per second introduces a binocular vision three-dimensional reconstruction method in detail, and provides a speckle image matching strategy and a matching method at the same time, thereby providing a complete three-dimensional deformation measurement scheme.

1) The scheme measures the dynamic changes of the displacement field and the strain field of an experimental object in the fracture process, wherein the highest spatial positioning precision of the target point position can reach 0.01mm, and the feasibility and the effectiveness of the whole set of measuring scheme in the rock fracture experiment are verified.

2) In the speckle image matching process, the selection interval of the target point and the target image block window can be determined according to the experiment requirement, so that the flexibility of data analysis is improved.

3) The high-speed video measurement technology can measure high-speed moving objects by means of unique high frame frequency characteristics, and a high-speed camera with the acquisition frame frequency of 400 frames/second can accurately measure dynamic data with the frequency of 40 Hz.

4) The patent also elaborates the solving process of the deformation parameters in detail, and provides reliable experimental data for further research and analysis work.

Claims

1. A rock crack propagation experiment monitoring method based on high-speed video measurement is characterized by comprising the following steps:

2) in the sequence image analysis process, calculating sequence homonymous image point coordinates in a speckle interest area by a sub-pixel level tracking matching method, and acquiring time sequence three-dimensional point cloud data of the surface of the rock to be detected under uniaxial compression by a photogrammetric analysis algorithm, wherein the method specifically comprises the following steps:

23) matching points with the same name: specifically, a rough point location of a whole pixel level is determined by calculating a normalized correlation coefficient, then an accurate point location of a sub-pixel level is determined by a least square matching method, wherein the maximum normalized correlation coefficient of the least square matching method is the maximum of a target function, an affine distortion model between a left image and a right image is considered, adjustment processing is carried out by utilizing gray information and position information in a window, and finally an accurate matching point location is obtained;

25) reconstructing three-dimensional point cloud: according to the matched image point coordinates of each pair of homonymous points and the calibrated inner orientation element and outer orientation element, acquiring time sequence three-dimensional point position coordinates of homonymous target points in the sequence images through forward intersection based on a collinear equation;

3) and aiming at the time-space sequence analysis of the sequence three-dimensional point cloud, acquiring three-dimensional deformation parameters including displacement, speed, acceleration and a strain field of the rock crack to be detected, acquiring displacement data by the difference of the time-sequence three-dimensional point location coordinates, and performing primary differentiation and secondary differentiation on the displacement data in time to respectively acquire speed data and acceleration data.

2. The method for monitoring the rock fracture propagation experiment based on the high-speed video measurement as claimed in claim 1, wherein in the step 1), the high-speed camera in the binocular vision measurement system is an industrial camera, and the image acquisition is kept synchronous through a synchronous controller.

3. The method for monitoring the rock fracture propagation experiment based on the high-speed video measurement as claimed in claim 2, wherein the two high-speed cameras are horizontally placed and photographed in an intersection manner, so as to achieve the purpose of increasing the image overlapping coverage rate.

4. The method for monitoring the rock crack propagation experiment based on the high-speed video measurement as claimed in claim 1, wherein in the step 1), a speckle region is sprayed on the rock to be measured, specifically: