CN110146024B - Double-precision displacement measurement method based on self-adaptive search - Google Patents
Double-precision displacement measurement method based on self-adaptive search Download PDFInfo
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- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
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Abstract
The invention relates to the technical field of ancient building protection, and provides a double-precision displacement measuring method based on self-adaptive search in order to solve the problem that a colored drawing beam is damaged because a target needs to be arranged on the colored drawing beam in the conventional deformation measurement of the colored drawing beam, wherein the double-precision displacement measuring method based on self-adaptive search comprises the following steps: an image acquisition step: acquiring an image of a measured object and generating image information; a storage step: storing the calculation rule; a calculation step: and calculating the image information according to the calculation rule to obtain the displacement.
Description
Technical Field
The invention relates to the technical field of historic building protection, in particular to a double-precision displacement measurement method based on self-adaptive search.
Background
At present, in the deformation measurement of a colored drawing beam of an ancient building, measurement equipment such as a level gauge and a total station is mainly adopted, and when the level gauge or the total station is used for measurement, a target needs to be arranged on the colored drawing beam to finish high-precision measurement. Because the setting of target on the colored drawing roof beam needs target and colored drawing roof beam in close contact with, can make colored drawing roof beam surface appear the damage, set up the target on the colored drawing roof beam in addition and also can influence the pleasing to the eye of colored drawing roof beam. Therefore, the invention provides a non-contact measuring method to avoid the problem that the colored drawing beam is damaged due to the fact that the target is arranged on the colored drawing beam.
Disclosure of Invention
The invention aims to provide a double-precision displacement measurement method based on self-adaptive search, so as to solve the problem that in the existing colored drawing beam deformation measurement, a target needs to be arranged on a colored drawing beam to damage the colored drawing beam.
The basic scheme provided by the invention is as follows: the double-precision displacement measurement method based on the self-adaptive search comprises the following steps:
an image acquisition step: acquiring an image of a measured object and generating image information;
a storage step: storing the calculation rule;
and (3) calculating: calculating the image information according to a calculation rule to obtain a displacement;
wherein: the calculation rules comprise a parameter algorithm, a coefficient threshold algorithm, a size algorithm, a local exhaustive search method, a gradient method and a weight operation method;
the calculating step comprises:
and (3) parameter calculation: calculating two image information of a measured object before and after a period of time according to a parameter algorithm to obtain a parameter value;
coefficient threshold calculation step: calculating the parameter value according to a coefficient threshold algorithm to obtain a coefficient threshold;
a subset selection step: calculating the size of the subset meeting the coefficient threshold according to a size algorithm;
and searching for the integral pixel displacement: the method comprises the steps of obtaining an initial value of a whole pixel according to local exhaustive search on an initial calculation subset;
and (3) searching initial values of sub-pixel displacement: the method comprises the steps of determining a sub-pixel region subset according to an integer pixel initial value and a subset selection method, and respectively calculating sub-pixel displacement in the sub-pixel region subset for integer pixel initial value points at two ends of the sub-pixel region subset according to a gradient method;
and calculating a displacement accurate value: and performing weight calculation on the sub-pixel displacement to obtain a displacement accurate value.
As shown in fig. 7, the portion inside the dashed line frame in the figure is the adaptive subset size calculation method portion, and the optimal subset size at the point to be measured is selected through multiple times of loop calculation.
Description of the drawings: in the scheme, the deformation displacement is calculated based on a DIC data image processing method, and in a theoretical system of the DIC digital image processing method, the gray information of the surface of the material is generally considered to generate synchronous displacement deformation with the material, and a mathematical relation between images of a measurement area before deformation and images after deformation is established based on the assumption. DIC digital image correlation method obtains the digital image of the measured plane object surface before and after deforming through the camera, and then obtain the displacement of each point of measured object surface through the corresponding image subset in the digital image before and after the matching deformation, namely obtain the digital image of the measured object before and after the deformation through the camera or camera, then obtain the displacement that the numerical value image moves through the resolving method, wherein the resolving method is to choose the subset size manually through the choice of the correlation function and shape function at first, then continue to twist the displacement of the whole pixel, the sub-pixel is located and searched, finish the deformation measurement through the calibration of the displacement of the pixel finally.
The basic scheme has the working principle and the beneficial effects that: compared with the existing measurement mode, the method has the advantages that 1, displacement is obtained by operating the image information of the obtained colored drawing beam, non-contact measurement is realized, and the problem that the colored drawing beam is damaged due to direct contact with the colored drawing beam in the measurement process is solved;
2. at present, although the problem of low search accuracy and low calculation efficiency is overcome by an adaptive path search method for calculating integer pixel displacement, the problem of unknown initial value of displacement exists because the first displacement search point is assumed to start from the original point, and thus, a plurality of wrong local optimal solutions are easily caused, and the final calculation result is inaccurate. In the scheme, the whole pixel displacement is calculated by adopting a local exhaustive search method, and the problem of existence of a plurality of wrong local optimal solutions is solved through the determined accurate initial displacement, so that the accuracy of a calculation result is ensured.
3. Considering that when selecting the computation subset, in the conventional selection method, a square subset in the x direction and the y direction in the speckle pattern is generally selected, as shown in fig. 6, however, for the ancient architectural colored drawing beam, due to the severe difference of the pattern gradients in the x direction and the y direction, the sizes of the subsets in the x direction and the y direction are unreasonable, which not only increases the computation amount and reduces the computation speed, but also reduces the computation accuracy of the point to be measured if the size of the subset is too large for the uneven displacement field with unreasonable sizes in the x direction and the y direction. Therefore, in the scheme, when the subset size is determined, the subset size which is calculated by a size algorithm and meets a coefficient threshold value is selected as the initial sizes Mx and My of the selected initial subsets in the x direction and the y direction around the point to be measured, so that the optimal subset size of the point to be measured is determined.
The first preferred scheme is as follows: preferably, the method further comprises the following timing steps: sending a starting signal according to the stored timing information; the control step is as follows: and after receiving a starting signal, controlling the image acquisition step to start. Has the advantages that: in the scheme, the automatic acquisition of the image of the measured object is realized through the arrangement of the timing module and the control module, and the operation is convenient.
The preferred scheme II is as follows: preferably, the calculating step further calculates the obtained displacement accuracy value and a displacement threshold value, and sends alarm information when the calculated displacement accuracy value is greater than or equal to the displacement threshold value. Has the advantages that: the measurement of considering to colored drawing roof beam deformation is the damage problem of fracture or damage appears because deformation is too big in order to avoid the colored drawing roof beam, consequently, in this scheme, through setting up the displacement threshold value, when calculating the displacement accurate value more than or equal to displacement threshold value of colored drawing roof beam, that is to say that the colored drawing roof beam faces the problem of damage, alarm information's sending then can in time remind, guarantee that the staff can in time carry out safeguard measure to the colored drawing roof beam, in order to avoid the colored drawing roof beam to damage.
The preferable scheme is three: preferably, the method further comprises the following input steps: and inputting a displacement threshold value and storing the displacement threshold value. Has the advantages that: considering that the critical points of deformation are different for different colored drawing beams, namely the displacement threshold values are different, the scheme is further provided with an input module, so that the displacement threshold values can be conveniently input by workers, and the reminding accuracy is ensured.
The preferable scheme is four: preferably, as a second preferred scheme, the displacement threshold includes a plurality of groups of mutually matched colored drawing beam types and critical thresholds, and further includes an input step: inputting the type of the colored drawing beam; matching: and matching a critical threshold value serving as a current displacement threshold value of the colored drawing beam according to the type of the input colored drawing beam. Has the advantages that: considering that the critical points of deformation are different for different colored drawing beams, that is, the displacement threshold values are different, therefore, the displacement threshold values in the scheme include the types of the colored drawing beams and the matched critical threshold values, an input module is further arranged, so that the type of the colored drawing beams can be conveniently input by a worker, and the matching module automatically matches the critical threshold values of the current colored drawing beams for calculation, thereby ensuring the accuracy of reminding.
The preferable scheme is five: preferably, the image acquisition step uses a CCD camera. Has the beneficial effects that: the CCD camera has the characteristics of small volume, light weight, no influence of a magnetic field and vibration and impact resistance.
The preferable scheme is six: preferably, in the image acquisition step, the optical axis of the CCD camera is opposite to the centroid of the object to be measured and is perpendicular to the pattern surface of the object to be measured. Has the advantages that: in the scheme, the optical axis of the image acquisition module is arranged right opposite to the measured object, so that the accuracy of the acquired image is ensured, and the accuracy of the displacement accurate value obtained by calculation is ensured.
The preferable scheme is seven: preferably, the method further comprises the following processing steps: and performing edge removal processing on the image information, wherein the image information subjected to the edge removal processing is calculated in the calculating step. Has the beneficial effects that: in the scheme, in order to avoid the influence of the boundary of the structural edge region on the displacement recognition result, the generated image information is subjected to edge removal processing, and the calculation module calculates the processed image information, so that the accuracy of the final displacement accurate value is improved.
The preferable scheme is eight: preferably, in the processing step, edge removing processing is performed on left and right edges of the image information. Has the advantages that: considering that the left and right ends of the colored drawing beam are usually fixed on the column after being fixed, the scheme only carries out edge removing processing on the left and right edges of the image information, and reduces the processed amount.
The preferable scheme is nine: preferably, the method further comprises the following steps: inputting an edge processing size, and performing edge removing processing on the image information according to the edge processing size in the processing step. Has the advantages that: the input module is convenient for workers to manually input the edge processing size, and operation is convenient.
Drawings
FIG. 1 is a block diagram of a measurement system used in a first embodiment of a dual-precision displacement measurement method based on adaptive search according to the present invention;
FIG. 2 shows a test loading device and a measuring device according to an embodiment;
FIG. 3 (a) is the initial working condition of the Hexi color painting beam;
FIG. 3 (b) shows the final working condition of the Hexi color painting beam;
FIG. 4 shows Hexi color painting beam pixel calibration data;
FIG. 5 shows deflection recognition results of three monitoring measuring points DIC of Hexi color painting beam under working condition 5;
FIG. 6 is a schematic diagram of adaptive subset size parameters;
FIG. 7 is a process flow of adaptive process selection for integer-pel and sub-pel search subsets;
FIG. 8 is a schematic diagram of a search process;
FIG. 9 is a schematic diagram of a gradient method;
FIG. 10 (a) shows the sub-pixel shift calculation result with the initial value point of the integer pixel being 0 under the influence of noise;
FIG. 10 (b) shows the sub-pixel shift calculation result with the initial value point of integer pixel being 1 under the influence of noise;
FIG. 11 is a schematic diagram of fitting the standard deviation SD of noise and the weight coefficient a at eight sub-pixel displacement points;
FIG. 12 is a cubic spline interpolation curve of sub-pixel displacement and weight coefficient under different noise conditions;
FIG. 13 is a comparison of the precision of the integer pixel displacement calculation method;
FIG. 14 is a diagram of a double-precision algorithm and stability verification;
FIG. 15 is a Hexi color painting test beam pattern;
FIG. 16 is a schematic of the experimental loading.
Detailed Description
The following is further detailed by way of specific embodiments:
reference numerals in the drawings of the specification include: the device comprises a loading jack 1, a pressure sensor 2, a dial indicator 3, light 4 and a CCD camera 5.
The double-precision displacement measurement method based on the self-adaptive search comprises the following steps:
an image acquisition step: acquiring an image of a measured object and generating image information;
a storage step: storing the calculation rule;
and (3) calculating: calculating the image information according to a calculation rule to obtain a displacement;
wherein: the calculation rules comprise a parameter algorithm, a coefficient threshold algorithm, a size algorithm, a local exhaustive search method, a gradient method and a weight operation method;
the calculating step comprises:
and (3) parameter calculation: calculating two image information of a measured object before and after a period of time according to a parameter algorithm to obtain a parameter value;
coefficient threshold calculation step: calculating the parameter value according to a coefficient threshold algorithm to obtain a coefficient threshold;
a subset selection step: calculating the size of the subset meeting the coefficient threshold according to a size algorithm;
and searching for the integral pixel displacement: the method comprises the steps of obtaining an initial value of a whole pixel according to local exhaustive search on an initial calculation subset;
searching the initial value of the sub-pixel displacement: the method comprises the steps of determining a sub-pixel region subset according to an integer pixel initial value and a subset selection method, and respectively calculating sub-pixel displacement in the sub-pixel region subset for integer pixel initial value points at two ends of the sub-pixel region subset according to a gradient method;
and calculating a displacement accurate value: carrying out weight calculation on the sub-pixel displacement to obtain a displacement accurate value;
an alarming step: and the calculation step is also used for calculating the obtained displacement accurate value and the displacement threshold value, and sending alarm information when the calculated displacement accurate value is greater than or equal to the displacement threshold value.
Specifically, the method also comprises a timing step: sending a starting signal according to the stored timing information; the control steps are as follows: after receiving a starting signal, controlling the image acquisition step to start;
an input step: inputting a displacement threshold value and storing the displacement threshold value; the displacement threshold comprises a plurality of groups of mutually matched colored drawing beam types and critical thresholds, and further comprises the following input steps: inputting the type of the colored drawing beam; matching: matching a critical threshold value serving as a displacement threshold value of the current colored drawing beam according to the type of the input colored drawing beam; the treatment steps are as follows: the method comprises the following steps of performing edge removing processing on image information, and calculating the image information subjected to the edge removing processing in the calculating step, specifically comprising the input step of: inputting an edge processing size, and performing edge removing processing on the image information according to the edge processing size in the processing step.
Based on the above measurement method, as shown in fig. 1, the embodiment further discloses a measurement system, which includes an image acquisition module for acquiring an image of a measured object and generating image information;
the storage module is used for storing the calculation rule and the displacement threshold value;
the input module is used for inputting timing information and edge processing size, and the storage module stores the timing information;
the processing module is used for removing edges of the image information; specifically, the processing module performs edge processing on the left edge and the right edge of the image information according to the edge processing size;
the calculation module is used for calculating the image information after the edge removal processing according to the calculation rule to obtain the displacement;
specifically, the calculation rule comprises a parameter algorithm, a coefficient threshold algorithm, a size algorithm, a local exhaustive search method, a gradient method and a weight operation method;
the timing module is prestored with timing information and used for sending a starting signal according to the timing information;
the control module is used for controlling the image acquisition module to acquire the image of the object to be detected and generate image information after receiving the starting signal;
the calculation module comprises:
a parameter calculation unit: the system is used for calculating two image information of a measured object before and after a period of time according to a parameter algorithm to obtain a parameter value; if the timing information is T, the two pieces of front and back image information are image information at time T and image information at time T + T;
coefficient threshold value calculation unit: the coefficient threshold value is obtained by calculating the parameter value according to a coefficient threshold value algorithm;
a subset selection unit: the size of the subset meeting the coefficient threshold is calculated according to a size algorithm;
integer pixel displacement search unit: the method comprises the steps of obtaining an initial value of a whole pixel according to local exhaustive search on an initial calculation subset;
a sub-pixel displacement initial value searching unit: the method comprises the steps of determining a sub-pixel region subset according to an integer pixel initial value and a subset selection method, and respectively calculating sub-pixel displacement in the sub-pixel region subset according to gradient methods for integer pixel initial value points at two ends of the sub-pixel region subset;
a displacement accuracy value calculation unit: carrying out weight calculation on the sub-pixel displacement to obtain a displacement accurate value;
the calculation module is also used for calculating the obtained displacement accurate value and the displacement threshold value;
and the alarm module is used for sending alarm information when the calculated displacement accurate value is greater than or equal to the displacement threshold value.
The above size algorithm is as follows:
(1)
(2)
function[TH]＝threshold(imgT1,imgT2,imgR,m,n,Srr,Scr,sx,sy,SD)
% imgT1: pretreatment of pictures 1
% imgT2: pretreatment picture 2
% imgR: reference image
% m: calculating the y coordinate of the subset center point by referring to the image
% n: calculating subset center point x coordinate by reference image
% SD: setting standard deviation of displacement identification error
% Srr: integer pixel search reference image computation subset y-direction radius
% Scr: integer pixel search reference image computation subset x-direction radius
% sx: reference image computation subset internal x-direction computation interval
% sy: reference image computation subset internal y-direction computation interval
[H]＝crossCorrelation(imgR,m,n,Srr,Scr,sx,sy)；
[Dn]＝varianceNoise(imgT1,imgT2)；
TH＝H*Dn/(SD^2)
(3)
clc,clear；
imgT t1= double (imread ('t 1. Tif')); % pretreatment of Picture 1
imgT t2= double (imread ('t 2. Tif')); % pre-processed Picture 2
imgR = double (immead ('P _1. Tif')); % reference image to be calculated
SD =0.01; % displacement identification error (standard deviation, unit: pixel)
m＝1024；
n＝1024；
[Ssrr,Sscr]＝subpixelsubsetsize(imgT1,imgT2,imgR,m,n,SD)
(4)
For convenience of description, the above calculation schemes are collectively referred to as an adaptive search double-precision gradient DIC method.
In the above process, when the deformation displacement is calculated, based on the DIC data image processing method, in a theoretical system of the DIC digital image processing method, it is generally considered that the gray information of the material surface will generate displacement deformation in synchronization with the material, and it is based on this assumption that a mathematical relationship between the images of the measurement area before and after deformation is established. DIC digital image correlation method obtains the digital image of the measured plane object surface before and after deforming through the camera, and then obtain the displacement of each point of measured object surface through the corresponding image subset in the digital image before and after matching the deformation, namely obtain the digital image of the measured object before and after deforming through the camera or camera, then obtain the displacement that the numerical value image moves through the resolving method, wherein the resolving method is to choose the subset size manually through the choice of the correlation function and shape function at first, then continue to twist the displacement of the whole pixel, sub-pixel displacement search, finish the deformation measurement through the calibration of the displacement of the pixel finally.
In the scheme, the initial displacement vector in the traditional self-adaptive search method is updated through an exhaustion method, so that the accurate initial displacement vector is obtained. The searching method comprises two processes, firstly, carrying out a local exhaustive search on a first searching point according to a given searching area in the same image to find out accurate displacement; then, the accurate displacement information of the first point is used as an initial value, recursive self-adaptive path search is carried out on other adjacent points, and the displacement information of other measuring points in the image is calculated. The specific solving process is as follows:
the first step is as follows: and based on a given search area, carrying out local exhaustion method search displacement on a first point calculation point in the image to obtain an initial displacement vector (u, v).
The second step is that: and taking the max (u, v) obtained by the first step of calculation as the calculation step length of the adjacent points to be measured, and respectively carrying out matching calculation on four prediction points which are up, down, left and right and are away from the step length of the max (u, v) of the points and the displacement vector points obtained by the first step of calculation, wherein the five prediction points are totally (only four points if the prediction points are superposed) to obtain the optimal matching position.
A third part: searching a small diamond search mode around the optimal matching position obtained in the second step, and if the optimal matching position is the center of the small diamond, ending the search, wherein the point is the final displacement search point; otherwise, moving the small diamond center to a new optimal matching point, and repeatedly performing small diamond search pattern search until the optimal matching point is the small diamond center to obtain the point displacement value.
The fourth step: and repeating the second step to the third step for the next adjacent point until the searching of all the points to be detected is completed.
The search process is schematically illustrated in fig. 8.
In this embodiment, the adaptive size subset using the partial exhaustive search method is significantly smaller than the current calculation number of the square subset, and the calculation speed is increased without decreasing the accuracy, and the results are shown in table 1, which is shown in the example of comparing with the square.
Attached table 1. Adaptive size subset vs. square subset
The comparison with the medium-coarse-fine search method, the three-step search method and the diamond search method is verified, and the comparison result is shown in fig. 13.
The process of calculating the sub-pixel displacement in the sub-pixel region subset by using the gradient method specifically comprises the following steps:
as shown in fig. 9, the sub-pixel displacement accurate value position is denoted as E, the coordinate thereof is denoted as x, the sub-pixel displacement calculated from the initial value point obtained by the integer pixel search is denoted as a, and the coordinate thereof is denoted as x 1 And the sub-pixel displacement calculated by the initial value points of the adjacent integer pixels is marked as B, and the coordinate is x 2 (ii) a The coordinate x of the sub-pixel displacement accurate value E is defined by x 1 And x 2 Calculated, as shown in formula 3, where a is the weight coefficient at the position x of the corresponding sub-pixel displacement accurate value, but since the accurate position cannot be known, the weight coefficient is used herein
The weight coefficient alpha is approximate to the weight coefficient at the sub-pixel displacement accurate value position x, wherein x is obtained by calculation of formula 4, and the point is selected for approximate substitution because the sub-pixel displacement closer to the integer pixel initial value point has higher calculation precision, and the displacement accurate value can be more approximately substituted for calculation of the weight coefficient. According to the sub-pixel displacement data calculated in fig. 10, a weight coefficient a at each sub-pixel displacement control point where the interval between 0 and 0.1pixel is 0.1pixel under different noise conditions is calculated by formula 3 i I =0.1,0.2,0.3 \ 82301; wherein, when the sub-pixel displacement is assumed to be 0pixel, the sub-pixel displacement calculated at the initial value point of the corresponding whole pixel is the accurate value. Therefore, it is determined by theoretical analysis that alpha is 0 ＝0,ɑ 1 Where the sub-pixel displacement is 0.5pixel, the average errors of the dot displacements calculated from two directions are opposite to each other, and therefore there is a 0.5 =0.5; then, an exponential function is adopted to carry out alpha on the weight coefficient alpha of the remaining eight sub-pixel control points i Fitting with the image noise standard deviation SD using equation 5, where c 1 ,c 2 ,c 3 The results are shown in FIG. 11 for the fitting coefficients.
After 11 sub-pixel displacement weight coefficient control points with 0.1pixel intervals of 0 to 0.1pixel under different noise conditions are obtained, cubic spline interpolation is performed on the weight coefficients at the 11 control points according to formula 6, so that a weight coefficient a of an arbitrary sub-pixel displacement point under an arbitrary noise standard deviation condition can be obtained, as shown in fig. 12 for example.
x＝(1-a)·x 1 +a·x 2 (3)
Wherein SD is the standard deviation; c. C 1 ,c 2 ,c 3 Are fitting coefficients.
The above is the analysis result for the x direction. The same is true for the y-direction sub-pixel displacement calculation.
For convenience of description, the sub-pixel displacement calculation method is referred to as a double-precision algorithm.
The calculation method not only keeps the calculation stability of the gradient method in a noise environment, but also effectively overcomes the defect of weak noise resistance of the traditional gradient method in calculation precision. The results of the comparison of the accuracy and stability by the gradient method with those by the N-R method are shown in FIG. 14.
The specific implementation process is as follows: in this embodiment, taking a Hexi colored drawing beam as an example, as shown in FIG. 2, the size of the colored drawing wooden beam member is 1400mm × 1400mm × 50mm, and the size of the colored drawing image on the beam surface is 1300mm × 100mm, so as to avoid the influence of the boundary of the pattern of the edge area of the structure on the displacement recognition result, the positions close to the left and right edges are removed by 50mm, and therefore, the size of the colored drawing of the loading area is selected to be 1200mm × 100mm. The loading mode of the simply supported colored drawing beam is selected as two-point loading of the distribution beam, and the loading distance is 400mm. The distance between the supports is 1200mm, and the loading control mode is mid-span deflection control. In order to better compare the identification effect of DIC, three dial indicators are respectively placed at the middle of the wood beam span and at two sides of 300mm away from the middle of the wood beam span in the example to measure the deflection of the painted beam, and the value measured by the dial indicators is used as the real displacement. Meanwhile, a pressure sensor is arranged below the loader to measure real-time pressure, and the experimental device is shown in the attached figure 2. In the actual measurement environment, the light intensity has a large influence on the calculation accuracy of the DIC displacement measurement method, so that different light intensities are set under the same displacement working condition in the example to simulate the measurement condition under the complex light intensity in the actual measurement process, and the experimental illumination condition is divided into 1 and 2 stages and respectively corresponds to the weak light intensity and the strong light intensity.
Firstly, preloading is carried out on the Hexi color painting beam, and formal loading is carried out after all the Hexi color painting beam is determined to be normal.
After the experimental environment is built, each beam adopts a CCD camera to shoot two same pictures before formal loading so as to perform adaptive subset selection of DIC operation. Six working conditions (including initial working conditions) are set for each beam according to the maximum loading displacement, and the beams are divided into 3 comparison groups according to the light intensity under each working condition to carry out image shooting. According to the regulation of GB 50005-2017 'design Standard of Wood Structure', the maximum deflection of a roof beam in a wood structure during bending span is less than l/250,l calculated span, and l =1200mm in the experiment. The maximum deflection working condition of the colored drawing beam span arranged in the test is 12mm, which is 1/100 of the span of the simply-supported wood beam and is 2.5 times of the standard limit value. The working condition number and the working condition displacement are shown in the attached table 2 by taking the maximum deflection of the midspan of 12mm as the reference.
Attached table 2 working condition number and working condition displacement
The experimental loading device and the measuring device are arranged as shown in the attached figure 2, gaskets are arranged between the simply-supported support and the loading points of the two distribution beams and between the colored drawing beams to prevent the local crushing of the wood beam, three dial gauges are arranged at the lower part of the wood beam to measure deflection at different positions, and the distribution beam, the loading jack and a pressure sensor (model: CFBLZ S-shaped tension and compression sensor, model: SDY2202 type static strain gauge) are arranged at the upper part of the wood beam. The CCD camera (model GZL-CL-41C6M-C, image resolution 2048 x 2048 pixels) was placed on a tripod about 4 meters directly in front of the painted beam with its optical axis aligned with the centroid of the beam and perpendicular to the painted pattern plane, the camera data line was connected to a computer, and image acquisition was performed with software. Two groups of white light sources with adjustable brightness are adopted for illumination in the experimental process.
The loading and measuring steps are as follows:
1) The component, the jack and the pressure sensor are placed in place and then pre-pressurized, the pressure is gradually applied from small to large, so that the components are tightly contacted, meanwhile, the reading of the pressure sensor acquisition instrument is reset to zero and recorded, the illumination condition is set to be 2, and the working condition of 0 in the state is defined. At this time, a CCD camera is adopted to shoot two initial test images for image noise variance analysis and adaptive subset size selection. Then, pictures are taken under the illumination condition and the illumination condition 1 respectively, and three dial indicator initial values are recorded.
2) And (3) performing displacement loading under each working condition by using a loading device corresponding to the mid-span displacement control value under each working condition of the attached table 2, recording the reading of the pressure sensor and the readings of the three dial indicators after each stage of loading is stable, and simultaneously shooting two groups of colored drawing beam deformation images under the illumination condition by adopting a CCD (charge coupled device) camera.
3) And checking and sorting the acquired data after the loading is finished, and slowly unloading the loader after the data are checked to be correct.
The initial condition and final loading condition of the wooden beam are shown in fig. 3 (a) and 3 (b), and the loading data records of the wooden beam are shown in the attached table 3.
Attached table 3, hexi color painting beam loading data
The method comprises the steps of firstly preloading a test beam, and then carrying out formal loading after all the test beams are determined to be normal.
After the experimental environment is built, each beam adopts a CCD camera to shoot two same pictures before formal loading so as to perform adaptive subset selection of DIC operation. Six working conditions (including initial working conditions) are set for each beam according to the maximum loading displacement, and the beams are divided into 3 comparison groups according to the light intensity under each working condition to carry out image shooting. According to the regulation of GB 50005-2017 'design Standard of Wood Structure', the maximum deflection of a roof girder in a wood structure in a bending span is less than l/250,l calculation span, and l =1200mm in the experiment. The maximum deflection working condition of the colored drawing beam span arranged in the test is 12mm, which is 1/100 of the span of the simply-supported wood beam and is 2.5 times of the standard limit value. The working condition number and the working condition displacement are shown in the attached table 2 by taking the maximum deflection of the midspan of 12mm as the reference.
The experimental loading device and the measuring device are shown in the attached figure 2, gaskets are arranged between a simply-supported support and two distributing beam loading points and between colored drawing beams to prevent the local crushing of the wood beams, three dial gauges are arranged at the lower parts of the wood beams to measure deflection at different positions, and the distributing beams, the loading jacks and pressure sensors (model: CFBLZ S-shaped tension and compression sensors, model: SDY 2202-type static strain gauges) are arranged at the upper parts of the wood beams. The CCD camera (model GZL-CL-41C6M-C, image resolution 2048 x 2048 pixels) was placed on a tripod about 4 meters directly in front of the painted beam with its optical axis aligned with the centroid of the beam and perpendicular to the painted pattern plane, the camera data line was connected to a computer, and image acquisition was performed with software. Two groups of white light sources with adjustable brightness are adopted for illumination in the experimental process.
Meanwhile, a DIC method (Coorse-Fine Gradient DIC, CG-DIC) based on a manual selection subset, a Coarse-Fine integer pixel Search method and a Gradient sub-pixel displacement Search method is compared with an Adaptive Search Double-Precision Gradient DIC (AD-DIC) proposed by the scheme.
By analyzing the image data of the Hexi color painting beam, the pixels with the corresponding length in the image corresponding to the actual beam height of 100mm are shown in the attached figure 4, and then the quantitative relation between the pixel displacement and the real displacement in each group of experiments can be calculated by a formula 7.
For the analysis of the calculation precision, the deflection data in the pattern width range of the colored drawing beam at the corresponding position of the dial indicator is identified by adopting a DIC method and is compared with the dial indicator data. And calculating the Standard Deviation (SD) of deflection data identified by the DIC method and the Average Error (AE) obtained by subtracting the measured data of the corresponding dial indicator, wherein the Standard Deviation (SD) and the Average Error (AE) are respectively used as the evaluation standards of calculation stability and precision. Let the deflection value identified by DIC method at each pixel point in the measurement range be d i I belongs to N, N is a coordinate set of all pixel points in a measurement area, d is the wood beam deflection measured by a dial indicator in the corresponding area, and therefore the mean error and the standard deviation can respectively represent a formula 8 and a formula 9.
Wherein the actual size of the reference object is L R Pixel size within image is L P (ii) a Actual displacement is D R Pixel displacement in image of D P (ii) a Identified deflection value d i (ii) a AE is mean error; SD is standard deviation, and N is the number of calculation points.
Mean error and standard deviation of the identification results of the two DIC methods under each working condition of three dial indicators of Hexi color painting beam bending test are shown in an attached table 4 and an attached table 5.
Appendix table 4. Hexi color painting beam DIC method deflection identification precision analysis
Attached table 5 deflection identification stability analysis of Hexi color painting beam by DIC method
As can be known from the attached table 4, in the loading process of the Hexi color painting beam, along with the gradual increase of the bending degree of the Hexi color painting beam, the mean value errors of the non-contact type deflection identification results of the two DIC methods and the contact type measurement results of the dial indicator at the midspan measurement point have no obvious trend rule, but basically keep relatively stable, and the bending degree of the Hexi color painting beam has little influence on the deflection measurement of the midspan DIC method. The reason is that the midspan pattern basically moves in translation when the Hexi color painted beam bends, and no obvious rotation or uneven stretching exists, so that the influence of the deflection of the Hexi color painted beam on the identification of the midspan deflection result by the DIC method is small in a certain range. For the measurement positions on the two sides of the midspan, the average errors of the identification results of the two DIC methods generally show an increasing trend along with the increasing of the bending degree of the Hexi colored drawing beam, because the colored drawing patterns on the deflection measurement positions on the two sides of the midspan not only increase in translation displacement but also gradually increase in rotation displacement in the midspan direction along with the increasing of the deflection of the Hexi colored drawing beam, so that the interference on the image identification method is gradually increased, and the error of the identification result is increased. Comparing the AD-DIC method and the CG-DIC method provided by the application to the deflection recognition of the Hexi color painted beam, the AD-DIC method is superior to the CG-DIC method in the deflection recognition result in general, the mean error of the deflection recognition result of the AD-DIC method is below 0.1mm, the maximum absolute value is 0.198mm, the mean error of the deflection recognition result of the CG-DIC method is generally above 0.1mm, and the maximum absolute value is 0.908mm.
As can be seen from the attached table 5, the standard deviation of the AD-DIC method deflection identification results at the three measuring points gradually increases with the increase of the deflection of the Hexi color painting beam. Firstly, for a midspan measuring position, although no rotation displacement exists in the beam height range, with the increase of the bending degree of the Hexi color painting beam, the difference of the bending stretching lengths of the upper side and the lower side of a pixel point subset in the beam height range is gradually increased, the tiny bending deformation of the subset is gradually increased, and the interference in matching of front and rear image subsets is increased, so that the discreteness of the calculated data is gradually increased, but because the error is random, the influence on the mean value error of deflection identification in the beam height range is small, for measuring areas on two sides of a midspan, not only the bending deformation of the subset but also the deflection angle of the subset exist, so that the bending deflection of the Hexi color painting beam is increased, and the standard deviation of the deflection identification result at a measuring point is also increased. Meanwhile, the standard deviation of the deflection identification result of the CG-DIC method is irregular along with the increase of the deflection of the Hexi color painting beam, and the dispersion type is large, which is caused by the fact that the integer pixel positioning of the calculation pixel point is not accurate by the coarse-fine searching integer pixel calculation method adopted in the algorithm. The transverse comparison of the two algorithms shows that the flexibility recognition stability of the AD-DIC method is greatly superior to that of the CG-DIC method, and the standard deviation of the flexibility recognition result is always kept below 0.06 mm.
Based on the measured data of the dial indicator, the difference (identification error) between the identification results of the two DIC algorithms in the three measurement areas in the working condition 5 and the measured data of the corresponding dial indicator is analyzed, as shown in fig. 5.
As can be seen from fig. 5, the CG-DIC method generates a large positioning error (a large peak) of the whole-pixel displacement at some positions when the deflection identification is performed in the three measurement regions, while the AD-DIC method always has a high calculation accuracy because the gray features of the colored drawing pattern are weaker than the speckle pattern, and due to the influence of image noise, the coarse-fine search method adopted in the former method is prone to fall into a wrong local optimal solution, and the local exhaustive-adaptive search method based on the continuous deformation property of the image better overcomes the situation. When the deflection identified by the AD-DIC method is close to the bottom of the beam at the midspan measurement position, small errors are generated, and the reason is that when the boundary of the pixel point subset is calculated to be in contact with the bottom edge of the wood beam or exceed the bottom edge of the wood beam, image identification can be influenced by a background image, so that the identification result may fluctuate. Therefore, when displacement is actually measured by the DIC method, a certain distance should be left between the measurement area and the boundary of the component.
Example two
Compared with the first embodiment, in the present embodiment, the displacement threshold includes a plurality of groups of colored drawing beam types and critical thresholds which are matched with each other, and the method further includes an input step of inputting the colored drawing beam types, and the matching step of matching the colored drawing beam types to obtain the critical threshold which is used as the current colored drawing beam displacement threshold.
Considering that the critical points of deformation are different for different colored drawing beams, that is, the displacement threshold values are different, therefore, the displacement threshold values in the scheme include the types of the colored drawing beams and the matched critical threshold values, an input module is further arranged, so that the type of the colored drawing beams can be conveniently input by a worker, and the matching module automatically matches the critical threshold values of the current colored drawing beams for calculation, thereby ensuring the accuracy of reminding.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. The double-precision displacement measurement method based on the self-adaptive search comprises the following steps:
an image acquisition step: acquiring an image of a measured object and generating image information;
a storage step: storing the calculation rule;
a calculation step: calculating the image information according to a calculation rule to obtain a displacement;
the method is characterized in that: the calculation rules comprise a parameter algorithm, a coefficient threshold algorithm, a size algorithm, a local exhaustive search method, a gradient method and a weight operation method;
the calculating step includes:
and (3) parameter calculation: calculating two image information of a measured object before and after a period of time according to a parameter algorithm to obtain a parameter value;
coefficient threshold calculation step: calculating the parameter value according to a coefficient threshold algorithm to obtain a coefficient threshold;
a subset selection step: calculating the size of the subset meeting the coefficient threshold according to a size algorithm;
and searching for the integral pixel displacement: the method comprises the steps of obtaining an initial value of a whole pixel according to the local exhaustive search of an initial calculation subset;
searching the initial value of the sub-pixel displacement: the method comprises the steps of determining a sub-pixel region subset according to an integer pixel initial value and a subset selection method, and respectively calculating sub-pixel displacement in the sub-pixel region subset for integer pixel initial value points at two ends of the sub-pixel region subset according to a gradient method;
and calculating a displacement accurate value: and performing weight calculation on the sub-pixel displacement to obtain a displacement accurate value.
2. The adaptive search based dual-precision displacement measurement method according to claim 1, wherein: also comprises a timing step: sending a starting signal according to the stored timing information;
the control steps are as follows: and after receiving a starting signal, controlling the image acquisition step to start.
3. The adaptive search based dual-precision displacement measurement method according to claim 1, wherein: the method also comprises the following alarming steps: and the calculation step is also used for calculating the obtained displacement accurate value and the displacement threshold value, and sending alarm information when the calculated displacement accurate value is greater than or equal to the displacement threshold value.
4. The dual-precision displacement measurement method based on adaptive search according to claim 3, wherein: also comprises the input step: inputting a displacement threshold value and storing the displacement threshold value.
5. The dual-precision displacement measurement method based on adaptive search according to claim 3, wherein: the displacement threshold comprises a plurality of groups of mutually matched colored drawing beam types and critical thresholds, and further comprises the following input steps: inputting the type of the colored drawing beam; matching: and matching a critical threshold value serving as a current displacement threshold value of the colored drawing beam according to the type of the input colored drawing beam.
6. The dual-precision displacement measurement method based on adaptive search according to claim 1, wherein: and a CCD camera is adopted in the image acquisition step.
7. The adaptive search based dual-precision displacement measurement method according to claim 6, wherein: in the image acquisition step, the optical axis of the CCD camera is opposite to the centroid of the measured object and is perpendicular to the pattern surface of the measured object.
8. The dual-precision displacement measurement method based on adaptive search according to claim 1, wherein: also comprises the following processing steps: and performing edge removal processing on the image information, wherein the image information subjected to the edge removal processing is calculated in the calculating step.
9. The dual-precision displacement measurement method based on adaptive search according to claim 8, wherein: in the processing step, edge removing processing is performed on the left edge and the right edge of the image information.
10. The dual-precision displacement measurement method based on adaptive search according to claim 9, wherein: the method also comprises the following input steps: inputting an edge processing size, and performing edge removing processing on the image information according to the edge processing size in the processing step.
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