CN110779454A - Improved digital image correlation method based on double-channel structure speckle cross-correlation algorithm - Google Patents
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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
- G01B11/161—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge by interferometric means
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Abstract
The invention provides an improved digital image correlation method based on a two-channel structure speckle cross-correlation algorithm, and belongs to the technical field of deformation measurement. The method comprises the steps of firstly, drawing digital images of structural speckles and random speckles according to the known size of a to-be-tested object, and manufacturing a speckle template. Then processing the surface of a test piece to be tested, covering a random speckle template on the surface of the test piece, spraying red dye to manufacture a speckle point pattern, covering a structural speckle pattern on the surface of the test piece after drying, and manufacturing structural scattered spots on the surface of the test piece by blue dye; and shooting and storing the deformation image of the test piece, and separating R channel data and B channel data. And analyzing and optimizing the R channel and the B channel respectively to obtain more accurate image deformation data. The method realizes an improved digital image correlation method, and is proved to be suitable for the conditions of large deformation, rigid bodies and the like.
Description
Technical Field
The invention relates to the technical field of deformation measurement, in particular to an improved digital image correlation method based on a two-channel structure speckle cross-correlation algorithm.
Background
In recent years, a non-contact deformation measurement technology based on a digital image correlation method is widely applied to the fields of optical measurement mechanics, deformation measurement and various engineering measurement. Digital image correlation method was originally developed in 80 s of the last century by the mountain-mouth-one Lang of Japan
[1]And Peter and Ranson et al, university of south Carolina, USA
[2]Independently of each other, the positions of the sub-regions before deformation in the image after deformation are determined by comparing the gray levels of the sub-regions in the digital image before and after deformation.
Digital image correlation methods require that the surface of the test piece have a random distribution of characteristic speckle patterns. At present, the black-white speckle pattern is usually manufactured by using technological means such as manual random spraying, machine manufacturing or speckle template, and the like, wherein black or white stains (such as self-spray paint, ink and the like) are generally adopted to manufacture black and white random spots on the surface of a test piece, and then a black-white CCD (charge coupled device) or CMOS (complementary metal oxide semiconductor) camera is used for acquiring digital images before and after spot field deformation on the surface of the test piece. Then, a correlation function such as a least square distance function (SSD) is used to calculate the correlation between the corresponding positions of the two images, and finally the deformation information of the corresponding point is obtained. In the above calculation process, the image data has only single-channel gray scale information, which is generally 8 bits, and the gray scale depth is represented by 0-255 integers.
Scholars shoot color speckle images with three channels of RGB information by using a color CCD camera, then separate RB channel information to be respectively used as left and right camera images to carry out binocular stereo three-dimensional digital image correlation calculation
[3]. However, in terms of speckle morphology, it still uses conventional randomly distributed spots. And the subsequent data processing part is still the traditional digital image correlation calculation method. The conventional random distribution of spots will be in the form of large deformation or rigid displacementDecorrelation occurs resulting in data results with large errors.
Reference documents:
[1]Yamaguchi I.A laser-speckle strain gauge[J].Journal of Physics E:Scientific Instruments.1981,14:1270～1273.
[2]W.H.Peters,W.F.Ranson.Digital Imaging Techniques in ExperimentalStress Analysis[J].Optical Engineering.1981，21:427～431.
[3]Li J,Dan X,Xu W,et al.3D digital image correlation using singlecolor camera pseudo-stereo system[J].Optics&Laser Technology,2017,95:1-7.
disclosure of Invention
The invention provides an improved digital image correlation method based on a dual-channel structure speckle cross-correlation algorithm, which is used for acquiring deformation image information with blue structure characteristics and red speckle characteristics by using a color camera, then solving image data of two channels by using the cross-correlation algorithm respectively to finally obtain the improved digital image correlation method, and the method can be suitable for the conditions of large deformation, rigid displacement and the like after verification.
The method comprises the following steps:
(1) according to the known size of the to-be-tested object, drawing a digital image capable of making structural speckles and random speckles by using a computer;
(2) engraving the 2 speckle images drawn in the step (1) on paper by using a miniature laser engraving machine to respectively manufacture a structural speckle template and a random speckle template;
(3) processing the surface of a test piece to be tested, covering a random speckle template on the surface of the test piece, and spraying red dye to manufacture a traditional speckle point pattern with random size and random position; after the red dye is dried, covering the structural speckle template on the surface of the test piece, manufacturing structural scattered spots on the surface of the test piece by using the blue dye, and finally drying the structural scattered spots to obtain a random speckle pattern containing structural points, which is manufactured by red and blue colors;
(4) shooting and storing a deformation image of the test piece by using a computer and a color camera;
(5) dividing R channel data and B channel data from an original 3-channel RGB image collected by a color camera to obtain 2 independent groups of image sequences;
(6) analyzing the R channel image sequence on an integer pixel scale by using a traditional DIC method to obtain integer pixel deformation information of corresponding pixel points, namely a horizontal direction U and a vertical direction V;
(7) aiming at a B channel image sequence, performing cross-correlation operation on structural speckle images before and after B channel image deformation by using a correlation function with a single peak value to obtain a two-dimensional correlation coefficient matrix through operation, extracting the coordinates of the peak value and a secondary peak value of the two-dimensional correlation coefficient matrix, and obtaining a difference value of the two peak value coordinates in the horizontal direction, namely a strain value Ux in the whole pixel scale in the horizontal direction; the difference value of the two peak value coordinates obtained in the vertical direction is the strain value Vy of the vertical direction on the whole pixel scale;
(8) and (4) substituting the integer pixel deformation U and V obtained in the step (6) and the strain values Ux and Vy obtained in the step (7) into a first-order shape function formula to be used as iterative initial values of the NR sub-pixel method, so that the NR sub-pixel solving algorithm can be optimized to obtain more accurate image deformation data.
Wherein, the speckle template in the step (2) needs to be designed and manufactured in each group of experiments.
And (4) treating the surface of the test piece to be tested in the step (3) to remove dust and water stains on the surface of the test piece.
And (4) ensuring that the pattern filling of the color camera lens is not more than one half of the field of view.
The correlation function having a single peak in step (7) comprises a zero-mean normalized least-squares distance correlation function.
The main body of the random speckle pattern containing the structural points prepared in the step (3) is a red sprayed random speckle pattern, each structural point of the blue structural points is in an original shape or a rectangular shape, the total number of the structural points is not more than 5, the structural points are distributed with the center of 1, and the rest are symmetrically distributed.
The technical scheme of the invention has the following beneficial effects:
in the scheme, an effective improvement method is provided for the digital image correlation method, and the phenomena of decorrelation and the like in the traditional method during large deformation or rigid body displacement are made up. And the method can meet the measurement precision required by digital image correlation measurement, and can well promote the development of the non-contact material/member deformation detection technology based on the digital image correlation method.
Drawings
FIG. 1 is a flow chart of an improved digital image correlation method based on a two-channel structured speckle cross-correlation algorithm according to the present invention;
FIG. 2 is a comparison of structural speckle points used in the present invention with conventional randomly distributed scattered speckles, wherein (a) is structural speckle and (b) is conventional random speckle;
fig. 3 is a schematic diagram of a simulation example of the speckle cross-correlation algorithm with a two-channel structure from step five to step eight, which is used in the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides an improved digital image correlation method based on a two-channel structure speckle cross-correlation algorithm.
As shown in fig. 1, the method comprises the steps of:
(1) according to the known size of the to-be-tested object, drawing a digital image capable of making structural speckles and random speckles by using a computer;
(2) engraving the 2 speckle images drawn in the step (1) on paper by using a miniature laser engraving machine to respectively manufacture a structural speckle template and a random speckle template;
(3) processing the surface of a test piece to be tested, covering a random speckle template on the surface of the test piece, and spraying red dye to manufacture a traditional speckle point pattern with random size and random position; after the red dye is dried, covering the structural speckle template on the surface of the test piece, manufacturing structural scattered spots on the surface of the test piece by using the blue dye, and finally drying the structural scattered spots to obtain a random speckle pattern containing structural points, which is manufactured by red and blue colors;
(4) shooting and storing a deformation image of the test piece by using a computer and a color camera;
(5) dividing R channel data and B channel data from an original 3-channel RGB image collected by a color camera to obtain 2 independent groups of image sequences;
(6) analyzing the R channel image sequence on an integer pixel scale by using a traditional DIC method to obtain integer pixel deformation information of corresponding pixel points, namely a horizontal direction U and a vertical direction V;
(7) aiming at a B channel image sequence, performing cross-correlation operation on structural speckle images before and after B channel image deformation by using a correlation function with a single peak value to obtain a two-dimensional correlation coefficient matrix through operation, extracting the coordinates of the peak value and a secondary peak value of the two-dimensional correlation coefficient matrix, and obtaining a difference value of the two peak value coordinates in the horizontal direction, namely a strain value Ux in the whole pixel scale in the horizontal direction; the difference value of the two peak value coordinates obtained in the vertical direction is the strain value Vy of the vertical direction on the whole pixel scale;
(8) and (4) substituting the integer pixel deformation U and V obtained in the step (6) and the strain values Ux and Vy obtained in the step (7) into a first-order shape function formula to be used as iterative initial values of the NR sub-pixel method, so that the NR sub-pixel solving algorithm can be optimized to obtain more accurate image deformation data.
The following description is given with reference to specific examples.
The method comprises the following steps: before the experiment, the size of the to-be-detected piece is obtained, 2 speckle digital images are drawn by a computer, and the first speckle dot pattern is the traditional speckle dot pattern with random size and random position. The second panel is a set of structured speckle patterns made up of 5 rectangular squares.
Step two: before the experiment, 2 speckle images generated by a computer are engraved on paper with proper size by using a miniature laser engraving machine to manufacture a speckle template. Each set of experiments requires the design and fabrication of a corresponding speckle template.
The steps are all prefabricated in advance before a field test experiment.
Step three: before the experiment, the simple surface treatment of the to-be-tested piece removes dust and water stains on the surface and the like. Two pre-fabricated speckle templates, red dye (e.g., self-painting, etc.) and blue dye, were carried by the experimenter. Firstly, covering a first speckle template on the surface of a test piece, and spraying red dye to manufacture a traditional speckle dot pattern with random size and random position. And then, after the red dye is dried in the air, covering the second structural speckle template on the surface of the test piece, manufacturing structural scattered spots by using the blue dye to the surface of the test piece, and finally drying in the air to obtain the random speckle pattern containing the structural points, which is manufactured by red and blue colors.
Step four: in the experiment, a computer and a color camera are used for shooting and storing a deformation image of a test piece, a lens with proper multiplying power needs to be selected, and pattern filling is guaranteed to be not more than one half of a view field.
Step five: after the experiment, R channel data and B channel data are separated from the original 3-channel RGB image collected by the color camera, and a single 2-group image sequence is obtained.
Step six: and analyzing the R channel image sequence on the whole pixel scale by using a traditional DIC (digital image conversion) method to obtain whole pixel deformation information, a horizontal direction U and a vertical direction V of corresponding pixel points.
Step seven: aiming at a B channel image sequence, cross-correlation operation is carried out on structural speckle images before and after B channel image deformation by using correlation functions with single peak values, such as zero-mean normalized least square distance correlation functions and the like, a two-dimensional correlation coefficient matrix is obtained through operation, the coordinates of the peak value and the secondary peak value of the two-dimensional correlation coefficient matrix are extracted, and the difference value of the two peak value coordinates obtained in the horizontal direction is the strain value Ux in the whole pixel scale in the horizontal direction. And the difference value of the two peak coordinates obtained in the vertical direction is the strain value Vy of the vertical direction on the whole pixel scale. The data can be used as a strain solving value under the condition of large deformation, and can also be used as an optimization parameter to optimize an NR iteration initial value in the next step.
Step eight: and (4) bringing the integer pixel deformation U and V obtained in the sixth step and the strain initial values Ux and Vy obtained in the seventh step into a first-order shape function formula to be used as iteration initial values of the NR sub-pixel method, so that the NR sub-pixel solving algorithm can be optimized to obtain more accurate image deformation data.
The method uses the speckle template to manufacture the random speckle pattern with two colors and structural characteristics, and solves the problem that the traditional random spraying method can not quickly and directly manufacture the mixed speckle pattern.
A simulated example of the two-channel structured speckle cross-correlation algorithm used in the present invention from step five to step eight is shown in fig. 3. The simulated deformation is a horizontal X-direction uniaxial stretch.
Fig. 3 shows, in step five, examples of R-channel and B-channel images obtained by multi-channel separation, which are conventional randomly distributed scattered spots (left image) and structured scattered spots (right image), respectively.
As shown in fig. 3 in steps six and seven, the two image sequences are respectively operated.
As shown in fig. 3, in the results of the sixth and seventh steps, the left image is a horizontal displacement field image obtained by performing an operation using a red R-channel image sequence, the right image is a cloud image drawn by using a correlation coefficient matrix obtained by performing a cross-correlation using a blue B-channel image sequence, and a peak coordinate distance, that is, a horizontal strain Ux, is obtained in an enlarged image of a peak portion of the cloud image. As in step eight of fig. 3, the result may be selected to be directly output or further iterative optimization may be performed.
The schematic diagram of the structural speckle designed by the invention compared with the traditional random distribution scattered speckle is shown in fig. 2, and it can be seen that in the structural speckle diagram, a cross square represents a blue square-shaped structural point, and a solid dot represents a red random scattered speckle.
The invention designs a random speckle pattern with structural speckle characteristics as a mixed speckle field, namely, the traditional digital image correlation operation is carried out on an R channel, so that the full-field deformation information is obtained. And the strain information of the image in the horizontal and vertical directions is calculated on the B channel by using the cross-correlation coefficient matrix peak searching algorithm provided by the invention. Finally, the improvement of the traditional random speckle field by adding the structural speckle field is realized.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. An improved digital image correlation method based on a two-channel structure speckle cross-correlation algorithm is characterized in that: the method comprises the following steps:
(1) according to the known size of the to-be-tested object, drawing a digital image capable of making structural speckles and random speckles by using a computer;
(2) engraving the 2 speckle images drawn in the step (1) on paper by using a miniature laser engraving machine to respectively manufacture a structural speckle template and a random speckle template;
(3) processing the surface of a test piece to be tested, covering a random speckle template on the surface of the test piece, and spraying red dye to manufacture a traditional speckle point pattern with random size and random position; after the red dye is dried, covering the structural speckle template on the surface of the test piece, manufacturing structural scattered spots on the surface of the test piece by using the blue dye, and finally drying the structural scattered spots to obtain a random speckle pattern containing structural points, which is manufactured by red and blue colors;
(4) shooting and storing a deformation image of the test piece by using a computer and a color camera;
(5) dividing R channel data and B channel data from an original 3-channel RGB image collected by a color camera to obtain 2 independent groups of image sequences;
(6) analyzing the R channel image sequence on an integer pixel scale by using a traditional DIC method to obtain integer pixel deformation information of corresponding pixel points, namely a horizontal direction U and a vertical direction V;
(7) aiming at a B channel image sequence, performing cross-correlation operation on structural speckle images before and after B channel image deformation by using a correlation function with a single peak value to obtain a two-dimensional correlation coefficient matrix through operation, extracting the coordinates of the peak value and a secondary peak value of the two-dimensional correlation coefficient matrix, and obtaining a difference value of the two peak value coordinates in the horizontal direction, namely a strain value Ux in the whole pixel scale in the horizontal direction; the difference value of the two peak value coordinates obtained in the vertical direction is the strain value Vy of the vertical direction on the whole pixel scale;
(8) and (4) substituting the integer pixel deformation U and V obtained in the step (6) and the strain values Ux and Vy obtained in the step (7) into a first-order shape function formula to be used as iterative initial values of the NR sub-pixel method, so that the NR sub-pixel solving algorithm can be optimized to obtain more accurate image deformation data.
2. The improved digital image correlation method based on the dual-channel structural speckle cross-correlation algorithm of claim 1, wherein: the speckle template in the step (2) needs to be designed and manufactured in each group of experiments.
3. The improved digital image correlation method based on the dual-channel structural speckle cross-correlation algorithm of claim 1, wherein: and (4) the surface treatment of the test piece to be tested in the step (3) comprises removing dust and water stains on the surface of the test piece.
4. The improved digital image correlation method based on the dual-channel structural speckle cross-correlation algorithm of claim 1, wherein: and (4) ensuring that the pattern filling of the color camera lens is not more than one half of the field of view.
5. The improved digital image correlation method based on the dual-channel structural speckle cross-correlation algorithm of claim 1, wherein: the step (7) uses a correlation function with a single peak, including a zero-mean normalized least-squares distance correlation function.
6. The improved digital image correlation method based on the dual-channel structural speckle cross-correlation algorithm of claim 1, wherein: the main body of the random speckle pattern containing the structural points prepared in the step (3) is a red sprayed random speckle pattern, each structural point of the blue structural spots is in an original shape or a rectangular shape, the total number of the structural spots is not more than 5, the structural spots are distributed with the center of 1, and the rest of the structural spots are symmetrically distributed.
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