CN110567918B - Mirror surface quality analysis method based on 2D structured light - Google Patents
Mirror surface quality analysis method based on 2D structured light Download PDFInfo
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Abstract
A mirror surface quality analysis method based on 2D structured light comprises the following steps: 1) installing equipment; 2) constructing a mirror surface initial model; 3) analyzing the imaging process of the camera by using a pinhole model; 4) determining the corresponding relation among the pixel point P, the reflection point M and the target point T; 5) color checkerboard coding; 6) dividing and decoding the color checkerboard; 7) optimizing mirror equation description; 8) fitting a new mirror model according to the normal vector, wherein the fitting mode is a least square method; 9) calculating whether the model converges according to the new mirror model; 10) calculating a normal vector of a mirror reference point according to the obtained converged mirror model; 11) comparing whether the fitted normal vector at the reference point is the same as the designed normal vector; 12) calculating the focal length of the fitted parabolic mirror in the direction X, Y, calculating the standard deviation of the normal vector of the mirror surface from the ideal value, and representing the flatness of the mirror surface. The invention can obtain more accurate measurement results and higher measurement efficiency.
Description
Technical Field
The invention belongs to the field of mirror surface quality analysis and detection, relates to a technology in the aspect of condenser mirror surface analysis in light-gathering thermal power generation, and discloses a mirror surface quality analysis method based on 2D structured light.
Background
At present, the high-speed consumption of fossil energy and the serious pollution of the environment have become the focus of the world common attention, and the development of new energy has become the core of the research of all countries. Among them, solar energy attracts more and more attention and research in its sustainability and cleanliness. The main utilization mode of solar energy is to convert light energy into electric energy, the prior art mainly comprises photovoltaic power generation and concentrated solar thermal power generation, in the concentrated solar thermal power generation, the quality of a heat source directly influences the power generation efficiency and the service life of equipment, and the main factors influencing the quality are the quality of a mirror surface formed by a condenser and the precision of mirror surface installation. The analysis of the quality of the mirror surface is the core of the invention.
In order to ensure the quality of the condenser so that it converges sunlight to produce a heat source with a uniform energy distribution, the optical parameters constituting the mirror surface need to maintain a high degree of consistency with the design model. However, machining errors may change the focal length of the mirror, even resulting in distortion and surface irregularities, and the quality of the mirror may need further inspection. The mirror has the characteristic of high reflectivity, which makes it difficult for the three-dimensional scanning technology to directly complete the measurement of the mirror shape. In recent years, reflection-based measurement methods have received increasing attention, such as SOFAST. The method utilizes an LCD screen to display a phase-shift stripe picture as a target, and takes a reflection picture of a mirror surface through a digital camera. The mapping relation between the camera pixel and the reflection target point can be established by decoding the reflection fringe image, and the corresponding specular reflection point and normal vector can be determined according to the optical reflection principle and the ray tracing method. And fitting normal vector data by using a selected second-order equation, and obtaining parameters related to the optical characteristics of the mirror surface through equation parameters. But the process is still in a stage of development.
Disclosure of Invention
In order to overcome the defects of the existing mirror surface quality analysis, the invention provides a mirror surface analysis method which can obtain more accurate measurement results and higher measurement efficiency, and does not need complex equipment and higher cost.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a mirror surface quality analysis method based on 2D structured light comprises the following steps:
1) installing equipment, wherein the measuring system takes the color coding checkerboard image as a target, a digital camera is used for shooting a reflection image of a mirror surface, and the shooting distance of the camera is 5.5-6.5 m, and the mirror surface is 0.9-1.1 m away from the target;
2) constructing a mirror initial model
In order to efficiently concentrate sunlight onto a receiver, the collector mirror is designed in the shape of a paraboloid of revolution, which is described by the following equation:
Z＝A×X2+B×Y2+C×XY+D×X+E×Y+F
wherein X, Y and Z are mirror coordinate system three coordinate axes, A, B, C, D, E and F are parameters needing fitting;
3) analyzing camera imaging process using pinhole model
Point C is the camera transmission center, point P is the image point, and PC determines the direction vector of the reflected light
M is a mirror reflection point, the spatial position of the mirror reflection point is calculated through the intersection point of the light and the mirror (initially described by a design model), the corresponding reflection target point T is obtained through a checkerboard image decoding algorithm, and TM determines the incident direction of the light
According to the principle of ray reflection, the normal vector at the point M is calculated by the following equation:
4) determining the corresponding relation among the pixel point P, the reflection point M and the target point T, wherein the corresponding relation between P and M is determined through geometric analysis; the method comprises the steps of adopting a spatial coding structured light image (colored checkerboard) as a reflection target image, simplifying the target image into a flat plate, generating the image through a checkerboard coding algorithm, and decoding the image to establish a mapping relation between a pixel P and a target point T;
5) color checkerboard coding
Seven colors are adopted for coding to generate a target image, different colors are replaced by different numbers, the checkerboard image is converted into a corresponding number matrix, the matrix is coded, and the corresponding checkerboard image is obtained. The encoding process needs to generate a corresponding transverse sequence and a state transition sequence first, and then combine the transverse sequence and the state transition sequence to obtain a target matrix, which is divided into: coding a transverse sequence, generating a state conversion sequence and generating a target digital matrix;
6) color checkerboard segmentation and decoding
The checkerboard decoding process is divided into three steps: color identification, checkerboard segmentation and codeword matching;
converting the RGB image into an HSV color domain image, wherein in the image format, color information is independent of brightness information and only contained in hue H (hue) channel numerical values, firstly judging whether a pixel is colorful according to saturation, and then normalizing the pixel into a standard color according to color distance; aiming at an achromatic color pixel, if the picture brightness value of the achromatic color pixel is larger, the achromatic color pixel is judged to be white, otherwise, the picture is black, a color identification method is adopted to traverse the image, and the colors of all the pixels are normalized to standard colors (6 colors, white and black), so that the color identification can be completed;
dividing an original picture into 7 sub-pictures with different colors, completing noise filtering through corrosion and expansion algorithms, and finally combining 7 filtered pictures to obtain a target image;
7) mirror equation description optimization
The fitting of the mirror equation is performed in a mirror coordinate system, and therefore the coordinates of the pixel plane and the color checkerboard target need to be converted into the mirror coordinate system, and the conversion of the coordinate system is completed by the following three steps:
7.1) converting the pixel plane coordinate system to a camera coordinate system (X ' Y ' Z ');
7.2) converting the checkerboard target coordinate system (X 'Y' Z ') to a camera coordinate system (X' Y 'Z');
7.3) converting the camera coordinate system (X ' Y ' Z ') to the mirror coordinate system (XYZ). Because the position of the mirror surface corner point is estimated according to the mirror surface design model, certain errors exist, and the calibration deviation of the coordinate system XYZ can be caused. To obtain accurate mirror quality analysis results, the method minimizes the slope error (RMS) of the mirror equation using a gradient descent method.
8) And fitting a new mirror surface model according to the normal vector, wherein the fitting mode is a least square method, and the model formula of the mirror surface is as follows:
Z＝A×X2+B×Y2+C×XY+D×X+E×Y+F
wherein X, Y and Z are mirror coordinate system three coordinate axes, A, B, C, D, E and F are parameters needing fitting;
from the partial derivative formula, the normal vector at the point (x, y, z) is (n)1,n2,n3)
n1＝2A×X+C×Y+D
n2＝2B×Y+C×X+E
n3＝-1
Fitting all parameters according to the normal vectors of each point calculated in the step 3)4)5)6), wherein the C value is the average value of the two fitting values, so that the fitting of a new mirror model is realized;
9) calculating whether the model is converged according to the new mirror model, if not, repeating the step 3)4)5)6)8)9) to fit the mirror again until the model is converged, and if yes, entering the step 10);
10) calculating a normal vector of a mirror reference point according to the obtained converged mirror model, wherein the reference point is on a bisector of two short sides of the mirror and is 3/4 from the smaller short side to the larger short side;
11) comparing whether the normal vector fitted at the reference point is the same as the designed normal vector or not, and if so, entering the next step; if not, rotating around the reference point through the mirror coordinate system to enable the two normal vectors to be coincident, and then repeating the steps 5) to 11);
12) according to the fitted mirror model formula, the focal length of the fitted parabolic mirror in the direction X, Y is calculated, and the standard deviation of the normal vector of the mirror surface and the ideal value is calculated, so that the flatness of the mirror surface is represented.
The method only needs to collect 1 picture, adopts a parallel mode in the normal vector calculation process of the mirror surface, and utilizes double-layer iteration to ensure the fitting precision of the mirror model, thereby greatly improving the efficiency of mirror detection and being directly used for assembly line work.
The invention has the following beneficial effects: the mirror surface measuring device is simplified into a flat plate and a digital camera. The mirror surface can be measured through a single picture, the sampling resolution ratio of about 3000 can be realized, and the method is suitable for the rapid detection scene of the mirror surface.
Drawings
Fig. 1 is a flow chart of a method of specular quality analysis based on 2D structured light.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, a method for analyzing the quality of a mirror surface based on 2D structured light includes the following steps:
1) the measuring equipment of the invention comprises a color checkerboard flat plate and a digital camera, wherein the digital camera is placed at a position about 6m away from the mirror surface, and the target is about 1m away from the mirror surface; the mirror surface and the target form a certain included angle, so that the camera can shoot the checkerboard image of the target through the reflection of the mirror surface. And adjusting camera parameters including optical focal length, aperture, ISO, white balance, exposure time and the like to ensure the definition of the image shot by the camera. The camera is set to be in a remote control mode, so that the risk of damaging the coordinate relation between the measurement components by manual shooting is avoided;
2) calibrating internal parameters of the camera, namely calibrating the internal parameters of the camera by using a checkerboard calibration plate, wherein the calibration comprises the calibration of focal length, picture center, lens distortion and the like;
3) determining initial coordinate systems of a camera and a target, calculating a coordinate conversion matrix of the color checkered target and the camera by a reflection calibration algorithm by means of a high-quality plane mirror, and determining a conversion relation from the camera to a mirror coordinate system by an LHM algorithm based on corner point information of the mirror. Then, obtaining a conversion relation from a pixel plane to a camera coordinate system according to the previously obtained camera internal parameters;
4) and preliminarily establishing the position relation between the camera and the mirror surface. The invention adopts a laser range finder to measure the distance from a camera to a mirror surface;
5) estimating the distribution of normal vectors of the mirror surface according to an initial model of the mirror surface, firstly segmenting a checkerboard image to obtain code words of the checkerboard, establishing a mapping relation between sampling points and reflection target points, converting coordinates of the sampling points and the target points obtained by decoding the image into a mirror surface coordinate system according to a coordinate relation obtained by calibrating a measurement system, and minimizing a slope error (RMS) of a mirror surface equation by using a gradient descent method to obtain a measurement result of optical parameters of the mirror surface;
6) and fitting a new mirror model according to the normal vector, wherein the fitting mode is a least square method. The mirror surface model formula is as follows:
Z＝A×X2+B×Y2+C×XY+D×X+E×Y+F
wherein X, Y and Z are mirror coordinate system three coordinate axes, A, B, C, D, E and F are parameters needing fitting;
from the partial derivative formula, the normal vector at the point (x, y, z) is (n)1,n2,n3)
n1＝2A×X+C×Y+D
n2＝2B×Y+C×X+E
n3＝-1
Fitting all parameters according to the normal vectors of each point calculated in the step 5), wherein the C value is the average value of the two fitting values. Thus, the fitting of the new mirror model is realized;
7) calculating whether the model is converged according to the new mirror model, if not, repeating the step 5)6)7) to fit the mirror again until the model is converged, and if yes, entering the step 8);
8) calculating a normal vector of a mirror reference point according to the obtained converged mirror model, wherein the reference point is on a bisector of two short sides of the mirror and is 3/4 from the smaller short side to the larger short side;
9) comparing whether the fitted normal vector at the reference point is the same as the designed normal vector, if so, entering the next step, if not, enabling the two normal vectors to be coincided through the rotation of the mirror surface coordinate system around the reference point, and then repeating the steps 5) to 9);
10) according to the fitted mirror model formula, the focal length of the fitted parabolic mirror in the direction X, Y is calculated, and the standard deviation of the normal vector of the mirror surface and the ideal value is calculated, so that the flatness of the mirror surface is represented.
The method only needs to shoot 1 picture, simplifies the mirror surface measuring equipment into a flat plate and a digital camera, can finish the measurement of the mirror surface through a single picture, can realize about 3000 sampling resolution, and is suitable for the rapid detection scene of the mirror surface. The three-dimensional information calculation process of the mirror surface adopts a parallel mode, and the fitting precision of the mirror surface model is ensured by utilizing a double-layer iteration method, so that the mirror surface detection efficiency is greatly improved, and the level of directly using the mirror surface model for assembly line work is reached.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.
Claims (1)
1. A method for analyzing the quality of a mirror surface based on 2D structured light, which is characterized by comprising the following steps:
1) installing equipment, taking the color coded checkerboard image as a target by a measuring system, and shooting a reflection image of a mirror surface by using a digital camera, wherein the shooting distance of the camera is 5.5-6.5 m, and the distance between the mirror surface and the target is 0.9-1.1 m;
2) constructing a mirror initial model
In order to efficiently concentrate sunlight onto a receiver, the collector mirror is designed in the shape of a paraboloid of revolution, which is described by the following equation:
Z＝A×X2+B×Y2+C×XY+D×X+E×Y+F
wherein X, Y and Z are mirror coordinate system three coordinate axes, A, B, C, D, E and F are parameters needing fitting;
3) analyzing camera imaging process using pinhole model
Point C is the camera transmission center, point P is the image point, and PC determines the direction vector of the reflected light
M is a mirror reflection point, the spatial position of the mirror reflection point is calculated through the intersection point of the light and the mirror, the corresponding reflection target point T is obtained through a checkerboard image decoding algorithm, and TM determines the incident direction of the light
According to the principle of ray reflection, the normal vector at the point M is calculated by the following equation:
4) determining the corresponding relation among the pixel point P, the reflection point M and the target point T, wherein the corresponding relation between P and M is determined through geometric analysis; the method comprises the steps of adopting a spatial coding structured light image as a reflection target image, simplifying the target image into a flat plate, generating the image through a checkerboard coding algorithm, and decoding the image to establish a mapping relation between a pixel P and a target point T;
5) color checkerboard coding
Seven colors are adopted for coding to generate a target image, different colors are replaced by different numbers, the checkerboard image is converted into a corresponding number matrix, the coding of the matrix is completed, and the corresponding checkerboard image is obtained; the encoding process needs to generate a corresponding transverse sequence and a state transition sequence first, and then combine the transverse sequence and the state transition sequence to obtain a target matrix, which is divided into: coding a transverse sequence, generating a state conversion sequence and generating a target digital matrix;
6) color checkerboard segmentation and decoding
The checkerboard decoding process is divided into three steps: color identification, checkerboard segmentation and codeword matching;
converting the RGB image into an HSV color domain image, wherein in the image format, color information is independent of brightness information and only contained in hue H channel numerical values, firstly judging whether a pixel is colorful according to saturation, and then normalizing the pixel into a standard color according to color distance; aiming at an achromatic color pixel, if the picture brightness value of the achromatic color pixel is larger, the achromatic color pixel is judged to be white, otherwise, the picture is black, a color identification method is adopted to traverse the image, and the colors of all pixels are normalized to a standard color, so that the color identification can be completed;
dividing an original picture into 7 sub-pictures with different colors, completing noise filtering through corrosion and expansion algorithms, and finally combining 7 filtered pictures to obtain a target image;
7) mirror equation description optimization
The fitting of the mirror equation is performed in a mirror coordinate system, and therefore the coordinates of the pixel plane and the color checkerboard target need to be converted into the mirror coordinate system, and the conversion of the coordinate system is completed by the following three steps:
7.1) converting the pixel plane coordinate system to a camera coordinate system (X ' Y ' Z ');
7.2) converting the checkerboard target coordinate system (X 'Y' Z ') to a camera coordinate system (X' Y 'Z');
7.3) converting the camera coordinate system (X ' Y ' Z ') to the mirror coordinate system (XYZ) and minimizing the slope error of the mirror equation by using a gradient descent method;
8) and fitting a new mirror surface model according to the normal vector, wherein the fitting mode is a least square method, and the model formula of the mirror surface is as follows:
Z＝A×X2+B×Y2+C×XY+D×X+E×Y+F
wherein X, Y and Z are mirror coordinate system three coordinate axes, A, B, C, D, E and F are parameters needing fitting;
from the partial derivative formula, the normal vector at the point (x, y, z) is (n)1,n2,n3)
n1＝2A×X+C×Y+D
n2＝2B×Y+C×X+E
n3＝-1
Fitting all parameters according to the normal vectors of each point calculated in the step 3)4)5)6), wherein the C value is the average value of the two fitting values, so that the fitting of a new mirror model is realized;
9) calculating whether the model is converged according to the new mirror model, if not, repeating the step 3)4)5)6)7)8)9) to fit the mirror again until the model is converged, and if yes, entering the step 10);
10) calculating a normal vector of a mirror reference point according to the obtained converged mirror model, wherein the reference point is on a bisector of two short sides of the mirror and is 3/4 from the smaller short side to the larger short side;
11) comparing whether the normal vector fitted at the reference point is the same as the designed normal vector or not, and if so, entering the next step; if not, rotating around the reference point through the mirror coordinate system to enable the two normal vectors to be coincident, and then repeating the steps 5) to 11);
12) according to the fitted mirror model formula, the focal length of the fitted parabolic mirror in the direction X, Y is calculated, and the standard deviation of the normal vector of the mirror surface and the ideal value is calculated, so that the flatness of the mirror surface is represented.
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