A calibration method for a digital plane array projector based on precision verification device

Abstract

The application discloses a kind of based on precision verification device's calibration method for digital plane array projector, comprising the following steps: based on digital plane array projector and camera builds digital plane array projection system;The parameter of the digital plane array projection system is calibrated;Based on SBOA algorithm optimization BP neural network's weight and threshold, obtain SBOA-BP neural network model;Based on the SBOA-BP neural network model, the calibration error of the digital plane array projection system is optimized;Precision verification device is constructed, and the calibration result of the digital plane array projection system is evaluated based on the precision verification device.The present application designs a kind of precision verification device, utilizes the device and carries out digital plane array projector calibration by means of camera, and then generates projection pattern, realizes calibration precision verification, solves the problem that neural network is applied to projector calibration and is difficult to calibrate precision verification.

Description

Technical Field

[0001] This invention belongs to the field of structured light measurement technology, and particularly relates to a calibration method for digital area array projectors based on an accuracy verification device. Background Technology

[0002] Structured light measurement is a non-contact 3D measurement technique. It projects a structured light-coded pattern onto the surface of the target object using a projector. Due to the unevenness of the object's surface height, the pattern will be distorted. A camera then captures the distorted structured light image, and a computer decodes the image to obtain the correspondence between the points in the structured light image and the points in the projected pattern. Based on the principle of triangulation, the 3D point cloud data of the object's surface is calculated, enabling 3D reconstruction of the object's surface.

[0003] The research background and significance of structured light measurement technology lies in its ability to provide large field of view, non-contact, high precision, robustness and real-time three-dimensional measurement. Therefore, it has wide applications in fields such as industrial automatic inspection, product quality control, reverse design, biomedicine, virtual reality, cultural relic replication and human body measurement.

[0004] Camera and projector calibration is a crucial step in structured light measurement technology. The projector can be viewed as the reverse process of the camera; the image is projected onto the object and then captured by the camera. By projecting a known pattern and analyzing its deformation, the object's 3D information is obtained. Because projector lenses may have distortion, and system parameters may need adjustment before each use, calibration is necessary before each use. During calibration, a grating image is projected onto the surface of the object being measured by a projection device, while a camera simultaneously captures a grating image of the object's surface. By establishing a correspondence between the projector image and the camera image, projector calibration is transformed into mature camera calibration, and the projector is calibrated using methods for camera calibration. Calibration accuracy is a key indicator for evaluating the calibration quality of the projector and camera in a structured light 3D measurement system. High-precision calibration can significantly improve the quality of structured light measurements, and verifying calibration accuracy is a critical step in ensuring the accuracy of the 3D measurement system.

[0005] The application of neural networks to system calibration has yielded numerous research results both domestically and internationally, primarily focusing on camera calibration or stereo vision system calibration. Unlike cameras, projectors cannot capture images, making it impossible to directly obtain the relationship between the positions of points on the projected image and their corresponding points in three-dimensional space. This means that projector calibration technology is still under development.

[0006] To address the problems in existing technologies, there is an urgent need to propose a calibration method for digital area array projectors based on an accuracy verification device. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention proposes a calibration method for digital area array projectors based on an accuracy verification device. This method applies neural networks to the projector calibration problem, constructs a digital area array projection system using a digital area array projector and a CCD camera, calibrates the system parameters, optimizes the calibration error of the digital area array projector based on the neural network, designs an accuracy verification device based on the digital area array projector calibration, and proposes an accuracy verification method based on this device to solve the problems existing in the prior art.

[0008] To achieve the above objectives, the present invention provides a calibration method for a digital area array projector based on an accuracy verification device, comprising the following steps:

[0009] A digital array projection system was built based on a digital array projector and a camera.

[0010] The parameters of the digital area projection system are calibrated;

[0011] The SBOA-BP neural network model is obtained by optimizing the weights and thresholds of the BP neural network based on the SBOA algorithm.

[0012] The calibration error of the digital area array projection system is optimized based on the SBOA-BP neural network model.

[0013] An accuracy verification device is constructed, and the calibration results of the digital area array projection system are evaluated based on the accuracy verification device.

[0014] Optionally, the parameters that need to be pre-calibrated for the digital area projection system include:

[0015] The parameters of the camera pinhole model, the parameters of the digital area array projector pinhole model, the nonlinear distortion coefficients of the camera, the nonlinear distortion coefficients of the digital area array projector, and the rigid body transformation parameters between the camera coordinate system and the digital area array projector coordinate system.

[0016] Optionally, the process of calibrating the parameters of the camera in the digital area projection system includes:

[0017] The camera parameters in the digital area projection system are calibrated based on Zhang Zhengyou's calibration method.

[0018] Optionally, the process of calibrating the parameters of the digital area array projector in the digital area array projection system includes:

[0019] The calibration board is placed within the working range of the digital area array projection system, and an unstructured light image is captured. Then, horizontal and vertical sinusoidal fringe structured light is projected, and the corresponding image sequence is captured by the camera. By changing the pose of the calibration board, the shooting process is repeated to obtain multiple sets of calibration images. The center of the circular target point of the calibration board is extracted from the calibration images. The matching relationship between the imaging point on the plane of the digital area array projector and the center of the circular target point of the calibration board is established by using absolute phase unfolding technology and linear interpolation method, thereby completing the parameter calibration of the digital area array projector.

[0020] Optionally, the process of optimizing the initial weights and thresholds of the BP neural network based on the SBOA algorithm to obtain the SBOA-BP neural network model includes:

[0021] The input layer of the SBOA-BP neural network model is the pixel coordinates in the projector image coordinate system, and the output layer is the calibration point coordinates in the world coordinate system. The difference between the output calibration point coordinates in the world coordinate system and the actual calibration point coordinates in the world coordinate system is used as the optimization objective function. The initial weights and thresholds of the BP neural network are continuously optimized iteratively. When the maximum number of iterations is reached, the training ends, and the optimized SBOA-BP neural network model is obtained.

[0022] Optionally, the accuracy verification device includes several uniformly distributed projection positioning calibration points, test positions, and various projection contours;

[0023] The projection positioning calibration point is used to calibrate the relative pose of the digital area array projection system and the accuracy verification device.

[0024] Each test location is equipped with a crosshair reticle, which is used in conjunction with an industrial microscope to obtain the projection ray width and the maximum positioning error of the projection image of the digital area array projection system.

[0025] The projection profile features a groove design to receive projection light from the digital array projection system.

[0026] Optionally, the process of evaluating the calibration results of the digital area array projection system based on the accuracy verification device includes:

[0027] After the projection light from the digital area array projection system is projected onto the projection outline, the image projected by the digital area array projection system is compared with the geometric pattern of the projection outline. The positioning error of the projection image is obtained by combining the reading of the crosshair, and then the calibration result of the digital area array projection system is evaluated.

[0028] Optionally, a plurality of positioning holes are provided at the edge of the accuracy verification device to fix the position of the accuracy verification device.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] This invention proposes a calibration method for digital array projectors based on an accuracy verification device. By constructing an SBOA-BP neural network model, the calibration error of the digital array projection system is optimized, thus realizing the application of neural networks to projector calibration.

[0031] This invention constructs an accuracy verification device to evaluate the calibration results of a digital area array projection system. Compared with the traditional calibration plate method, this accuracy verification device provides a more intuitive and accurate accuracy evaluation method. It can not only be used to calibrate the projector but also to verify the accuracy of the calibration results, providing a more comprehensive and reliable accuracy verification means. Attached Figure Description

[0032] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0033] Figure 1 This is a schematic diagram of a digital area array projection system model according to an embodiment of the present invention;

[0034] Figure 2 This is a flowchart of the SBOA-BP neural network model calculation in an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of a digital area array projector calibration accuracy verification device according to an embodiment of the present invention. Detailed Implementation

[0036] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0038] Example 1

[0039] This embodiment provides a calibration method for a digital area array projector based on an accuracy verification device, including the following steps:

[0040] A digital array projection system was built based on a digital array projector and a camera.

[0041] The parameters of the digital area projection system are calibrated;

[0042] The SBOA-BP neural network model is obtained by optimizing the weights and thresholds of the BP neural network based on the SBOA algorithm.

[0043] The calibration error of the digital area array projection system is optimized based on the SBOA-BP neural network model.

[0044] An accuracy verification device is constructed, and the calibration results of the digital area array projection system are evaluated based on the accuracy verification device.

[0045] It is feasible to build a digital array projection system:

[0046] like Figure 1 As shown, the digital area array projection system mainly consists of an industrial camera, a digital area array projector, a workpiece to be measured, and a computer software system. It establishes the spatial relationship between the camera, projector, and workpiece to be measured within the system, where O... w –X w Y w Z w Represents the world coordinate system; O c –X c Y c Z represents the camera coordinate system; O p –X p Y p Z p Let R represent the coordinate system of the projection device; R and t represent the rotation matrix and translation vector in the rigid body transformation from the camera coordinate system to the projection coordinate system, respectively.

[0047] The camera uses an optical lens to capture point X in the world coordinate system. w Projected onto the CCD imaging plane, i.e., point X c In a digital array projector, the digital micromirror device (DMD) is equivalent to the image plane formed by the CCD or CMOS chip in a camera. The working model of the projector can be viewed as the inverse process of the camera's imaging process, that is, starting from the optical center O of the projector... p Starting from the image point X on the projection plane p A ray of light is projected onto a point X in the world coordinate system. w Therefore, both the camera and the projection device can be represented by the pinhole model as follows:

[0048]

[0049] In the formula, It represents the homogeneous coordinates of the image points on the camera (or projector) image plane; Point X in the world coordinate system w The homogeneous coordinate form; s is any non-zero scaling factor; R and t are the rotation matrix and translation vector from the world coordinate system to the camera (or projector) coordinate system, respectively; K is the pinhole model parameter matrix.

[0050]

[0051] In the formula, f x f y c is the normalized focal length of the camera (or projector) in the horizontal and vertical directions; x c y The offset coordinates of the principal point of the camera (or projector).

[0052] Considering the nonlinear distortion effect of optical lenses, the spatial point X w The actual imaging point is not at the ideal imaging point x(u, v), but has moved to x. d (u d v d Specifically, it can be expressed as:

[0053]

[0054] In the formula, r is the radius of distance from the image pixel to the optical center of the lens. 2 =u 2 +v 2 ;d=[k1 k2 p1 p2] T k1 and k2 are the first and second order radial distortion coefficients of the camera (or projector) lens; p1 and p2 are the first and second order tangential distortion coefficients.

[0055] The system parameters that need to be pre-calibrated include the camera's pinhole model parameter matrix K. c The pinhole model parameter matrix K of the projection device p The nonlinear distortion coefficient d of the camera c The nonlinear distortion coefficient d of the projection device p And the rigid body transformation parameter R between the camera coordinate system and the projection device coordinate system. cp t cp Once the intrinsic parameters of the projection device and camera, as well as the coordinate transformation relationship between them, are determined through the calibration process, the imaging point x on the projected image plane... p and the imaging point x on the camera image plane c The matching relationship can be used to uniquely determine the spatial point X using the trigonometric method. w Three-dimensional coordinates in the system coordinate system.

[0056] Feasible steps include parameter calibration of the camera and digital area array projector:

[0057] The camera calibration process uses the traditional Zhang Zhengyou calibration method. This method is low-cost, easy to use, and has strong anti-interference capabilities. Its calibration results can meet the accuracy requirements of most measurement applications, and therefore it is widely used in camera calibration. This calibration method can be implemented in both the Matlab Calibration Toolbox and OpenCV.

[0058] The calibration process of the projector utilizes structured light to project grating images, establishing a matching relationship between the imaging points on the projector plane and the centers of the circular target points on the planar calibration plate. First, the calibration plate is placed within the system's working range, and the camera captures an original image of the calibration plate without structured light fringe projection. Then, keeping the calibration plate stationary, the projection device sequentially projects two sets of sinusoidal fringe structured light images (horizontal and vertical directions) onto the calibration plate. Simultaneously, the camera acquires the corresponding image sequence of the calibration plate under each structured light projection. The pose of the calibration plate is varied, and the above shooting process is repeated to obtain multiple sets of calibration images. For each series of calibration images captured under each calibration plate pose, the pixel coordinates (x, y) of the centers of each circular target point on the calibration plate in the camera's image plane are first extracted from the original calibration plate image. c (u, v), and then perform absolute phase unrolling on the structured light image sequence of the calibration plate to solve for x. c The horizontal absolute phase and vertical absolute phase values ​​(φ) at (u, v) x , φ y The phase of this point corresponds to the pixel x on the projected image plane. p The positions of (u', v') can be obtained by the following formula:

[0059]

[0060] In the formula, f is the frequency corresponding to the structured light image.

[0061] Since the center coordinates on the calibration board are obtained by fitting the ellipse edge pixels extracted from the image, (u, v) are usually sub-pixel values. However, structured light decoding can only obtain the phase values ​​of integer pixels in the x and y directions. Therefore, linear interpolation is required to obtain the (φ) corresponding to (u, v). x , φ y After obtaining the two-dimensional coordinates of the center of each circular target point on the calibration plate on the camera image plane and the projection image plane, the calibration of all system parameters is completed based on the calibration image.

[0062] Feasible methods for optimizing projector calibration accuracy:

[0063] Through precise calculations, the pixel coordinates of the points to be measured in the projector image coordinate system, as well as the three-dimensional coordinates of these points in the world coordinate system, were obtained. This step was achieved through the calibration process in the structured light system, ensuring an accurate correspondence between the two-dimensional image and the three-dimensional space.

[0064] Neural networks have powerful nonlinear approximation capabilities. During projector calibration, when the system structure is determined, the projector image coordinates, camera image coordinates, and corresponding 3D coordinates have a fixed one-to-one mapping relationship based on the established system model. Ignoring complex system models, neural networks can be used to approximate the structured light system model and directly establish this mapping relationship.

[0065] Backpropagation (BP) neural networks are a common type of artificial neural network (ANN) architecture used to solve supervised learning problems such as classification and regression. It consists of an input layer, hidden layers, and an output layer, where there can be one or more hidden layers. BP neural networks learn the mapping relationship between inputs and outputs through training, and the main algorithm used is backpropagation.

[0066] The Secretary Bird Optimization Algorithm (SBOA) is a novel metaheuristic algorithm that demonstrates outstanding performance in terms of solution quality, convergence speed, and stability. This algorithm is inspired by the survival behaviors of secretaries in their natural environment, particularly their predation on snakes and their escape from predators.

[0067] The SBOA algorithm consists of two main phases: the exploration phase and the development phase. The exploration phase simulates a heron hunting a snake, including the process of finding prey, consuming prey, and attacking prey. The development phase simulates a heron escaping a predator, where the heron observes the environment and chooses the most suitable escape strategy, such as running away or camouflage.

[0068] The initial preparation phase of the algorithm involves randomly initializing the positions of the herons in the search space, with each heron representing a candidate solution. In the hunting strategy, the herons use their keen eyesight to locate prey and act swiftly upon finding it. This process is achieved by updating the heron positions through a mathematical model, including the use of weighted Levy flight to improve optimization accuracy. In the escape strategy, the herons may engage in two behaviors: using environmental camouflage or running or flying to escape the predator. A dynamic perturbation factor is introduced into the algorithm to balance exploration and exploitation in order to solve the optimization problem.

[0069] In the SBOA algorithm, chaotic mapping is used to replace the conventional uniformly distributed random number generator to optimize the SBOA algorithm. Experiments have shown that the fitness function value of random numbers generated by chaotic mapping is significantly improved. Replacing the conventional uniformly distributed random number generator with chaotic mapping can obtain better results, especially when there are many local solutions in the search space, it is easier to find the global optimum, which can enhance the randomness and diversity of particles.

[0070] like Figure 2 As shown, to improve the simulation capability of the BP neural network model, this embodiment constructs an SBOA-BP neural network model. Since the initial weights and thresholds in the BP neural network training process are generated by random numbers, which affects the network structure, the SBOA algorithm is used to optimize the initial weights and thresholds of the BP neural network, thus obtaining a more stable SBOA-BP neural network model. In the SBOA algorithm, firstly, relevant parameters need to be initialized, including the initial data size, the number of independent variables, and the upper and lower limits of the independent variables, and the initial fitness value is calculated. The fitness value represents the degree of fit of the optimized parameter scheme, and each parameter needs to be updated during the iteration process. Secondly, each parameter is optimized, and the optimized fitness value is calculated. Finally, it is determined whether each parameter and fitness value meets the conditions. If they do, the process exits and the result is output; otherwise, the above process is repeated.

[0071] The SBOA-BP neural network model applied to optimize the calibration accuracy of projectors has the following input layer: pixel coordinates in the projector image coordinate system and world coordinates of the point to be measured. The difference between the world coordinates of the calibration point calculated by the neural network and the actual world coordinates of the calibration point is used as the optimization objective function. The weights and thresholds of the neural network are continuously optimized iteratively. When the maximum number of iterations is reached, the training ends and the optimization is considered complete.

[0072] Feasible methods and apparatus for verifying projector calibration accuracy:

[0073] To verify the calibration accuracy of a projector, the first step is to use a CCD camera to capture an image of the object under test, identify the feature points on the object, and select a calibration feature point from a calibration image as the center O of the world coordinate system. w Determine X respectively w Y w Z w The directions of the coordinates can be determined using the following formulas to establish the relationship between the world coordinate system and the camera coordinate system and the projector coordinate system, respectively:

[0074] X c =M c X w ,

[0075] X p=M p X w ,

[0076] Where X c ={x c y c , z c} T X p ={x p y p , z p} T X w ={x w y w , z w} T M represents the coordinates of the point to be measured in the camera, projector, and world coordinate systems, respectively. c =[R c , t c ], M p =[R p , t p The transformation relationships from the camera coordinate system and projector coordinate system to the world coordinate system include rotation and translation transformations.

[0077] Based on the calculation results, the coordinates of the feature points can be transformed into the projector coordinate system. The coordinates of the graphic to be projected relative to the feature points can be calculated and a projected image can be generated. This image contains all the measurement results and related information that need to be displayed on the surface of the workpiece being measured.

[0078] In practice, factors such as the curvature and reflectivity of the workpiece surface, as well as ambient light, need to be considered, as these factors can all affect the accuracy of the projection. Therefore, adjustments to the projector's focal length, brightness, and contrast may be necessary to optimize the image display on the workpiece. After completing these steps, the digital array projector will accurately map the image onto the corresponding position on the workpiece based on the calculated coordinates. By reading the error value between the projected image beam on the accuracy verification device and the original image, the calibration accuracy can be verified. This error value reflects the projector's accuracy in actual operation and is a crucial indicator for evaluating the calibration effect.

[0079] This embodiment combines a calibration object to design a device that can be used for both projector calibration and accuracy verification. The designed accuracy verification device provides an intuitive method to observe and measure the projector's calibration accuracy. A series of standardized geometric patterns, such as rectangles and triangles, can be designed on the device, and multiple reticles can be set along the paths of the patterns. These reticles can be used to measure the size and position of the projected image. A certain number of marker points are set at equal intervals on the surface of the device, forming a dot matrix of several rows and columns. Reflective dots are pasted at the marker points. By using a CCD camera to capture images of the device and identify the feature points on the device, the calibration of the camera and projector can be achieved. Several positioning holes are set near the edge of the device. These positioning holes are used to install and fix the device, ensuring stability and repeatability during the calibration and verification process.

[0080] As a specific implementation method, such as Figure 3 As shown, the detailed design of the accuracy verification device is as follows: the device measures 1700mm × 1500mm and has 12 positioning holes A1-A12, 21 projection positioning calibration points B1-B21 (reflective cooperative targets), 14 test positions C1-C14, and various projection contours. This device is used to test the maximum positioning error of the projected image and the width of the projected light rays in a digital area array projection system. The 21 projection positioning calibration points are evenly distributed on the device and can be used to calibrate the relative pose of the digital area array projection system and the device in the accuracy evaluation parameter test method of the digital area array projection system. The 14 test positions are evenly distributed on the device, and each test position is equipped with a crosshair reticle. The crosshair reticle is embedded in the test position and projection contour of the projection receiving surface of the device. The crosshair reticle is used in conjunction with an industrial microscope, which can be flexibly adjusted to be above the crosshair reticle to obtain the width of the projected light rays and the maximum positioning error of the projected image in the digital area array projection system. The projection contour adopts a groove design. After the projection light from the digital array projection system is projected onto the projection contour, the positioning error of the projection graphic is characterized by the reading of the magnified cross reticle.

[0081] After projection is complete, the accuracy of the digital area array projector's projection of different patterns can be verified to reflect the projector's calibration accuracy. The projected image is compared with the geometric pattern on the device to measure the projection deviation. Compared to the traditional calibration plate method, this accuracy verification device provides a more intuitive and precise method for accuracy assessment. It can be used not only for projector calibration but also to verify the accuracy of calibration results, offering a more comprehensive and reliable means of accuracy verification.

[0082] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A calibration method for a digital plane array projector based on precision verification device, characterized in that, The method comprises the following steps: a digital plane array projection system is built based on a digital plane array projector and a camera; parameters of the digital plane array projection system are calibrated; initial weights and thresholds of a BP neural network are optimized based on an SBOA algorithm to obtain an SBOA-BP neural network model; calibration errors of the digital plane array projection system are optimized based on the SBOA-BP neural network model; an accuracy verification device is constructed, and calibration results of the digital plane array projection system are evaluated based on the accuracy verification device; the process of optimizing the initial weights and thresholds of the BP neural network based on the SBOA algorithm to obtain the SBOA-BP neural network model comprises: an input layer of the SBOA-BP neural network model is pixel point coordinates in a projector image coordinate system, and an output layer is calibration point coordinates in a world coordinate system, a difference between the output calibration point coordinates in the world coordinate system and actual calibration point coordinates in the world coordinate system is taken as an optimization objective function, and initial weights and thresholds of the BP neural network are iteratively optimized, and when a maximum iteration number is reached, the training is ended, and the optimized SBOA-BP neural network model is obtained; the accuracy verification device comprises a plurality of uniformly distributed projection positioning calibration points, test positions and a plurality of projection profiles; the projection positioning calibration points are used for calibrating relative poses of the digital plane array projection system and the accuracy verification device; the test positions are each provided with a crosshair reticle, and the crosshair reticle is used in cooperation with an industrial microscope to obtain a projection light width and a maximum projection image positioning error of the digital plane array projection system; the projection profile is designed with a groove and is used for receiving a projection light of the digital plane array projection system; the parameters of the digital plane array projection system that need to be calibrated in advance comprise: pinhole model parameter matrices of the camera and the digital plane array projector, nonlinear distortion coefficients of the camera and the digital plane array projector, and rigid transformation parameters between a camera coordinate system and a digital plane array projector coordinate system.

2. The method of claim 1, wherein the process of calibrating parameters of the camera in the digital plane array projection system comprises: parameters of the camera in the digital plane array projection system are calibrated based on Zhang Zhengyou calibration method.

3. The method of claim 1, wherein the process of calibrating parameters of the digital plane array projector in the digital plane array projection system comprises: a calibration board is placed in a working range of the digital plane array projection system, a no-structure light image is shot, then horizontal and vertical sinusoidal fringe structure light is projected and corresponding image sequences are shot by the camera, the shooting process is repeated by transforming the calibration board pose to obtain multiple sets of calibration images, center points of circular target points of the calibration board are extracted from the calibration images, an absolute phase unwrapping technique and a linear interpolation method are used to establish a matching relationship between imaging points on a plane of the digital plane array projector and the center points of the circular target points of the calibration board, and then parameter calibration of the digital plane array projector is completed.

4. The method of claim 1, wherein the process of evaluating the calibration results of the digital plane array projection system based on the accuracy verification device comprises: When the projection light of the digital plane array projection system is projected to the projection profile, the image projected by the digital plane array projection system is compared with the geometric pattern of the projection profile, the positioning error of the projection image is obtained by combining the crosshair reticle reading, and then the calibration result of the digital plane array projection system is evaluated.

5. The method of claim 1, wherein, The edge position of the precision verification device is provided with a plurality of positioning holes for fixing the position of the precision verification device.

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