Digital area array projector-oriented calibration method based on precision verification device

Abstract

The invention discloses a calibration method for a digital area array projector based on a precision verification device. The calibration method comprises the following steps: building a digital area array projection system based on the digital area array projector and a camera; calibrating parameters of the digital area array projection system; optimizing a weight and a threshold value of the BP neural network based on an SBOA algorithm to obtain an SBOA-BP neural network model; the calibration error of the digital area array projection system is optimized based on the SBOA-BP neural network model; and constructing a precision verification device, and evaluating the calibration result of the digital area array projection system based on the precision verification device. The precision verification device is designed, the digital area array projector is calibrated through the device and the camera, then the projection graph is generated, calibration precision verification is achieved, and the problems that a neural network is applied to projector calibration and calibration precision verification is difficult to conduct are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of structured light measurement, and in particular relates to a calibration method for a digital area array projector based on an accuracy verification device. Background Art

[0002] Structured light measurement technology is a non-contact 3D measurement technology. It projects a structured light coded pattern onto the surface of the target object through a projector. Due to the inconsistency of the height of the object surface, the pattern will be deformed. Then the deformed structured light image is captured by a camera, and the image is decoded and processed by a computer to obtain the correspondence between the structured light image points and the projected pattern points. Based on the principle of triangulation, the 3D point cloud data of the object surface is calculated to achieve 3D reconstruction of the surface of the object being measured.

[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, strong robustness, and real-time three-dimensional measurement. Therefore, it has a wide range of applications in industrial automatic inspection, product quality control, reverse design, biomedicine, virtual reality, cultural relic replication, human body measurement and other fields.

[0004] The calibration of cameras and projectors is an important step in structured light measurement technology. The projector can be regarded as the inverse process of the camera, that is, the image is projected from the projector to the object, and then captured by the camera. The three-dimensional information of the object is obtained by projecting a known pattern and analyzing its deformation. Since the projector lens may be distorted and the system parameters may need to be adjusted before each use, calibration is required before each use. During the calibration process, the projection device projects the grating image onto the surface of the object to be measured, and the camera is used to capture the grating image of the surface of the object to be measured. By establishing the correspondence between the projector image and the camera image, the projector calibration is converted into a mature camera calibration, and the projector is calibrated using the camera calibration method. Calibration accuracy is a key indicator for evaluating the calibration quality of projectors and cameras in structured light three-dimensional measurement systems. High-precision calibration can significantly improve the quality of structured light measurement, and verifying the calibration accuracy is a key step to ensure the accuracy of the three-dimensional measurement system.

[0005] There have been many achievements in the application of neural networks to system calibration at home and abroad, mostly focusing on camera calibration or stereo vision system calibration. Unlike cameras, projectors cannot capture images, so it is impossible to directly obtain the relationship between the position of each point on the projected image and its corresponding point in three-dimensional space, so the calibration technology of projectors is still under development.

[0006] In view of the problems in the prior art, it is urgent to propose a calibration method for a digital area array projector based on an accuracy verification device. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a calibration method for a digital area array projector based on an accuracy verification device, applies neural networks to the problem of projector calibration, builds a digital area array projection system using a digital area array projector and a CCD camera, calibrates the system parameters and 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 the device to solve the problems existing in the above-mentioned prior art.

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

[0009] Build a digital area array projection system based on digital area array projector and camera;

[0010] Calibrate the parameters of the digital area array projection system;

[0011] Based on the SBOA algorithm, the weights and thresholds of the BP neural network are optimized to obtain the SBOA-BP neural network model;

[0012] Optimizing the calibration error of the digital area array projection system based on the SBOA-BP neural network model;

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

[0014] Optionally, the parameters of the digital area array projection system that need to be calibrated in advance include:

[0015] The pinhole model parameter matrix of the camera, the pinhole model parameter matrix of the digital area array projector, the nonlinear distortion coefficient of the camera, the nonlinear distortion coefficient 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 parameters of a camera in the digital area array projection system includes:

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

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

[0019] A calibration plate is placed within the working range of a digital area array projection system to capture an unstructured light image. Then, horizontal and vertical sinusoidal stripe structured light are projected and the corresponding image sequence is captured by a camera. The shooting process is repeated by changing the position of the calibration plate to obtain multiple sets of calibration images. The circular target center of the calibration plate is extracted from the calibration image, and the absolute phase unwrapping technology and the linear interpolation method are used to establish a matching relationship between the imaging point on the plane of the digital area array projector and the circular target center of the calibration plate, 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 calibration point coordinates in the output 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 by iteration. 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 a number of evenly distributed projection positioning calibration points, test positions, and a plurality of projection profiles;

[0023] The projection positioning calibration points are used to calibrate the relative position and posture of the digital array projection system and the accuracy verification device;

[0024] The test positions are all equipped with a cross-reticle plate, which is used in conjunction with an industrial microscope to obtain the projection light width and the maximum error of the projection image positioning of the digital area array projection system;

[0025] The projection profile adopts a groove design and is used to receive projection light from a digital area array projection system.

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

[0027] When the projection light of the digital area array projection system is projected onto the projection contour, the image projected by the digital area array projection system is compared with the geometric pattern of the projection contour, and the projection image positioning error is obtained in combination with the cross-reticle reading, thereby evaluating the calibration result of the digital area array projection system.

[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] The present invention proposes a calibration method for a digital area array projector based on an accuracy verification device. The calibration error of the digital area array projection system is optimized by constructing an SBOA-BP neural network model, thereby achieving the purpose of applying the neural network to the projector calibration.

[0031] The present invention constructs an accuracy verification device, and evaluates the calibration result of the digital array projection system based on the accuracy verification device. Compared with the traditional calibration plate method, this accuracy verification device provides a more intuitive and accurate accuracy evaluation method, which can not only be used to calibrate the projector, but also to verify the accuracy of the calibration result, providing a more comprehensive and reliable accuracy verification method. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 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 calculation flow chart of the SBOA-BP neural network model according to an embodiment of the present invention;

[0035] Figure 3 Schematic diagram of a device for verifying the calibration accuracy of a digital area array projector according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

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

[0038] Embodiment 1

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

[0040] Build a digital area array projection system based on digital area array projector and camera;

[0041] Calibrate the parameters of the digital area array projection system;

[0042] Based on the SBOA algorithm, the weights and thresholds of the BP neural network are optimized to obtain the SBOA-BP neural network model;

[0043] Optimizing the calibration error of the digital area array projection system based on the SBOA-BP neural network model;

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

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

[0046] like Figure 1 As shown in the figure, the digital area array projection system is mainly composed of an industrial camera, a digital area array projector, a workpiece to be measured, and a computer software system. The spatial relationship between the camera, projector and workpiece to be measured in the system is established. 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 represents the projection device coordinate system; 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 the optical lens to transform point X in the world coordinate system w Projected onto the CCD imaging plane, point X c The digital micromirror devices (DMD) in the digital area array projector is equivalent to the image plane formed by the CCD or CMOS chip of the camera. The working model of the projector can be regarded as the inverse process of the camera imaging process, that is, from the optical center O of the projector to the image plane of the camera. p Starting from the image point X on the projection plane p The ray is projected to a point X in the world coordinate system w Therefore, both the camera and the projection device can be expressed by the following pinhole model, namely:

[0048]

[0049] In the formula, It is the homogeneous coordinate form of the image point on the camera (or projector) image plane; is the point X in the world coordinate system w Homogeneous coordinate form; s is an arbitrary non-zero scale 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 is the normalized focal length of the camera (or projector) in the horizontal and vertical directions; c x 、c y is the offset coordinate of the camera (or projector) principal point.

[0052] Taking into account the nonlinear distortion effect of the optical lens, the spatial point X w The corresponding actual imaging point is not at the ideal imaging point x(u, v), but moves to x d (u d , v d ), which can be specifically expressed as:

[0053]

[0054] Where r is the distance radius from the image pixel to the lens optical center, r 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 calibrated in advance 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 of the camera d 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 After the internal parameters of the projection device and the camera and the coordinate transformation relationship between the two are determined through the calibration process, the imaging point x on the projection image plane p and the imaging point x on the camera image plane c The matching relationship can be determined by triangulation to uniquely determine the spatial point X w The three-dimensional coordinates in the system coordinate system.

[0056] It is feasible to calibrate the parameters of the camera and the digital area array projector:

[0057] The camera calibration process uses the traditional Zhang Zhengyou calibration method, which has low calibration cost, convenient use, strong anti-interference ability, and its calibration results can meet the accuracy requirements of most measurement occasions, so it is widely used in the camera calibration process. This calibration method is implemented in both Matlab calibration toolbox and OpenCV.

[0058] The calibration process of the projector uses structured light to project a grating image to establish a matching relationship between the imaging point on the projector plane and the center of the circular target on the plane calibration plate. First, place the calibration plate within the working range of the system, and the camera takes an original image of the calibration plate without structured light stripe projection. Then, keep the calibration plate still, and project two sets of sinusoidal stripe structured light images in the horizontal and vertical directions built into the projection device onto the calibration plate in sequence by the projection device. At the same time, the camera obtains the corresponding image sequence of the calibration plate under each structured light projection. Change the position of the calibration plate, repeat the above shooting process, and obtain multiple sets of calibration images. For a series of calibration images taken at each calibration plate position, first extract the pixel coordinates x of the center of each circular target of the calibration plate in the camera image plane from the original calibration plate image. c (u, v), and then perform absolute phase unwrapping on the structured light image sequence of the calibration plate to solve 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 projection image plane. p The (u', v') position can be obtained by:

[0059]

[0060] Where f is the frequency corresponding to the structured light image.

[0061] Since the coordinates of the center of the circle on the calibration plate are obtained by fitting the edge pixels of the ellipse extracted from the image, (u, v) is usually sub-pixel, and the phase values ​​of the integer pixels in the x and y directions can only be obtained during structured light decoding. Therefore, it is necessary to use linear interpolation 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 according to the calibration image.

[0062] The feasible method to optimize the projector calibration accuracy is as follows:

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

[0064] Neural networks have powerful nonlinear approximation capabilities. In the projector calibration process, when the system structure is determined, according to the established system model, the projector image coordinates, camera image coordinates and the corresponding three-dimensional coordinates have a fixed one-to-one mapping relationship. Ignoring the complex system model, the neural network can be used to approximate the structured light system model and directly establish the mapping relationship.

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

[0066] Secretary Bird Optimization Algorithm (SBOA) is a new meta-heuristic algorithm with excellent performance in terms of solution quality, convergence speed and stability. The algorithm is inspired by the survival behavior of secretary birds in natural environments, especially their behavior of preying on snakes and escaping from predators.

[0067] The SBOA algorithm is divided into two main phases: exploration and exploitation. The exploration phase simulates a heron hawk preying on a snake, including the process of finding prey, consuming prey, and attacking prey. The exploitation phase simulates a heron hawk escaping from a predator, where the heron hawk observes the environment and chooses the most appropriate escape strategy, such as running away or camouflage.

[0068] The initial preparation phase of the algorithm involves randomly initializing the positions of the eagles in the search space, with each eagle representing a candidate solution. In the hunting strategy, the eagle uses its keen eyesight to find prey and takes quick action once it finds the prey. This process is achieved by updating the positions of the eagles through a mathematical model, which includes the use of weighted Levy flight to improve the optimization accuracy. In the escape strategy, the eagle may take two actions: using environmental camouflage or running or flying quickly to escape from the predator. A dynamic perturbation factor is introduced in the algorithm to balance exploration and exploitation to achieve the solution of 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 search for the global optimal solution, which can enhance the randomness and diversity of particles.

[0070] like Figure 2 As shown, in order to improve the simulation ability of the BP neural network model, this embodiment constructs the SBOA-BP neural network model. The initial weights and thresholds in the BP neural network training process are generated by random numbers, which have an impact on the trained network structure. The SBOA algorithm is used to optimize the initial weights and thresholds of the BP neural network, thereby obtaining a relatively stable SBOA-BP neural network model. In the SBOA algorithm, the relevant parameters must first be initialized, including the initial data scale, the number of independent variables, the upper and lower limits of the independent variables, etc., and the initial fitness value is calculated. The fitness value represents the degree of adaptation of the parameter scheme after optimization, 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 meet the conditions. If so, exit and output the result, otherwise repeat the above process.

[0071] The input layer of the SBOA-BP neural network model used for optimizing the calibration accuracy of the projector includes the pixel coordinates in the projector image coordinate system, and the output layer is the world coordinates of the points to be measured. The difference between the world coordinates of the calibration points output by the neural network and the actual world coordinates of the calibration points is used as the optimization objective function. The weights and thresholds of the neural network are continuously optimized by iteration. When the set maximum number of iterations is reached, the training ends and the optimization is considered completed.

[0072] Feasible projector calibration accuracy verification method and device:

[0073] To verify the calibration accuracy of the projector, first use a CCD camera to capture an image of the object to be tested, identify the feature points on the object to be tested, and select a calibration feature point in a calibration image as the center of the world coordinate system. w , respectively determine X w , Y w , Z w , the relationship between the world coordinate system and the camera coordinate system and the projector coordinate system can be determined according to the following formula:

[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 are the coordinates of the point to be measured in the camera, projector and world coordinate systems respectively, M c =[R c , t c ],M p =[R p , t p ] is the transformation relationship from the camera coordinate system and the projector coordinate system to the world coordinate system, including rotation and translation transformations respectively.

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

[0078] In actual operation, factors such as the curvature of the workpiece surface, reflective characteristics, and ambient light need to be considered, all of which may affect the accuracy of the projection. Therefore, the focal length, brightness, and contrast of the projector may need to be adjusted to optimize the display effect of the image on the workpiece. After completing the above work, the digital area array projector will accurately map the image to the corresponding position of the measured workpiece according to the calculated coordinates. The calibration accuracy can be verified by reading the error value between the graphic beam projected on the accuracy verification device and the original graphic. This error value reflects the accuracy of the projector in actual work and is an important indicator for evaluating the calibration effect.

[0079] In this embodiment, a device that can be used for both projector calibration and accuracy verification is designed in combination with a calibration object. The designed accuracy verification device can provide an intuitive method to observe and measure the calibration accuracy of the projector. A series of standardized geometric patterns, such as rectangles and triangles, can be designed on the device, and multiple graticules are set on the path of the graphics. These graticules can be used to measure the size and position of the projected image; a certain number of marking points are set at equal intervals on the surface of the device to form a dot matrix of several rows and columns, and reflective points are pasted at the marking points. The image of the device is captured by a CCD camera, and the feature points on the device are identified, so as to realize the calibration of the camera and the projector; a number of positioning holes are set near the edge of the device, and these positioning holes are used to install fixing devices to ensure stability and repeatability during the calibration and verification process.

[0080] As a specific implementation method, Figure 3 As shown in the figure, the details of the specifically designed precision verification device are as follows: the device size is 1700mm×1500mm, with 12 positioning holes A1-A12, 21 projection positioning calibration points B1-B21 (reflective cooperative targets), 14 test positions C1-C14 and multiple projection profiles. The device is used to test the maximum error of the projection pattern positioning and the projection light width of the digital array projection system. The 21 projection positioning calibration points are evenly distributed on the device, which can be used to calibrate the relative position of the digital array projection system and the device in the precision evaluation parameter test method of the digital array projection system. The 14 test positions are evenly distributed on the device, and each test position is equipped with a cross-reticle plate, which is embedded in the test position and projection profile of the projection receiving surface of the device. The cross-reticle plate is used with an industrial microscope, and the industrial microscope can be flexibly adjusted to the top of the cross-reticle plate to obtain the projection light width and the maximum error of the projection pattern positioning of the digital array projection system. The projection contour adopts a groove design. After the projection light of the digital area array projection system is projected onto the projection contour, the positioning error of the projection figure is characterized by combining the magnified cross-reticle reading.

[0081] After the projection is completed, the calibration accuracy of the projector can be reflected by verifying the accuracy of the projection when the digital area array projector projects different patterns. The image projected by the projector is compared with the geometric pattern on the device to measure the deviation of the projection. Compared with the traditional calibration plate method, this accuracy verification device provides a more intuitive and accurate accuracy assessment 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 method.

[0082] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A calibration method for a digital area array projector based on an accuracy verification device, characterized in that: The following steps are involved: Build a digital area array projection system based on digital area array projector and camera; Calibrate the parameters of the digital area array projection system; Based on the SBOA algorithm, the weights and thresholds of the BP neural network are optimized to obtain the SBOA-BP neural network model; Optimizing the calibration error of the digital area array projection system based on the SBOA-BP neural network model; An accuracy verification device is constructed, and the calibration result of the digital area array projection system is evaluated based on the accuracy verification device.

2. The method according to claim 1, characterized in that The parameters of the digital area array projection system that need to be calibrated in advance include: The pinhole model parameter matrix of the camera, the pinhole model parameter matrix of the digital area array projector, the nonlinear distortion coefficient of the camera, the nonlinear distortion coefficient 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.

3. The method according to claim 1, characterized in that The process of calibrating the parameters of the camera in the digital area array projection system includes: The parameters of the camera in the digital area array projection system are calibrated based on Zhang Zhengyou's calibration method.

4. The method according to claim 1, characterized in that: The process of calibrating parameters of the digital area array projector in the digital area array projection system includes: A calibration plate is placed within the working range of a digital area array projection system to capture an unstructured light image. Then, horizontal and vertical sinusoidal stripe structured light are projected and the corresponding image sequence is captured by a camera. The shooting process is repeated by changing the position of the calibration plate to obtain multiple sets of calibration images. The circular target center of the calibration plate is extracted from the calibration image, and the absolute phase unwrapping technology and the linear interpolation method are used to establish a matching relationship between the imaging point on the plane of the digital area array projector and the circular target center of the calibration plate, thereby completing the parameter calibration of the digital area array projector.

5. The method according to claim 1, characterized in that The process of optimizing the initial weights and thresholds of the BP neural network based on the SBOA algorithm and obtaining the SBOA-BP neural network model includes: 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 calibration point coordinates in the output 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 by iteration. When the maximum number of iterations is reached, the training ends and the optimized SBOA-BP neural network model is obtained.

6. The method according to claim 1, characterized in that The accuracy verification device includes a number of evenly distributed projection positioning calibration points, test positions and a variety of projection profiles; The projection positioning calibration points are used to calibrate the relative position and posture of the digital array projection system and the accuracy verification device; The test positions are all equipped with a cross-reticle plate, which is used in conjunction with an industrial microscope to obtain the projection light width and the maximum error of the projection image positioning of the digital area array projection system; The projection profile adopts a groove design and is used to receive projection light from a digital area array projection system.

7. The method according to claim 6, characterized in that The process of evaluating the calibration result of the digital area array projection system based on the accuracy verification device includes: When the projection light of the digital area array projection system is projected onto the projection contour, the image projected by the digital area array projection system is compared with the geometric pattern of the projection contour, and the projection image positioning error is obtained in combination with the cross-reticle reading, thereby evaluating the calibration result of the digital area array projection system.

8. The method according to claim 6, characterized in that A plurality of positioning holes are arranged at the edge of the precision verification device to fix the position of the precision verification device.

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