Multi-surface reflection error correction method and equipment for pressure-sensitive coating measurement

Abstract

The invention relates to a pressure-sensitive coating measurement-oriented multi-surface reflection error correction method and equipment, which introduces a micro light ray tracing technology to perform analogue simulation on the process from pressure-sensitive coating luminescence to camera imaging, and obtains a multi-surface reflection error correction result based on known parameters such as pressure-sensitive coating reflectivity, image resolution, relative position of camera and model placement and the like. And accurately analyzing the real luminous intensity of the pressure-sensitive coating on the surface of the model through a back propagation algorithm. And finally, adjusting the reflection parameter of the pressure-sensitive coating to be zero, and directly generating an image without reflection influence under the imaging visual angle of the camera, thereby effectively eliminating multi-surface reflection interference, maintaining the corresponding relation between image pixel points and the surface of the model unchanged, and avoiding an additional image registration step. Compared with the prior art, the method has the advantages of high measurement precision, wide application scene, low cost and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of aerodynamics and fluid mechanics testing, and in particular to a multi-surface reflection error correction method and device for pressure-sensitive coating measurement. Background Art

[0002] Pressure Sensitive Paint (PSP) is an advanced optical measurement technology that measures the pressure change on the surface of the model to be tested by spraying special paint on the surface of the model and using UV light source to excite it, using the "oxygen quenching" effect in the photoluminescence process. Compared with the traditional contact pressure measurement method, this technology has the advantages of high spatial resolution, low cost and good model adaptability, and has been widely used in aerodynamic experiments.

[0003] At the corners of the model (such as the junction between the wing and the fuselage), the PSP emitted light is prone to mutual reflection on adjacent surfaces. Such mixed reflection signal images cannot be eliminated by image comparison or in-situ calibration in areas with large pressure gradient changes, which will seriously affect the measurement accuracy of the PSP technology. Therefore, it is urgent to correct the errors of such images with multi-surface reflection problems in order to obtain the real light signal of the surface to be measured and realize high-precision measurement of PSP.

[0004] The error correction method for multi-surface reflection that is widely used at present is mainly based on the calculation of large linear equations, which places high demands on the number and curvature changes of three-dimensional triangular meshes, the correspondence between three-dimensional meshes and two-dimensional images, etc. In addition, since there is no one-to-one correspondence between two-dimensional pixels and three-dimensional triangular facets, a large amount of interpolation and approximation is required in the correction process. Therefore, the existing methods have high computational costs and many practical application limitations, and cannot achieve accurate and reliable correction of multi-surface reflection errors of pressure-sensitive coatings.

[0005] In summary, there is currently a lack of a multi-surface reflection error correction method to solve or partially solve the above-mentioned problems. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a multi-surface reflection error correction method and device for pressure-sensitive coating measurement, so as to realize accurate and reliable correction of multi-surface reflection errors of pressure-sensitive coatings.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] One aspect of the present invention provides a multi-surface reflection error correction method for pressure-sensitive coating measurement, comprising the following steps:

[0009] Obtaining the reflectivity of the pressure-sensitive coating at the wavelength of the emitted light;

[0010] Acquire triangular mesh data of the multi-surface area to be measured;

[0011] Acquire a real luminous image of the multi-surface test area sprayed with pressure-sensitive paint, and camera parameters when photographing the real luminous image;

[0012] Initialize the luminous intensity of each triangular facet in the area to be measured based on the triangular mesh data and the camera parameters, obtain a simulated image of the multi-surface area to be measured corresponding to the reflectivity of the wavelength of the emitted light through differentiable ray tracing, and iteratively optimize the simulated image with the goal of minimizing the simulation error between the simulated image and the real luminous image;

[0013] Based on the simulation image after iterative optimization, the reflectivity of the pressure-sensitive coating is configured to zero, and the correction image when the surface reflection is zero is obtained by differentiable ray tracing.

[0014] As a preferred technical solution, the calculation process of the simulation error includes the following steps:

[0015] Through multi-scale Gaussian filtering and downsampling, the difference information between the simulated image and the real luminous image at different scales is captured, and the simulation error is calculated.

[0016] As a preferred technical solution, the simulation error is calculated using the following formula:

[0017]

[0018]

[0019] Among them, Img_1 is the real luminous image, Img_2 n represents the simulated image obtained by differentiable ray tracing technology at the nth iteration, Diff represents the difference image at different scales, AvgPool 2×2 is an average pooling layer of size 2×2, gf 7×7 is a Gaussian filter kernel of size 7×7. During filtering, uniform zero padding is performed on the image boundary. Padding is the padding value. h and w represent the number of pixels of the height and width of the original image, respectively. Loss is the simulation error.

[0020] As a preferred technical solution, during the iterative optimization process, the light intensity of each pixel is updated using the following formula:

[0021] I=∫∫f(u,v;C,E)dudv

[0022] Among them, I is the light intensity information of the pixel, f is the scene function including the camera, model and light source parameters, C and E are the camera parameters and the luminous intensity of each triangle respectively.

[0023] As a preferred technical solution, when the simulation error is less than a preset threshold, or the number of iterations exceeds a set value, the iterative optimization is terminated.

[0024] As a preferred technical solution, the camera parameters include image resolution, camera position, camera orientation, camera field of view and height direction of the camera taking images.

[0025] As a preferred technical solution, by configuring the size of the triangular facets of the multi-surfaces, each triangular facet can reflect the luminous intensity of the corresponding area.

[0026] As a preferred technical solution, the process of measuring the reflectivity of the emission light wavelength includes the following steps:

[0027] The reflectivity of the pressure sensitive coating in the emission light band is measured using an integrating sphere.

[0028] Another aspect of the present invention provides an electronic device, comprising: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the aforementioned multi-surface reflection error correction method for pressure-sensitive coating measurement.

[0029] Another aspect of the present invention provides a computer-readable storage medium comprising one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the aforementioned multi-surface reflection error correction method for pressure-sensitive coating measurement.

[0030] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0031] (1) High measurement accuracy: The present invention overcomes the shortcomings of the prior art, such as large amount of calculation and many model limitations. It significantly improves the final measurement accuracy through a refined error correction process. The entire correction process is simple and practical, and has a wide range of applicability. It can effectively cope with the multi-surface reflection challenges of various complex models based on pressure-sensitive coating measurements, and provides strong support for achieving high-precision and high-reliability measurements of pressure-sensitive coatings.

[0032] (2) Wide application scenarios and low cost: The present invention only needs to ensure the size of the three-dimensional triangular mesh of the model to be tested and its reconstruction accuracy. There are no additional restrictions on factors such as the change amplitude of the model surface curvature and the number of multiple surfaces. There is no need to perform mutual mapping between the two-dimensional image and the three-dimensional model, which reduces interpolation calculations and significantly reduces computing costs. While effectively eliminating the interference of multi-surface reflections, the correspondence between the image pixels and the model surface is maintained unchanged, avoiding additional image registration steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic flow chart of a multi-surface reflection error correction method for pressure-sensitive coating measurement in an embodiment;

[0034] Figure 2 It is a schematic diagram of the model to be tested in the embodiment;

[0035] Figure 3 Schematic diagram of light intensity distribution of the original image (including reflection error);

[0036] Figure 4 Schematic diagram of the difference between the original image and the true value;

[0037] Figure 5 Schematic diagram of light intensity distribution of the original image after error correction of the present invention;

[0038] Figure 6 It is a schematic diagram of the difference between the original image after the error correction of the present invention and the true value;

[0039] Figure 7 Schematic diagram of an electronic device in an embodiment. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] Example 1

[0042] In view of the problems existing in the aforementioned prior art and in order to overcome the defects of the prior methods in that the correction is based on solving large sets of linear equations, the present embodiment provides a multi-surface reflection error correction method for pressure-sensitive coating measurement based on differentiable ray tracing, which can remove the restrictions on the number and curvature of three-dimensional triangular meshes, get rid of the dependence on the mapping relationship between two-dimensional and three-dimensional, and directly perform pixel-by-pixel error correction based on real-shot images containing multi-surface reflections.

[0043] See also Figure 1 , the method comprises the following steps:

[0044] S1. Obtain the reflectivity R of the pressure-sensitive coating at the wavelength of the emitted light. The reflectivity R must be measured strictly at the emitted light band of the pressure-sensitive coating.

[0045] S2. Obtain triangular mesh data of the area to be tested in the multi-surface three-dimensional model. The size of the triangular facets should ensure that each triangular facet can fully represent the luminous intensity of its corresponding area.

[0046] S3. Spray pressure-sensitive coating on the model to be tested area, use the camera to obtain the luminous image Img_1 of the model surface under the experimental conditions, and record the camera parameters

[0047] C=[Res,Pos,Look_at,fov,Up]

[0048] Among them, Res represents the image resolution, Pos represents the camera position, Look_at represents the camera orientation, fov represents the camera field of view, and Up represents the height direction of the camera taking the image.

[0049] S4. Obtain the actual luminous intensity of the triangular facets: initialize the luminous intensity E of all triangular facets, and use the differentiable ray tracing technology to obtain the simulated image Img_2; and use Img_1 as the optimization target, and continuously iteratively update the luminous intensity E of each triangular facet through the back propagation algorithm to ensure that the simulation error between the image Img_2 finally obtained by simulation and the actual image Img_1 is minimized.

[0050] Preferably, the simulation error calculation can capture the difference information of the image at different resolutions through multi-scale Gaussian filtering and downsampling, providing a more comprehensive and accurate difference measurement. A specific simulation error calculation example is:

[0051]

[0052] Where n is the number of iterations; h and w are the number of pixels of the original image height and width respectively; Img_2 n represents the simulated image obtained by differentiable ray tracing technology at the nth iteration; Diff represents the difference image at different scales; AvgPool 2×2 is an average pooling layer of size 2×2; gf 7×7 is a Gaussian filter kernel of size 7×7, and uniform zero padding (padding=3) is performed on the image boundary during filtering.

[0053] S5. Based on the actual luminous intensity E of each triangular facet finally obtained in step S4, the reflectivity of the pressure-sensitive coating is set to zero, and the differentiable ray tracing technology is used to obtain a simulated image Img_3 (i.e., an image after reflection error correction) when the surface reflection is zero.

[0054] Preferably, the gradient calculation method based on back propagation can be manually returned or automatically calculated based on the automatic differentiator (Autograd) in PyTorch. The light intensity information I of each pixel can be expressed as

[0055] I=∫∫f(u,v;C,E)dudv

[0056] Wherein, u and v are the horizontal and vertical coordinates of the image pixels, respectively, and f is a scene function including parameters related to the camera, model, light source, etc. Iterative optimization algorithms can be selected including but not limited to: SGD, Adam, etc.

[0057] The following is an actual example to illustrate this method. In this example, the pressure-sensitive coating technology is used to measure the surface pressure of the tail part of an aircraft, and this method is used to correct the multi-surface reflection error to obtain the true luminous value of the pressure-sensitive coating after correcting the reflection error.

[0058] Step S1: using an integrating sphere to measure the reflectivity R=0.75 of the batch of pressure-sensitive coating at the wavelength of emitted light;

[0059] Step S2: Obtain the three-dimensional model of the tail part of the aircraft, and extract the three-dimensional triangular mesh data of the area to be tested, such as Figure 2 As shown, the white area is the ROI area;

[0060] Step S3: spray the same batch of pressure-sensitive coating as in step S1 on the test area of ​​the tail wing of the aircraft, and use a camera to obtain the luminous image Img_1 (such as Figure 3 As shown, the difference between it and the true value image is as follows Figure 4 As shown), record the camera parameters

[0061] C=[Pos,Look_at,fov,Up],

[0062] in

[0063] Pos = [100,100,100];

[0064] Look_at = [0.0, 0.0, -50];

[0065] fov=25;

[0066] Up = [1.00, 0.0, 0.0];

[0067] Step S4: Initialize the luminous intensity of all triangles in the ROI area of ​​the three-dimensional model in S2 to

[0068] E0=0.5×I N×1 ,

[0069] Among them, I N×1represents a unit matrix of size N×1, where N=60646 is the number of triangular facets. Then, all triangular facets are used as light sources to simulate and generate an image Img_2 containing multi-surface reflections; Img_1 obtained in S3 is used as the optimization target, and based on the camera parameter C recorded in step S3 and the reflectivity R=0.75 of the pressure-sensitive coating measured in step S1, the actual luminous intensity E1 of each triangular facet on the model surface is obtained through iterative updating;

[0070] Step S5: Based on the actual luminous intensity E1 of each triangular facet on the model surface obtained in step S5, the reflectivity of the pressure-sensitive coating is set to 0, and a luminous image under non-reflective conditions is obtained, such as Figure 5 As shown in , the image is the image after error correction. At this time, the difference between the image and the true value is as follows: Figure 6 shown.

[0071] By comparison Figure 4 and Figure 6 , the result corrected by this method is significantly close to the true value without reflection, and a good error correction result of multi-surface reflection is obtained. This method significantly improves the final measurement accuracy through a refined error correction process, and the entire correction process is relatively simple and practical, with wide applicability, and can effectively cope with the multi-surface reflection challenges of various complex models based on pressure-sensitive coating measurement.

[0072] In summary, this method first uses an integrating sphere to measure the reflectivity of the current pressure-sensitive coating at the wavelength of its emitted light, obtains the three-dimensional mesh data of the model surface, sprays the pressure-sensitive coating on the model surface, and uses an excitation light source to excite the pressure-sensitive coating on the model surface and photograph it under experimental conditions; then, the ROI area in the actual image is extracted, and the camera parameters are recorded; subsequently, based on the recorded camera parameters and the reflectivity of the current pressure-sensitive coating at the wavelength of its emitted light, the actual image is taken as the true value, and the luminous intensity of the triangular facet is continuously adjusted through iteration until the optimal luminous intensity that meets the requirements is obtained; finally, the reflectivity of the pressure-sensitive coating is set to 0, and the image after multi-surface reflection error correction is obtained.

[0073] Compared with the traditional correction method based on complex linear equations, this method only needs to ensure the size of the three-dimensional triangular mesh of the model to be measured and its reconstruction accuracy. There are no additional restrictions on factors such as the change amplitude of the model surface curvature and the number of multiple surfaces. There is no need to map the two-dimensional image to the three-dimensional model, which reduces the interpolation calculation and significantly reduces the calculation cost. This method not only overcomes the shortcomings of the existing technology of large calculation amount and many model restrictions, but also significantly improves the final measurement accuracy through a refined error correction process. The entire correction process is simple and practical, with wide applicability. It can effectively cope with the multi-surface reflection challenges of various complex models based on pressure-sensitive coating measurement, and provides strong support for the high-precision and high-reliability measurement of pressure-sensitive coatings.

[0074] Example 2

[0075] This embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the multi-surface reflection error correction method for pressure-sensitive coating measurement as described in Example 1.

[0076] See also Figure 7 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Of course, in addition to the software implementation, the present invention does not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0077] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0078] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0079] Example 3

[0080] This embodiment provides a computer-readable storage medium, including one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the multi-surface reflection error correction method for pressure-sensitive coating measurement as described in Example 1.

[0081] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0083] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A multi-surface reflection error correction method for pressure-sensitive coating measurement, characterized in that: The steps include: Obtaining the reflectivity of the pressure-sensitive coating at the wavelength of the emitted light; Acquire triangular mesh data of the multi-surface area to be measured; Acquire a real luminous image of the multi-surface test area sprayed with pressure-sensitive paint, and camera parameters when photographing the real luminous image; Initialize the luminous intensity of each triangular facet in the area to be measured based on the triangular mesh data and the camera parameters, obtain a simulated image of the multi-surface area to be measured corresponding to the reflectivity of the wavelength of the emitted light through differentiable ray tracing, and iteratively optimize the simulated image with the goal of minimizing the simulation error between the simulated image and the real luminous image; Based on the simulation image after iterative optimization, the reflectivity of the pressure-sensitive coating is configured to zero, and the correction image when the surface reflection is zero is obtained by differentiable ray tracing.

2. The multi-surface reflection error correction method for pressure-sensitive coating measurement according to claim 1, characterized in that: The calculation process of the simulation error includes the following steps: Through multi-scale Gaussian filtering and downsampling, the difference information between the simulated image and the real luminous image at different resolutions is captured, and the simulation error is calculated.

3. The multi-surface reflection error correction method for pressure-sensitive coating measurement according to claim 2, characterized in that: The simulation error is calculated using the following formula: Among them, Img_1 is the real luminous image, Img_2 n represents the simulated image obtained by differentiable ray tracing technology at the nth iteration, Diff represents the difference image at different scales, AvgPool 2×2 is an average pooling layer of size 2×2, gf 7×7 is a Gaussian filter kernel of size 7×7. During filtering, uniform zero padding is performed on the image boundary. Padding is the padding value. h and w represent the number of pixels of the height and width of the original image, respectively. Loss is the simulation error.

4. The multi-surface reflection error correction method for pressure-sensitive coating measurement according to claim 1, characterized in that: During the iterative optimization process, the light intensity of each pixel is updated using the following formula: I=∫∫f(u,v;C,E)dudv Among them, I is the light intensity information of the pixel, f is the scene function including the camera, model and light source parameters, C and E are the camera parameters and the luminous intensity of each triangle respectively.

5. The multi-surface reflection error correction method for pressure-sensitive coating measurement according to claim 1, characterized in that: When the simulation error is less than the preset threshold or the number of iterations exceeds the set value, the iterative optimization is terminated.

6. The multi-surface reflection error correction method for pressure-sensitive coating measurement according to claim 1, characterized in that: The camera parameters include image resolution, camera position, camera orientation, camera field of view, and the height direction of the camera taking the image.

7. The multi-surface reflection error correction method for pressure-sensitive coating measurement according to claim 1, characterized in that: By configuring the size of the triangles of the multi-surface, each triangle can reflect the luminous intensity of the corresponding area.

8. The multi-surface reflection error correction method for pressure-sensitive coating measurement according to claim 1, characterized in that: The process of measuring the reflectivity of the emission light wavelength comprises the following steps: The reflectivity of the pressure sensitive coating in the emission light band is measured using an integrating sphere.

9. An electronic device, characterized in that: include: One or more processors and a memory, wherein the memory stores one or more programs, wherein the one or more programs include instructions for executing the multi-surface reflection error correction method for pressure-sensitive coating measurement as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: It comprises one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the multi-surface reflection error correction method for pressure-sensitive coating measurement as claimed in any one of claims 1-8.

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