A multi-surface reflection error correction method and apparatus for pressure sensitive paint measurements

Abstract

The present application relates to a kind of multi-surface reflection error correction method and equipment for pressure sensitive paint measurement, introduce the process of "pressure sensitive paint luminescence to camera imaging" is simulated by micro light ray tracing technique, based on the known pressure sensitive paint reflectivity, image resolution, camera and model relative position such as parameter, accurate analysis model surface pressure sensitive paint real luminous intensity by back propagation algorithm.Finally, by adjusting the reflection parameter of pressure sensitive paint to zero, directly generate the image without reflection influence under the view angle of camera imaging, so that the corresponding relationship between image pixel and model surface is maintained unchanged while effectively eliminating the interference of multi-surface reflection, avoid additional image registration step.Compared with prior art, the present application has the advantages of high measurement accuracy, wide application scenarios, low cost and the like.

Description

Technical Field

[0001] This invention relates to the field of aerodynamics and fluid dynamics testing technology, and in particular to a method and device for correcting multi-surface reflection errors in pressure-sensitive coating measurements. Background Technology

[0002] Pressure-sensitive paint (PSP) is an advanced optical measurement technique that measures pressure changes on a model's surface by spraying a special coating onto the model and exciting it with a UV light source. The "oxygen quenching" effect during photoluminescence is utilized to measure these changes. Compared to traditional contact pressure measurement methods, this technique offers advantages such as high spatial resolution, low cost, and good model adaptability, and has been widely applied in aerodynamic experiments.

[0003] At model corners (such as the junction of the wing and fuselage), the emitted light from the PSP (Pressure Sensor) is prone to reflection from adjacent surfaces. These mixed reflection signals cannot be eliminated by image comparison or in-situ calibration in areas with large pressure gradient changes, severely impacting the measurement accuracy of PSP technology. Therefore, error correction is urgently needed for images with multi-surface reflection problems to obtain the true light signal from the surface under test and achieve high-precision PSP measurement.

[0004] Currently widely used error correction methods for multi-surface reflection mainly rely on the calculation of large linear equation systems, which place high demands on the number and curvature variations of the 3D triangular mesh, as well as the correspondence between the 3D mesh and the 2D image. Furthermore, since the 2D pixels and 3D triangular faces are not in a one-to-one correspondence, a large amount of interpolation and approximation is required during the correction process. Therefore, existing methods have high computational costs, many practical limitations, and cannot achieve accurate and reliable correction of multi-surface reflection errors in 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 aforementioned problems. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art by providing a method and device for correcting multi-surface reflection errors in pressure-sensitive coatings, so as to achieve accurate and reliable correction of multi-surface reflection errors in pressure-sensitive coatings.

[0007] The objective of this invention can be achieved through 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] Obtain the reflectivity of the pressure-sensitive coating at the emitted light wavelength;

[0010] Acquire triangular mesh data of the multi-surface test area;

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

[0012] Based on the triangular mesh data and the camera parameters, the luminous intensity of each triangular facet in the region under test is initialized. By differentiable ray tracing, a simulation image of the multi-surface region under test corresponding to the reflectivity of the emitted light wavelength is obtained. With the goal of minimizing the simulation error between the simulation image and the real luminous image, the simulation image is iteratively optimized.

[0013] Based on the iteratively optimized simulation image, the reflectivity of the pressure-sensitive coating is configured to zero, and a corrected image with zero surface reflection is obtained by micro-ray tracing.

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

[0015] By employing multi-scale Gaussian filtering and downsampling, the differences between simulated and real luminescent images at different scales are 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] Where Img_1 is the actual luminescent image, and Img_2 is the actual luminescent image. n This represents the simulated image obtained using differentiable ray tracing technology at the nth iteration, where Diff represents the difference image at different scales, and AvgPool represents the difference image at different scales. 2×2 For an average pooling layer of size 2×2, gf 7×7 The filter kernel is a 7×7 Gaussian filter. During filtering, uniform zero padding is applied to the image boundaries. Padding is the padding value, h and w represent the number of pixels in the height and width of the original image, respectively, and 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] Where I represents the light intensity information of a pixel, f is a scene function containing camera, model, and light source parameters, and C and E are the camera parameters and the light intensity of each triangular facet, respectively.

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

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

[0025] As a preferred technical solution, by configuring the size of the multi-surface triangular facets, 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 emitted light wavelength includes the following steps:

[0027] The reflectivity of pressure-sensitive coatings in the emitted light band was measured using an integrating sphere.

[0028] In another aspect, an electronic device is provided, comprising: one or more processors and a memory, the memory storing one or more programs, the one or more programs including instructions for performing the aforementioned multi-surface reflection error correction method for pressure-sensitive coating measurements.

[0029] In another aspect, the present invention provides a computer-readable storage medium including one or more programs executable by one or more processors of an electronic device, said one or more programs including instructions for performing the aforementioned multi-surface reflection error correction method for pressure-sensitive coating measurements.

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

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

[0032] (2) Wide range of applications and low cost: This invention only needs to ensure the size of the three-dimensional triangular mesh of the model under test and its reconstruction accuracy. It does not impose additional restrictions on factors such as the change range of the curvature of the model surface and the number of multiple surfaces. Moreover, it does not require mutual mapping between two-dimensional images and three-dimensional models, which reduces interpolation calculations and significantly reduces computational costs. While effectively eliminating the interference of multi-surface reflections, it maintains the correspondence between image pixels and model surfaces, avoiding additional image registration steps. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the multi-surface reflection error correction method for pressure-sensitive coating measurement in the embodiment.

[0034] Figure 2 This is a schematic diagram of the model under test in the embodiment;

[0035] Figure 3 This is a schematic diagram of the light intensity distribution in the original image (including reflection errors);

[0036] Figure 4 This is a diagram illustrating the difference between the original image and the true value.

[0037] Figure 5 This is a schematic diagram of the light intensity distribution of the original image after error correction according to the present invention.

[0038] Figure 6 This is a schematic diagram showing the difference between the original image after error correction according to this invention and the true value.

[0039] Figure 7 This is a schematic diagram of the electronic device in the embodiment. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0041] Example 1

[0042] To address the aforementioned problems in the existing technology and overcome the shortcomings of existing methods that rely on solving large linear equations for correction, this embodiment provides a multi-surface reflection error correction method for pressure-sensitive coating measurement based on micro-ray tracing. This method can remove the limitations on the number and curvature of three-dimensional triangular meshes, eliminate the dependence on two-dimensional and three-dimensional mapping relationships, and directly perform pixel-by-pixel error correction based on real-world images containing multi-surface reflections.

[0043] See Figure 1 This method includes the following steps:

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

[0045] S2. Obtain the triangular mesh data of the test area of ​​the multi-surface 3D model. The size of the triangular facets should ensure that each triangular facet can adequately represent the luminous intensity of its corresponding area.

[0046] S3. Spray pressure-sensitive coating onto the test area of ​​the model, use a camera to acquire the luminescence image Img_1 of the model surface under experimental conditions, and record the camera parameters.

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

[0048] Where 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 image captured by the camera.

[0049] S4. Obtain the actual luminous intensity of the triangular facets: Initialize the luminous intensity E of all triangular facets, and obtain the simulated image Img_2 using differentiable ray tracing technology; with Img_1 as the optimization target, continuously update the luminous intensity E of each triangular facet through backpropagation algorithm to ensure that the simulation error between the final simulated image Img_2 and the real image Img_1 is minimized.

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

[0051]

[0052] Where n represents the number of iterations; h and w represent the number of pixels in the height and width of the original image, respectively; Img_2 n The image obtained using differentiable ray tracing technology at the nth iteration represents the simulated image; Diff represents the difference image at different scales; AvgPool 2×2 For an average pooling layer of size 2×2; gf 7×7 It uses a Gaussian filter kernel of size 7×7, and performs uniform zero padding (padding=3) at the image boundaries during filtering.

[0053] S5. Based on the actual luminous intensity E of each triangular facet obtained in step S4, the reflectivity of the pressure-sensitive coating is set to zero. The simulation image Img_3 (i.e., the image after reflection error correction) when the surface reflection is zero is obtained using the differential ray tracing technology.

[0054] Preferably, the gradient calculation method based on backpropagation can be performed manually or automatically using the automatic differentiator (Autograd) in PyTorch. The light intensity information I of each pixel can be represented as...

[0055] I=∫∫f(u,v;C,E)dudv

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

[0057] The following example illustrates this method. In this example, pressure-sensitive coating technology is used to measure the surface pressure of the tail section of an aircraft, and this method is used to correct the multi-surface reflection error to obtain the true value of the luminescence of the pressure-sensitive coating after the reflection error is corrected.

[0058] Step S1: Use an integrating sphere to measure the reflectance of this batch of pressure-sensitive coatings at the emitted light wavelength, R = 0.75;

[0059] Step S2: Obtain the 3D model of the tail section of the aircraft and extract the 3D triangular mesh data of the area to be tested, such as... Figure 2 As shown, the white area represents the ROI region;

[0060] Step S3: Spray the same batch of pressure-sensitive coating as in Step S1 onto the test area of ​​the tail section of the aircraft, and use a camera to acquire an image Img_1 of the luminescence on the model surface under experimental conditions (e.g., ...). Figure 3 As shown, the difference between it and the true image is as follows: Figure 4 (As shown), record 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 triangular facets in the ROI region of the 3D model in S2 to...

[0068] E0 = 0.5 × I N×1 ,

[0069] Among them, I N×1Let N represent an N×1 identity matrix, where N = 60646 is the number of triangular facets. Then, using all triangular facets as light sources, an image Img_2 containing multi-surface reflections is simulated. Using Img_1 obtained in step S3 as the optimization target, and based on the camera parameters C recorded in step S3 and the pressure-sensitive coating reflectivity R = 0.75 measured in step S1, the actual luminous intensity E1 of each triangular facet on the model surface is obtained through iterative updates.

[0070] Step S5: Based on the actual luminous intensity E1 of each triangular facet of the model surface obtained in step S5, set the reflectivity of the pressure-sensitive coating to 0, and obtain the luminous image under no-reflection conditions, such as... Figure 5 As shown, this image is the image after error correction. At this point, the difference between this image and the true value is as follows: Figure 6 As shown.

[0071] By comparison Figure 4 and Figure 6 The results corrected by this method significantly approach the true value of no reflection, achieving good error correction results for multi-surface reflection. 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, effectively addressing the challenges of multi-surface reflection in various complex models based on pressure-sensitive coating measurements.

[0072] In summary, this method first uses an integrating sphere to measure the reflectivity of the current pressure-sensitive coating at its emitted light wavelength to obtain the three-dimensional mesh data of the model surface. The pressure-sensitive coating is then sprayed onto the model surface, and under experimental conditions, an excitation light source is used to excite the pressure-sensitive coating on the model surface and an image is captured. Next, the Region of Interest (ROI) is extracted from the captured image, and camera parameters are recorded. Subsequently, based on the recorded camera parameters and the reflectivity of the current pressure-sensitive coating at its emitted light wavelength, using the captured image as the ground truth, the luminous intensity of the triangular facets is iteratively adjusted 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 to traditional correction methods based on complex linear equations, this method only needs to ensure the size and reconstruction accuracy of the 3D triangular mesh of the model under test. It imposes no additional restrictions on factors such as the variation in surface curvature or the number of multiple surfaces, and eliminates the need for mapping between 2D images and 3D models, reducing interpolation calculations and significantly lowering computational costs. This method not only overcomes the shortcomings of existing technologies, such as high computational load and numerous model limitations, but also significantly improves the final measurement accuracy through a refined error correction process. Furthermore, the entire correction process is simple, practical, and widely applicable, effectively addressing the challenges of multi-surface reflection in various complex models based on pressure-sensitive coating measurements. This provides strong support for achieving high-precision and high-reliability measurements 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, the one or more programs including instructions for executing the multi-surface reflection error correction method for pressure-sensitive coating measurement as described in Embodiment 1.

[0076] See Figure 7 At the hardware level, this electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 The multi-surface reflection error correction method is described above. Of course, besides software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0077] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0078] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable 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 executable by one or more processors of an electronic device, the one or more programs including instructions for performing a multi-surface reflection error correction method for pressure-sensitive coating measurements as described in Embodiment 1.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0083] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for correcting multi-surface reflection errors in pressure-sensitive coating measurements, characterized in that, Includes the following steps: Obtain the reflectivity of the pressure-sensitive coating at the emitted light wavelength; Acquire triangular mesh data of the multi-surface test area; Acquire a real luminescence image of the multi-surface test area coated with pressure-sensitive paint, and the camera parameters when capturing the real luminescence image; Based on the triangular mesh data and the camera parameters, the luminous intensity of each triangular facet in the region under test is initialized. By differentiable ray tracing, a simulation image of the multi-surface region under test corresponding to the reflectivity of the emitted light wavelength is obtained. With the goal of minimizing the simulation error between the simulation image and the real luminous image, the simulation image is iteratively optimized. Based on the iteratively optimized simulation image, the reflectivity of the pressure-sensitive coating is configured to zero, and a corrected image with zero surface reflection is obtained through differential ray tracing. The calculation process for the simulation error includes the following steps: By employing multi-scale Gaussian filtering and downsampling, the differences between simulated and real luminescent images at different resolutions are captured, and the simulation error is calculated. The simulation error is calculated using the following formula: in, For real luminous images, Indicates the first n In the next iteration, the simulated image was obtained using differentiable ray tracing technology. AvgPool represents the difference image at different scales. 2×2 For an average pooling layer of size 2×2, gf 7×7 A Gaussian filter kernel of size 7×7 is used, and uniform zero-padding is applied to the image boundaries during filtering. For fill value, h and w These represent the number of pixels in the height and width of the original image, respectively. This represents the simulation error.

2. 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: in, For the light intensity information of a pixel, A scene function that includes camera, model, and light source parameters. , These are the camera parameters and the luminous intensity of each triangular facet, respectively. , These are the horizontal and vertical coordinates of the image pixels, respectively.

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

4. 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 image captured by the camera.

5. The method for correcting multi-surface reflection errors in pressure-sensitive coating measurement according to claim 1, characterized in that, By configuring the size of the triangular facets with multiple surfaces, each triangular facet can reflect the luminous intensity of the corresponding area.

6. 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 emitted light wavelength includes the following steps: The reflectivity of pressure-sensitive coatings in the emitted light band was measured using an integrating sphere.

7. An electronic device, characterized in that, include: One or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for performing the multi-surface reflection error correction method for pressure-sensitive coating measurements as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, Includes one or more programs executed by one or more processors of an electronic device, said one or more programs including instructions for performing the multi-surface reflection error correction method for pressure-sensitive coating measurements as described in any one of claims 1-6.

Images