Infrared Model Verification Calculation Method and Device for Ground Targets

Abstract

The present application provides an infrared model verification calculation method and device for ground targets, so as to at least solve the problem that there is currently a lack of accuracy verification between the results of the simulation model and the sample set collected by the real physical acquisition device. The method includes: obtaining a real-shot ground target image and a simulation image on the model seeker under the same attitude and the same distance; extracting feature quantities in the simulation image and the real-shot ground target image; the feature quantities include: energy, maximum gray level, average gray level, aspect ratio, angular second moment, and Hu moment; calculating the similarity degree between the feature quantities of the simulation image and the feature quantities of the real-shot ground target image through a preset calculation method.

Description

Technical Field

[0001] This application belongs to the field of simulation technology. Specifically, it relates to a method and device for infrared model verification and calculation for ground targets. Background Art

[0002] Infrared scene simulation is the signal simulation of the entire link of an infrared guidance system. First, clarify the simulation object, describe the simulation target and the external field environment, and study the physical characteristics, radiation characteristics, and trajectory characteristics of the target; secondly, establish models of the target and the background environment based on the characteristic research, including: geometric model, radiation model, motion model, and atmospheric environment model; on the basis of computer graphics, call the models for scene rendering, including geometric stage rendering such as model and viewpoint transformation, lighting, projection, clipping, and screen projection; establish a machine vision model according to the currently used sensor, set the basic level and dynamic range, perform vertex shading, pixel fragment operation, and depth culling to complete the graphics pipeline drawing and generate a two-dimensional image sequence under this machine vision. The verification of the model is to verify the accuracy problem between the results of the initial conceptual model - intermediate mathematical model - final loop simulation model under the model conversion link and the sample set obtained by the real physical acquisition device, so as to verify whether the correct model has been established, that is, to prove that the model can run with convincing accuracy within its applicable range and ensure the credibility of the simulation. Summary of the Invention

[0003] This application provides a method and device for infrared model verification and calculation for ground targets, so as to at least solve the problem of the lack of accuracy verification between the results of the simulation model and the sample set obtained by the real physical acquisition device.

[0004] According to one aspect of this application, a method for infrared model verification and calculation for ground targets is provided, including:

[0005] Obtain the actual photographed ground target image and the simulation image on the model seeker under the same attitude and the same distance;

[0006] Extract the feature quantities in the simulation image and the actual photographed ground target image; the feature quantities include: energy, maximum gray level, average gray level, aspect ratio, angular second moment, and Hu moment;

[0007] Calculate the similarity degree between the feature quantities of the simulation image and the feature quantities of the actual photographed ground target image through a pre-set calculation method.

[0008] In an embodiment, when the feature quantity is energy, calculating the similarity degree between the feature quantity of the simulation image and the feature quantity of the actual photographed ground target image through a pre-set calculation method includes:

[0009] Accumulatively calculate the relative gray levels of the pixel points in the actual photographed ground target image to obtain the energy;

[0010] In one embodiment, when the feature quantity is the highest gray level, the similarity degree between the feature quantity of the simulated image and the feature quantity of the actual photographed ground target image is calculated by a preset calculation method, including:

[0011] Obtain the gray level value of the point with the highest imaging gray level in the actual photographed ground target image;

[0012] Calculate the difference between the gray level value of the point with the highest gray level and the background gray level of the image, which is the highest gray level.

[0013] In one embodiment, when the feature quantity is the angular second moment, the similarity degree between the feature quantity of the simulated image and the feature quantity of the actual photographed ground target image is calculated by a preset calculation method, including:

[0014] Obtain the gray level co-occurrence matrix P defined by a certain rule, the number of occurrences of the interval i to which the gray level of the previous point of the point pair representing the specified azimuth structure belongs and the interval j to which the gray level of the latter point belongs in the entire image;

[0015] Calculate the angular second moment according to the gray level co-occurrence matrix P, the gray level belonging interval i and the gray level belonging interval j of the latter point.

[0016] According to another aspect of the present application, there is also provided an infrared model verification calculation device for ground targets, including:

[0017] An acquisition unit for acquiring the actual photographed ground target image and the simulated image on the model seeker under the same attitude and the same distance;

[0018] An extraction unit for extracting the feature quantities in the simulated image and the actual photographed ground target image; the feature quantities include: energy, highest gray level, average gray level, aspect ratio, angular second moment and Hu moment;

[0019] A calculation unit for calculating the similarity degree between the feature quantity of the simulated image and the feature quantity of the actual photographed ground target image by a preset calculation method.

[0020] In one embodiment, when the feature quantity is energy, the calculation unit includes:

[0021] A relative gray level accumulation module for accumulating and calculating the relative gray levels of the pixel points in the actual photographed ground target image to obtain the energy;

[0022] In one embodiment, when the feature quantity is the highest gray level, the calculation unit includes:

[0023] A highest gray level value acquisition module for acquiring the gray level value of the point with the highest imaging gray level in the actual photographed ground target image;

[0024] A highest gray level calculation module for calculating the difference between the gray level value of the point with the highest gray level and the background gray level of the image, which is the highest gray level.

[0025] In one embodiment, when the feature quantity is the angular second moment, the calculation unit includes:

[0026] A data acquisition module, configured to acquire a gray-level co-occurrence matrix P defined by a certain rule, the number of times that a pair of points representing a structure in a specified orientation, where the gray level of the previous point belongs to interval i and the gray level of the subsequent point belongs to interval j, appears in the entire image;

[0027] An angular second moment calculation module, configured to calculate the angular second moment according to the gray-level co-occurrence matrix P, the gray-level belonging interval i, and the gray-level belonging interval j of the subsequent point. Description of the Drawings

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0029] Figure 1 It is a flowchart of an infrared model verification calculation method for ground targets provided by this application. Detailed Embodiments

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0031] Based on the problems existing in the background technology, this application provides an infrared model verification calculation method and device for ground targets to at least solve the problem of the current lack of accuracy verification between the results of the simulation model and the sample set collected by the real physical acquisition device.

[0032] An infrared model verification calculation method for ground targets, as Figure 1 shown, includes:

[0033] S101: Acquire a real-shot ground target image and a simulation image on the model seeker under the same attitude and the same distance.

[0034] S102: Extract the feature quantities in the simulation image and the real-shot ground target image. The feature quantities include: energy, maximum gray level, average gray level, aspect ratio, angular second moment, and Hu moment.

[0035] S103: Calculate the similarity degree between the feature quantities of the simulated image and those of the actual photographed ground target image through a preset calculation method.

[0036] In one embodiment, when the feature quantity is energy, calculating the similarity degree between the feature quantities of the simulated image and those of the actual photographed ground target image through a preset calculation method includes:

[0037] Accumulate the relative gray levels of the pixel points in the actual photographed ground target image;

[0038] Calculate the difference between the accumulated relative gray level and the image background gray level, which is the energy.

[0039] In one embodiment, when the feature quantity is the highest gray level, calculating the similarity degree between the feature quantities of the simulated image and those of the actual photographed ground target image through a preset calculation method includes:

[0040] Obtain the gray level value of the point with the highest imaging gray level in the actual photographed ground target image;

[0041] Calculate the difference between the gray level value of the point with the highest gray level and the image background gray level, which is the highest gray level.

[0042] In one embodiment, when the feature quantity is the angular second moment, calculating the similarity degree between the feature quantities of the simulated image and those of the actual photographed ground target image through a preset calculation method includes:

[0043] Obtain the gray-level co-occurrence matrix P defined by a certain rule, the number of times that the pair of points representing the specified azimuth structure, where the first point gray level belongs to interval i and the second point gray level belongs to interval j, appears in the entire image;

[0044] Calculate the angular second moment according to the gray-level co-occurrence matrix P, the gray-level interval i, and the second point gray-level interval j.

[0045] In a specific embodiment, based on the actual photographed ground target data, compare the image of the model on the seeker with the features of the actual photographed image under the same attitude and the same distance. The feature quantities include features such as energy, the highest gray level, average gray level, aspect ratio, angular second moment, and Hu moment for comparison and comprehensive verification. Through the comparison of image features, evaluate the similarity degree between the modeling effect and the actual model.

[0046] Energy: The sum of the relative gray levels of the pixel points in the target area of the original image, where the relative gray level is the difference between the imaging gray level of the pixel point and the image background gray level.

[0047] E = ∑[f T (x, y) - G Bkg

[0048] where f T ​(x, y) refers to the gray level of the pixel points in the target area.

[0049] Highest gray level: The difference between the gray level value of the point with the highest imaging gray level in the target area of the original image and the gray level of the image background.

[0050] G Max = f max (x, y) - G Bkg

[0051] Among them, f max (x, y) refers to the highest gray level of the target area, and G Bkg refers to the background estimation value.

[0052] Average gray level: The difference between the average value of the imaging gray levels of the pixel points in the target area of the original image and the gray level of the image background.

[0053] G Ave = f ave (x, y) - G Bkg

[0054] Among them, f ave (x, y) refers to the average gray level of the target area.

[0055] Second-order moment of angle: Reflects the texture change of the object itself. The calculation formula is:

[0056]

[0057] In the formula, p(i, j) is the element in the gray-level co-occurrence matrix P defined by a certain rule, representing the number of times that the point pair with a specified orientation structure and the gray level of the previous point belonging to the interval i and the gray level of the latter point belonging to the interval j appears in the whole image.

[0058] Hu moment (the sixth component): Seven invariant moments are constructed using the second-order and third-order central moments. They can maintain translation, scaling, and rotation invariance under continuous image conditions and mainly describe the shape of the object. The texture features cannot be well described. The central moment is defined as follows:

[0059]

[0060] Among them, is the centroid of the target, and (p, q) is called the order. The normalized central moment is defined as follows:

[0061] η pq = μ pq / (μ 00 ρ )

[0062] Among them, ρ = (p + q) / 2 + 1. In this project, the 5th, 6th, and 7th components of the Hu moment are adopted and defined as follows:

[0063] M 5 = (η 30 - 3η 12 )(η 30 + η 12 )((η 30 + η 12 ) 2 - 3(η 21 + η 03 ) 2 ) + (3η 21 - η 03 )(η 21 + η 03 (3(η 30 + η 12 )) 2 - (η 21 + η 03 ) 2 )

[0064] M 6 = (η 20 - η 02 )((η 30 + η 12 )) 2 - (η 21 + η 03 ) 2 ) + 4η 11 (η 30 + η 12 )(η 21 + η 03 )

[0065] M 7 = (3η 21 - η 03 )(η 30 + η 12 )((η 30 + η 12 )) 2 - 3(η 21 + η 03 ) 2 - (η 30 - 3η 12 )(η 21 + η 03 (3(η 30 + η 12 )) 2 - (η 21 + η 03 ) 2 )

[0066] Length: It refers to the length in the long axis direction of the object. The target is equivalent to an ellipse according to the area size and flatness information. The long axis of this ellipse is the length of the target. The calculation formula is:

[0067]

[0068] where A is the target area and xyl is the flatness. is the ratio of the short axis to the long axis of the area-equivalent ellipse.

[0069] Width: It refers to the length in the short axis direction of the object. The target is equivalent to an ellipse according to the area size and flatness information. The short axis of this ellipse is the width of the target. The calculation formula is:

[0070]

[0071] In another specific embodiment, based on the above calculation method, a model verification test is carried out, and the verification results are shown in Table 1.

[0072] Table 1

[0073] Feature Name Measured Value Simulated Value Similarity Energy 16327552 19720801 0.827935539 Average Gray Scale 3794 4141 0.916203816 Maximum Gray Scale 12470 13154 0.948000608 Six Components of Hu Moments 29 33 0.878787879 Wavelet High-Frequency Coefficients 31332 32062 0.977231614 Second-Order Moment of Histogram 1188 1233 0.96350365 Second-Order Angular Moment 121722 131141 0.928176543 Area 33526 35031 0.957038052 Perimeter 1197 1241 0.964544722 Aspect Ratio 3.02 3.13 0.96485623

[0074] Comprehensive similarity: Meets the modeling requirements.

[0075] Based on the same inventive concept, an infrared model verification calculation device for ground targets is further provided in an embodiment of the present application, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Since the principle of solving problems by this infrared model verification calculation device for ground targets is similar to that of the infrared model verification calculation method for ground targets, the implementation of the infrared model verification calculation device for ground targets can refer to the implementation of the infrared model verification calculation method for ground targets, and the repeated parts will not be described again. Hereinafter, the term "unit" or "module" may be a combination of software and / or hardware that can implement a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0076] According to another aspect of the present application, an infrared model verification calculation device for ground targets is further provided, including:

[0077] An acquisition unit, configured to acquire a real-shot ground target image and a simulation image on the model seeker in the case of the same attitude and the same distance;

[0078] An extraction unit, configured to extract feature quantities from the simulation image and the real-shot ground target image; the feature quantities include: energy, maximum gray level, average gray level, aspect ratio, angular second moment, and Hu moment;

[0079] A calculation unit is configured to calculate the similarity degree between the feature quantity of the simulated image and the feature quantity of the real-shot ground target image through a preset calculation method.

[0080] In one embodiment, when the feature quantity is energy, the calculation unit includes:

[0081] A relative gray-scale accumulation module is configured to accumulate the relative gray-scale of the pixel points in the real-shot ground target image;

[0082] An energy calculation module is configured to calculate the difference between the accumulated relative gray-scale and the image background gray-scale as the energy.

[0083] In one embodiment, when the feature quantity is the highest gray-scale, the calculation unit includes:

[0084] A highest gray-scale value acquisition module is configured to acquire the gray-scale value of the point with the highest imaging gray-scale in the real-shot ground target image;

[0085] A highest gray-scale calculation module is configured to calculate the difference between the gray-scale value of the point with the highest gray-scale and the image background gray-scale as the highest gray-scale.

[0086] In one embodiment, when the feature quantity is the angular second moment, the calculation unit includes:

[0087] A data acquisition module is configured to acquire the gray-level co-occurrence matrix P defined by a certain rule, the number of occurrences of the interval i to which the gray-scale of the previous point of the point pair representing the specified azimuth structure belongs, and the number of occurrences of the interval j to which the gray-scale of the subsequent point belongs in the entire image;

[0088] An angular second moment calculation module is configured to calculate the angular second moment according to the gray-level co-occurrence matrix P, the gray-scale interval i, and the gray-scale interval j of the subsequent point.

[0089] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0091] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0093] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

[0094] Embodiments of the present application also provide a specific implementation manner of an electronic device that can implement all steps in the methods in the above embodiments. The electronic device specifically includes the following:

[0095] A processor, a memory, a communication interface, and a bus;

[0096] Wherein, the processor, the memory, and the communication interface complete communication with each other through the bus;

[0097] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all steps of the method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0098] S101: Obtain a real-shot ground target image and a simulation image on the model seeker under the same attitude and the same distance.

[0099] S102: Extract the feature quantities in the simulation image and the real-shot ground target image. The feature quantities include: energy, maximum gray level, average gray level, aspect ratio, angular second moment, and Hu moment.

[0100] S103: Calculate the similarity degree between the feature quantities of the simulation image and the feature quantities of the real-shot ground target image through a preset calculation method.

[0101] An embodiment of the present application further provides a computer-readable storage medium capable of implementing all steps of the method in the above embodiments. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all steps of the method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0102] S101: Obtain a real-shot ground target image and a simulation image on the model seeker under the same attitude and the same distance.

[0103] S102: Extract the feature quantities in the simulation image and the real-shot ground target image. The feature quantities include: energy, maximum gray level, average gray level, aspect ratio, angular second moment, and Hu moment.

[0104] S103: Calculate the similarity degree between the feature quantities of the simulation image and the feature quantities of the real-shot ground target image through a preset calculation method.

[0105] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the embodiments of "hardware + program", since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments. Although the method operation steps described in the embodiments of this specification are provided as in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way among many execution orders of the steps and does not represent the only execution order. When actually executed by a device or terminal product, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in an environment of parallel processors or multi-threaded processing, or even in a distributed data processing environment). The term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, product or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, product or device. Without further limitation, there are no additional identical or equivalent elements excluded in the process, method, product or device including the said elements. For the convenience of description, the above device is described by dividing it into various modules according to functions. Of course, when implementing the embodiments of this specification, the functions of each module can be realized in the same or multiple software and / or hardware, or the modules realizing the same function can be realized by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms. The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general computer, a special computer, an embedded processor or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in one or more flows or blocks and / or one or more blocks in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks

[0106] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the description of the method embodiment. In the description of this specification, the description of reference terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of this specification.

[0107] In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples. The above is only the embodiments of the embodiments of this specification and is not used to limit the embodiments of this specification. For those skilled in the art, various changes and modifications can be made to the embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of this specification shall be included within the scope of the claims of the embodiments of this specification.

Claims

1. An infrared model verification calculation method for ground targets, characterized in that, it includes: Obtain the actual photographed ground target image and the simulation image on the model seeker under the same attitude and the same distance; Extract the feature quantities in the simulation image and the actual photographed ground target image; The feature quantities include: energy, highest gray level, average gray level, aspect ratio, angular second moment, and Hu moment; Calculate the similarity degree between the feature quantity of the simulation image and the feature quantity of the actual photographed ground target image through a preset calculation method; When the feature quantity is energy, the calculating the similarity degree between the feature quantity of the simulation image and the feature quantity of the actual photographed ground target image through a preset calculation method includes: Accumulatively calculate the relative gray levels of the pixel points in the actual photographed ground target image to obtain the energy; When the feature quantity is the highest gray level, the calculating the similarity degree between the feature quantity of the simulation image and the feature quantity of the actual photographed ground target image through a preset calculation method includes: Obtain the gray level value of the point with the highest imaging gray level in the actual photographed ground target image; Calculate the difference between the gray level value of the point with the highest gray level and the background gray level of the image as the highest gray level; The average gray level represents the difference between the average imaging gray level of the pixel points in the target area of the original image and the background gray level of the image; Compare and comprehensively verify the feature quantities including energy, highest gray level, average gray level, aspect ratio, angular second moment, and Hu moment, and evaluate the similarity degree between the modeling effect and the actual model through image feature comparison.

2. The infrared model verification calculation method for ground targets according to claim 1, characterized in that, When the feature quantity is the angular second moment, the calculating the similarity degree between the feature quantity of the simulation image and the feature quantity of the actual photographed ground target image through a preset calculation method includes: Obtain the gray-level co-occurrence matrix P defined by a certain rule, the number of times that the first point gray level belonging interval i and the second point gray level belonging interval j representing the specified azimuth structure appear in the whole image; Calculate the angular second moment according to the gray-level co-occurrence matrix P, the gray level belonging interval i, and the second point gray level belonging interval j.

3. An infrared model verification calculation device for ground targets, characterized in that, it includes: An acquisition unit for acquiring the actual photographed ground target image and the simulation image on the model seeker under the same attitude and the same distance; An extraction unit for extracting the feature quantities in the simulation image and the actual photographed ground target image; The feature quantities include: energy, highest gray level, average gray level, aspect ratio, angular second moment, and Hu moment; A calculation unit for calculating the similarity degree between the feature quantity of the simulation image and the feature quantity of the actual photographed ground target image through a preset calculation method; When the feature quantity is energy, the calculation unit includes: A relative gray level accumulation module for accumulatively calculating the relative gray levels of the pixel points in the actual photographed ground target image to obtain the energy; When the feature quantity is the highest gray level, the calculation unit includes: A highest gray level value acquisition module for acquiring the gray level value of the point with the highest imaging gray level in the actual photographed ground target image; The maximum gray level calculation module is used to calculate the difference between the gray level of the point with the highest gray level and the gray level of the image background, which is the maximum gray level; The average gray level represents the difference between the average imaging gray level of the pixel points in the target area of the original image and the gray level of the image background; The feature quantities include energy, maximum gray level, average gray level, aspect ratio, angular second moment, and Hu moment features for comparison and comprehensive verification. Through image feature comparison, the similarity between the modeling effect and the actual model is evaluated.

4. The infrared model verification calculation device for ground targets according to claim 3, characterized in that, when the feature quantity is the angular second moment, the calculation unit includes: The data acquisition module is used to acquire the gray level co-occurrence matrix P defined by a certain rule, the interval i to which the gray level of the previous point of the point pair representing the specified azimuth structure belongs, and the number of times the interval j to which the gray level of the subsequent point belongs appears in the entire image; The angular second moment calculation module is used to calculate the angular second moment according to the gray level co-occurrence matrix P, the gray level interval i, and the gray level interval j of the subsequent point.

5. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, the steps of the infrared model verification calculation method for ground targets according to any one of claims 1 to 2 are implemented.

6. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the steps of the infrared model verification calculation method for ground targets according to any one of claims 1 to 2 are implemented.

Images