Vehicle reflective imaging omni-directional checking method, device and equipment based on dynamic optical check matrix and storage medium

Abstract

The invention discloses a vehicle reflective imaging omni-directional checking method, device and equipment based on a dynamic optical checking matrix and a storage medium, and relates to the technical field of vehicle optical design and checking, and the method comprises the steps: obtaining optical attribute information and reflection surface attribute information; constructing an optical check matrix based on the optical attribute information and the reflecting surface attribute information to generate check cells, simulating a simulation target scene, and determining a target check parameter set and an imaging index set; and checking a target imaging cell based on the imaging index set and the target checking parameter set, tracking a target light reflection path, marking that the number of target light intersection points is abnormal, generating a checking report, and completing vehicle reflective imaging omnibearing checking based on the checking report. According to the method, the dynamic optical check matrix is constructed, the check cells are generated, the abnormal light intersection points are recognized, the check report is generated, light source combinations are comprehensively covered, dynamic environment simulation is supported, the misjudgment rate is reduced, automatic check is achieved, and the efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle optical design and calibration technology, and in particular to a method, device, equipment and storage medium for all-round calibration of vehicle reflective imaging based on a dynamic optical calibration matrix. Background Art

[0002] As vehicles become increasingly intelligent and comfortable, their interior and exterior optical systems are becoming increasingly complex. Vehicle reflective imaging not only impacts driving safety but also the visual comfort of drivers and passengers. For example, onboard light sources such as instrument screens, head-up display (HUD) projections, and headlights, as well as highly reflective interior materials, can produce glare or reflections under varying lighting conditions, disrupting the driver's vision and increasing driving risks. Therefore, comprehensive and accurate calibration of vehicle reflective imaging is essential.

[0003] Currently, the existing practice is to use static models and single-environment simulation methods to calibrate vehicle reflective imaging. For example, by collecting vehicle data, building 3D human models, and simulating driving environments, the glare risk is judged. This relies on static models. However, the existing approach has limited coverage and does not include the verification of dynamic combinations of multiple light sources and indirect light sources. It is easy to miss complex light source combinations and is not adaptable to dynamic environmental changes such as day and night, rain and fog. Moreover, the judgment criteria are relatively simple and do not consider the differences in the dynamic visual threshold of the human eye, resulting in a high misjudgment rate. Therefore, how to more comprehensively cover complex light source combinations, adapt to dynamic environmental changes, and combine the visual characteristics of the human eye to conduct a comprehensive verification of vehicle reflective imaging has become an urgent problem to be solved.

[0004] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a method, device, equipment and storage medium for all-round calibration of vehicle reflective imaging based on a dynamic optical calibration matrix, aiming to solve the technical problem of how to more comprehensively cover complex light source combinations, adapt to dynamic environmental changes and combine the visual characteristics of the human eye to perform all-round calibration of vehicle reflective imaging.

[0006] To achieve the above objectives, the present application proposes a method for omnidirectional verification of vehicle reflective imaging based on a dynamic optical verification matrix, the method comprising:

[0007] Obtaining optical property information and reflective surface property information;

[0008] Constructing an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, and simulating a target scene to determine a target calibration parameter set and an imaging index set;

[0009] The target imaging cells are calibrated based on the imaging index set and the target calibration parameter set, and the target light reflection path is tracked to mark the abnormal number of target light intersections, and a calibration report is generated. Based on the calibration report, a full-scale calibration of the vehicle reflective imaging is completed.

[0010] In one embodiment, the step of obtaining optical property information and reflective surface property information includes:

[0011] Obtain light source information and reflecting surface information;

[0012] Classifying the light source information according to a light source classification system to determine optical property information, wherein the optical property information includes direct light source property information and indirect light source property information;

[0013] Based on the reflecting surface information, imaging attributes are identified and reflection characteristics are quantified to determine reflecting surface attribute information.

[0014] In one embodiment, the steps of constructing an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, simulating a target scene, and determining a target calibration parameter set and an imaging index set include:

[0015] Constructing an optical calibration matrix based on the optical property information and the reflective surface property information, and determining a calibration cell set;

[0016] Matching corresponding calibration parameters based on the calibration cell set, and inputting the scenario working condition calibration model for training to determine the target calibration parameter set;

[0017] Performing scene simulation based on the verification cell set, loading environmental variables to match the corresponding simulation target scene, and determining scene parameter information, wherein the scene parameter information includes scene background brightness information and scene reflective surface information;

[0018] An imaging index set is calculated based on the scene parameter information, where the imaging index set includes spot contrast and imaging displacement.

[0019] In one embodiment, the step of matching corresponding calibration parameters based on the calibration cell set and inputting a scenario condition calibration model for training to determine a target calibration parameter set includes:

[0020] Obtain light intensity gradient, weather influencing factors, age difference information, and gender difference information;

[0021] determining a target limit value set based on matching corresponding calibration parameters of the calibration cell set;

[0022] Based on the light illuminance gradient, the weather influencing factor and the target limit value set, an environmental dynamic parameter library is input for training, and the corresponding target comparison value is adjusted to determine an environmental calibration parameter set;

[0023] A visual recognition model is input for training based on the age difference information, the gender difference information and the environmental calibration parameter set, and the corresponding target contrast value is adjusted to obtain a target calibration parameter set.

[0024] In one embodiment, the step of calibrating the target imaging cell based on the imaging indicator set and the target calibration parameter set, tracking the target light reflection path and marking the abnormal number of target light intersections, and generating a calibration report includes:

[0025] Get the sensitive area of vision;

[0026] Calibrate the target imaging cell based on the imaging index set and the target calibration parameter set, mark the cell color, and determine the calibration cell result and imaging position information;

[0027] Based on the verification cell results, the imaging position information and the field of view sensitive area, abnormal cases are identified, and the target light reflection path is tracked to mark the abnormal number of target light intersections, and a verification report is generated.

[0028] In one embodiment, the step of identifying abnormal cases based on the calibration cell results, the imaging position information, and the field of view sensitive area, and tracking the target light reflection path to mark the abnormal number of target light intersections, and generating a calibration report includes:

[0029] generating a feedback report based on the verification cell result, detecting imaging quality according to the imaging position information and the field of view sensitive area, and determining a detection result, wherein the feedback report includes a material replacement plan or a structural modification plan;

[0030] Identify abnormal cases based on the detection results, track the target light reflection path, mark the abnormal number of target light intersections, and generate optimization suggestions and intersection abnormality reports;

[0031] A verification report is generated based on the verification cell results, the feedback report, the optimization suggestions, and the intersection anomaly report, wherein the verification report includes a visualization chart and an optimization suggestion table.

[0032] In one embodiment, the steps of identifying abnormal cases based on the detection results, tracking the target light reflection path, marking abnormal number of target light intersections, and generating optimization suggestions and intersection abnormality reports include:

[0033] Identify abnormal cases based on the detection results and determine abnormal case information;

[0034] When the abnormal case information indicates that there is no abnormal case, identifying the target imaging position deviation and generating an optimization suggestion;

[0035] When the abnormal case information indicates that an abnormal case exists, light energy information is obtained, the reflection path of the target light is tracked based on the light energy information, the number of intersections is determined, the number of target light intersections is marked as abnormal based on the number of intersections, and an intersection abnormality report is generated.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle reflective imaging omnidirectional verification device based on a dynamic optical verification matrix, the vehicle reflective imaging omnidirectional verification device based on a dynamic optical verification matrix comprising:

[0037] An acquisition module, used to acquire optical property information and reflective surface property information;

[0038] A processing module, configured to construct an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, and simulate a target scene to determine a target calibration parameter set and an imaging index set;

[0039] An execution module is used to calibrate the target imaging cells based on the imaging index set and the target calibration parameter set, track the target light reflection path, mark the abnormal number of target light intersections, generate a calibration report, and complete the full-scale calibration of vehicle reflective imaging based on the calibration report.

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes a comprehensive vehicle reflection imaging calibration device based on a dynamic optical calibration matrix, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the comprehensive vehicle reflection imaging calibration method based on a dynamic optical calibration matrix as described above.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the all-round calibration method of vehicle reflection imaging based on the dynamic optical calibration matrix as described above are implemented.

[0042] One or more technical solutions proposed in this application have at least the following technical effects:

[0043] This embodiment proposes a method for all-round calibration of vehicle reflective imaging based on a dynamic optical calibration matrix, which obtains optical property information and reflective surface property information; constructs an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, simulates a target scene, and determines a target calibration parameter set and an imaging index set; calibrates the target imaging cells based on the imaging index set and the target calibration parameter set, tracks the target light reflection path, marks any abnormalities in the number of target light intersections, generates a calibration report, and completes all-round calibration of vehicle reflective imaging based on the calibration report. This application obtains optical property information and reflective surface property information, constructs a dynamic optical calibration matrix and generates calibration cells, thereby simulating the target scene, determining the target calibration parameter set and imaging index set, and calibrating the target imaging cells, tracking the target light reflection path and marking anomalies, generating a calibration report, and realizing all-round calibration of vehicle reflective imaging, comprehensively covering the complex combination of direct and indirect light sources, avoiding calibration omissions, and supporting real-time simulation of dynamic environmental changes such as day and night, rain and foggy weather, significantly improving the calibration accuracy. In addition, combined with the dynamic visual threshold of the human eye, considering factors such as age and gender, the misjudgment rate is significantly reduced. At the same time, the automated process greatly shortens the design cycle, supports rapid iteration, and significantly improves development efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 A flowchart illustrating a first embodiment of a method for omnidirectional verification of vehicle reflective imaging based on a dynamic optical verification matrix is provided in this application;

[0047] Figure 2 A flowchart illustrating a second embodiment of a method for omnidirectional calibration of vehicle reflective imaging based on a dynamic optical calibration matrix is provided in this application;

[0048] Figure 3 This is a schematic diagram of the module structure of a omnidirectional vehicle reflective imaging calibration device based on a dynamic optical calibration matrix according to an embodiment of the present application;

[0049] Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the all-round vehicle reflection imaging calibration method based on the dynamic optical calibration matrix in the embodiment of the present application.

[0050] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0052] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0053] The main solution of the embodiment of the present application is: obtaining optical property information and reflective surface property information; constructing an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, and simulating the target scene to determine the target calibration parameter set and the imaging index set; calibrating the target imaging cells based on the imaging index set and the target calibration parameter set, and tracking the target light reflection path to mark the abnormal number of target light intersections, generating a calibration report, and completing a full-scale calibration of the vehicle reflective imaging based on the calibration report.

[0054] In this embodiment, for ease of description, the following description is made by taking the identification of a vehicle reflective imaging omnidirectional calibration device based on a dynamic optical calibration matrix as the execution subject.

[0055] Due to the limited coverage of existing technology verification, it does not include the verification of dynamic combinations of multiple light sources and indirect light sources, and is prone to missing complex light source combinations. It also lacks adaptability to the environment and cannot adapt to dynamic environmental changes such as day and night alternation, rainy and foggy weather, etc. Moreover, the judgment criteria are relatively simple and do not take into account the differences in the dynamic visual thresholds of the human eye, resulting in a high misjudgment rate.

[0056] The present application provides a solution, which obtains optical property information and reflective surface property information; constructs an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, simulates a target scene, and determines a target calibration parameter set and an imaging index set; calibrates the target imaging cells based on the imaging index set and the target calibration parameter set, and tracks the target light reflection path to mark the number of target light intersections that are abnormal, generates a calibration report, and completes a full-scale calibration of the vehicle reflective imaging based on the calibration report.

[0057] It can be seen from the above embodiments that the present application obtains optical property information and reflective surface property information, constructs a dynamic optical calibration matrix and generates calibration cells, thereby simulating the target scene, determining the target calibration parameter set and imaging index set, and calibrating the target imaging cells, tracking the target light reflection path and marking anomalies, generating a calibration report, and realizing all-round calibration of vehicle reflective imaging, comprehensively covering the complex combination of direct and indirect light sources, avoiding calibration omissions, and supporting real-time simulation of dynamic environmental changes such as day and night alternation, rainy and foggy weather, significantly improving the calibration accuracy, and, combined with the dynamic visual threshold of the human eye, considering factors such as age and gender, significantly reducing the misjudgment rate, and at the same time, the automated process greatly shortens the design cycle, supports rapid iteration, and significantly improves development efficiency.

[0058] Based on this, the embodiment of the present application provides a vehicle reflection imaging omnidirectional verification method based on a dynamic optical verification matrix, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle reflective imaging omnidirectional calibration method based on the dynamic optical calibration matrix of this application.

[0059] In this embodiment, the vehicle reflection imaging omnidirectional verification method based on the dynamic optical verification matrix includes steps S10 to S30:

[0060] Step S10, obtaining optical property information and reflective surface property information;

[0061] It should be noted that the optical property information is the quantitative parameters of the characteristics of various light sources collected by the vehicle optical system, and the reflective surface property information is the quantitative parameters of the physical characteristics of the internal and external reflective surfaces of the vehicle.

[0062] It can be understood that the optical property information may include direct light source property information and indirect light source property information, wherein the direct light source property information is composed of natural light sources and vehicle-mounted light sources, wherein the natural light source is a naturally occurring light source, such as sunlight and moonlight, which may include azimuth and altitude parameters, and the vehicle-mounted light source is a light source generated by vehicle equipment, such as instrument screens, HUD projections, headlights and ambient lights, which may include spectral characteristics, luminous intensity and spatial distribution parameters, and the indirect light source property information characterizes the reflection characteristics of highly reflective materials and secondary reflection surfaces, wherein highly reflective materials are materials with higher reflection characteristics, such as chrome trim and light-colored interiors whose reflectivity exceeds a preset threshold.

[0063] In addition, it should be noted that the reflective surface attribute information may include the type of reflective surface, surface roughness, transmittance and coating properties, such as the transmittance of the front windshield is 70% to 90%, the surface roughness is less than or equal to 0.1μm, the haze value of the anti-glare coating of the vehicle screen is 15% to 25%, and the anodized glossiness of the bright trim is greater than or equal to 90GU.

[0064] For ease of understanding, the following description is made by taking obtaining optical property information and reflective surface property information as an example, wherein the information acquisition device is an information acquisition module, and the storage device is a memory.

[0065] The information acquisition module obtains light source information and reflective surface information, that is, reads light source parameters, such as IES light distribution data, reads reflective surface properties, such as BRDF data, classifies the light source information according to the light source classification system, and determines optical property information. The optical property information includes direct light source property information and indirect light source property information. That is, the light source classification system is used to divide the light source information into direct light source and indirect light source to obtain optical property information. Based on the reflective surface information, the imaging property quantitative reflection characteristics are identified to determine the reflective surface property information. That is, the reflective surface information is used to identify the imaging property quantitative reflection characteristics corresponding to each material to obtain the reflective surface property information. Subsequent processing is performed based on the optical property information and the reflective surface property information.

[0066] In a feasible implementation, step S10 may include steps A11 to A13:

[0067] Step A11, obtaining light source information and reflecting surface information;

[0068] It should be noted that the light source information is a data set collected to characterize detailed information of various light sources, and the reflecting surface information is a data set collected to characterize detailed information of various reflecting surfaces.

[0069] It is understandable that the dynamic expansion mechanism can be used to add new light sources or new reflective surfaces. For new light sources, their spectral characteristics, luminous intensity and spatial distribution parameters can be defined, such as defining the wavelength range as 380-780nm, defining the luminous intensity to comply with the GB25991-2010 standard, and defining the light distribution curve. For new reflective surfaces, calibration cells are generated by automatically associating relevant light sources. The light source information and reflective surface information can be used to truly simulate the reflective imaging situation under actual lighting conditions, thereby achieving accurate calibration.

[0070] Step A12: classify the light source information according to a light source classification system to determine optical property information, where the optical property information includes direct light source property information and indirect light source property information;

[0071] It should be noted that the direct light source attribute information is quantitative characteristic information of a light source that directly emits light, and the indirect light source attribute information is quantitative characteristic information of a light source that indirectly generates light by reflection or scattering.

[0072] It is understandable that the light source classification system divides light sources into two categories: direct light sources and indirect light sources. By combining the direct light source attribute information and the indirect light source attribute information, they jointly affect the effect of reflective imaging, so as to comprehensively cover the light source conditions inside and outside the vehicle, avoid omissions in verification, and achieve complete verification of the vehicle's reflective imaging.

[0073] Step A13: Identify imaging attributes and quantify reflection characteristics based on the reflection surface information to determine reflection surface attribute information.

[0074] It can be understood that imaging properties are the image characteristics formed by the reflective surface in the optical system, which may include imaging position, imaging clarity and imaging size, and characterize the image quality formed at a specific observation point after the light passes through the reflective surface. The reflection characteristics are the optical behavior when the reflective surface interacts with the light, and may include reflectivity, reflection directionality and surface roughness, and characterize the reflection intensity and directional distribution of the light on the reflective surface, which directly affects the quality and effect of the imaging.

[0075] Step S20, constructing an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, and simulating a target scene to determine a target calibration parameter set and an imaging index set;

[0076] It should be noted that the target calibration parameter set is a set of calibration parameters determined for each combination of light source and reflective surface in the optical calibration matrix, and the imaging index set is a series of quantitative indicators for evaluating imaging quality obtained by calculation in a simulated target scene.

[0077] It can be understood that the target calibration parameter set is obtained through scene condition calibration model training based on optical property information and reflective surface property information, and is used to evaluate the quality of reflective imaging. It may include reflective brightness, imaging position, contrast threshold, environmental adaptability parameters and human eye visual characteristic parameters. The imaging index set characterizes the performance of reflective imaging in a specific scene and is used for precise calibration. It may include spot contrast and imaging displacement.

[0078] For ease of understanding, the following description is given by taking the determination of a target calibration parameter set and an imaging index set as an example, wherein the information acquisition device is an information acquisition module, the storage device is a memory, and the processing device is a processing module.

[0079] The information acquisition module obtains optical property information and reflective surface property information, constructs an optical calibration matrix based on the optical property information and the reflective surface property information, determines a calibration cell set, that is, uses the optical property information as the horizontal axis X-axis and the reflective surface information as the vertical axis Y-axis to construct an optical calibration matrix, and uses a dynamic expansion mechanism to add a new light source or a new reflective surface, defines the light source and automatically associates the relevant light sources to generate corresponding calibration cells, wherein the calibration cell can record the calibration parameters of the "light source-reflective surface" combination, such as reflective brightness and imaging position, thereby obtaining a calibration cell set, obtains light brightness gradient, weather influence factor, age difference information and gender difference information, matches the corresponding calibration parameters based on the calibration cell set, and determines a target limit value set, that is, pre-sets an environmental dynamic parameter library and a human eye visual dynamic model, uses the generated calibration cells to match the corresponding calibration parameters, and obtains a target limit value set, inputs the environmental dynamic parameter library based on the light brightness gradient, the weather influence factor and the target limit value set for training, and adjusts the corresponding target contrast value to determine the environmental calibration parameter set, that is, uses the environmental dynamic parameter library for training, and defines different The illumination range and contrast threshold of the lighting scene, as well as the contrast threshold adjustment factor under special weather conditions, are used to obtain the environmental calibration parameter set. For example, the illumination of the clear sky scene at noon is 100,000 lux, the contrast threshold is 0.5, the illumination of the dusk scene is 1,000 lux, the contrast threshold is 0.6, the illumination of the urban road scene at night is 50 lux, the contrast threshold is 0.8, the contrast threshold is increased by 20% in heavy rain scenes, and by 30% in haze scenes. In order to adapt to the impact of different environments on imaging quality, the target limit value set is dynamically adjusted to obtain the one suitable for the current scene. The environmental calibration parameter set is input into a visual recognition model for training based on the age difference information, the gender difference information, and the environmental calibration parameter set, and the corresponding target contrast value is adjusted to obtain a target calibration parameter set. This is training using a human visual dynamic model, comprehensively considering the differentiated characteristics of age and gender to more accurately simulate the visual perception of reflective imaging by different populations. For example, in terms of age differentiation, the model divides the population into two age groups: 18-25 and 45-65, corresponding to spatial frequencies of 6 cpd and 3 cpd, respectively, and contrast sensitivity thresholds of 1.0 and 1.8. It can characterize the decline in visual contrast sensitivity with age. In terms of gender differentiation, the male-female ratio in the passenger car test group is 50:50, and the average pupil distance is 62mm for females and 67mm for males. In the commercial vehicle test group, males account for 90%, and the seat height increases by 5%, which complies with the SAEJ1517 standard. By adjusting the environmental calibration parameter set to adapt to the visual characteristics of different genders and vehicle usage scenarios, a target calibration parameter set suitable for the current scenario is obtained. Based on the calibration cell set, a scenario simulation is performed, and the environmental variables are loaded to match the corresponding simulation target scenario to determine the scenario. Parameter information, including scene background brightness information and scene reflective surface information, was used to simulate a specific vehicle reflective imaging scenario using SPEOS software. For example, a vehicle reflective imaging scenario was configured with the sun as the light source, an azimuth angle of 120°, an elevation angle of 45°, a windshield as the reflective surface, an AGC material, and an environmental effect setting of 0.8 rain intensity. The scene parameter information for this scenario was obtained and an imaging index set was calculated based on this scene parameter information. This imaging index set included spot contrast and imaging displacement, i.e., key index calculations were performed. The calculated spot contrast was expressed as:

[0080]

[0081] Among them, L max is the maximum background brightness, L bg is the background brightness, and L bg In night mode, it should be less than 0.1cd / m 2 .

[0082] The calculated imaging displacement is expressed as:

[0083]

[0084] Where h is the thickness of the reflecting surface and θ is the incident angle.

[0085] Subsequent processing is performed based on the target calibration parameter set and the imaging index set.

[0086] In a feasible implementation, step S20 may include steps B11 to B14:

[0087] Step B11, constructing an optical calibration matrix based on the optical property information and the reflective surface property information, and determining a calibration cell set;

[0088] It should be noted that the calibration cell set is a cell set formed according to all combinations of light sources and reflective surfaces in the constructed optical calibration matrix.

[0089] It can be understood that each cell in the calibration cell set records the key parameters after the interaction between a specific light source and a specific reflective surface, such as reflection brightness, imaging position and contrast, which are used to simulate and calibrate the effect of vehicle reflective imaging under different lighting conditions.

[0090] Step B12, matching corresponding calibration parameters based on the calibration cell set, and inputting the scenario working condition calibration model for training to determine the target calibration parameter set;

[0091] It should be noted that the calibration parameters are quantitative indicators to be trained for evaluating the quality of reflective imaging during the optical calibration process, and the scene condition calibration model is a calibration model used to simulate and evaluate the reflective imaging quality of a vehicle under different working conditions.

[0092] It is understandable that the use of scenario working condition verification models can simulate lighting scenes under different environmental conditions, support real-time simulation of dynamic environmental changes such as day and night alternation, rain and fog weather, significantly improve the adaptability and accuracy of verification, and shorten the design cycle through automated simulation and calculation, support rapid iteration, and significantly improve development efficiency.

[0093] In a feasible implementation, step B12 may include steps C11 to C14:

[0094] Step C11, obtaining light brightness gradient, weather influencing factors, age difference information, and gender difference information;

[0095] It should be noted that the light brightness gradient is the range of light brightness variation and its corresponding contrast threshold under different lighting conditions, the weather impact factor is the adjustment coefficient of the imaging contrast threshold under special weather conditions, the age difference information is the difference contrast value in visual perception of people of different age groups, and the gender difference information is the difference contrast value in visual perception of different genders.

[0096] It can be understood that the light brightness gradient can characterize the changes in light from strong light to weak light environment, such as the illumination range of a clear sky at noon is 100,000 lux, the contrast threshold is 0.5, the illumination range at dusk is 1,000 lux, the contrast threshold is 0.6, the illumination range of a city road at night is 50 lux, and the contrast threshold is 0.8, which is used to simulate the actual imaging environment under different lighting conditions. The weather impact factor is used to simulate the impact of different weather conditions on imaging quality. For example, in a heavy rain scene, the contrast threshold is increased by 20%; in a haze scene, the contrast threshold is increased by 3 0%. The age difference information represents the decrease in visual contrast sensitivity with age. For example, the spatial frequency of the 18-25 age group is 6 cpd, and the contrast sensitivity threshold is 1.0. The spatial frequency of the 45-65 age group is 3 cpd, and the contrast sensitivity threshold is 1.8. The gender difference information represents the impact of gender differences on visual perception. For example, in the passenger car test group, the male-female ratio is 50:50, and the average pupil distance is 62 mm for females and 67 mm for males, respectively. In the commercial vehicle test group, males account for 90%, and the seat height increases by 5%, which meets the SAE J1517 standard.

[0097] Step C12, determining a target limit value set based on matching the calibration cell set with corresponding calibration parameters;

[0098] It should be noted that the target limit value set is a set of limit parameters determined according to the corresponding environment pointed to by the current cell during the optical verification process.

[0099] It is understandable that the target limit value set is used to define the minimum standards that the reflective imaging quality needs to meet under specific environmental conditions, and may include a contrast threshold, a maximum allowable value of the imaging displacement, and upper and lower limits of the reflective brightness to ensure that the imaging quality meets the standards.

[0100] Step C13: inputting the environmental dynamic parameter library for training based on the light illuminance gradient, the weather influencing factor, and the target limit value set, and adjusting the corresponding target comparison value to determine the environmental verification parameter set;

[0101] It should be noted that the environmental calibration parameter set is a set of calibration parameters trained under various environmental conditions using an environmental dynamic parameter library.

[0102] It can be understood that the environmental calibration parameter set can characterize the calibration standards of reflective imaging in different environments. Using the environmental calibration parameter set for verification can ensure that the reflective imaging meets the required environmental standards in the current environment, accurately define the calibration standards for imaging quality, significantly improve the accuracy of calibration, and dynamically adjust the calibration standards according to different environmental conditions, supporting real-time simulation of dynamic environmental changes such as day and night alternation, rainy and foggy weather, etc.

[0103] Step C14: inputting a visual recognition model for training based on the age difference information, the gender difference information, and the environmental calibration parameter set, and adjusting the corresponding target contrast value to obtain a target calibration parameter set.

[0104] It is understandable that the target calibration parameter set takes into account the dynamic parameters of the environment and the visual characteristics of the human eye. There are great differences in the imaging quality under different lighting conditions and the imaging positions recognized by different human eyes. Therefore, in order to perform imaging calibration more accurately, it is necessary to combine environmental dynamic parameters such as light intensity gradient and weather influencing factors with visual characteristic parameters such as age and gender differences to conduct real-time evaluation and adjustment of the imaging quality. For example, in a strong light environment, the contrast threshold will be increased accordingly to adapt to the higher background brightness, while at night or in low light conditions, the contrast threshold will be lowered to ensure the visibility of the image. At the same time, in view of the visual differences of different age groups and genders, the judgment criteria need to be adjusted according to the differences in the actual pupil distance of different genders to ensure that the imaging results meet the actual perception needs of the human eye, so as to improve the imaging quality and significantly improve the adaptability and accuracy of the calibration.

[0105] Step B13, performing scene simulation based on the verification cell set, loading environmental variables to match the corresponding simulation target scene, and determining scene parameter information, wherein the scene parameter information includes scene background brightness information and scene reflective surface information;

[0106] It should be noted that the scenario parameter information is a parameter set of a specific scenario identified during the process of simulating a target scenario.

[0107] It can be understood that the scene parameter information can be used to characterize the background brightness and reflective surface characteristics under different environmental conditions, so that the simulated scene is close to the actual use scene, and a set of imaging indicators, such as spot contrast and imaging displacement, can be calculated to adapt to dynamic environmental changes such as day and night, rainy and foggy weather, etc., to ensure the adaptability and accuracy of the calibration results.

[0108] Step B14: Calculate an imaging index set based on the scene parameter information, where the imaging index set includes spot contrast and imaging displacement.

[0109] It should be noted that the spot contrast is the relative difference between the brightness of the imaging spot and the background brightness, and the imaging displacement is the deviation between the actual position and the expected position of the reflected spot on the imaging plane.

[0110] It can be understood that the use of the light spot contrast can ensure that the image can be clearly identified under different background brightness, and the use of the imaging displacement can ensure that the imaging position meets the design requirements and avoid visual interference or misjudgment caused by excessive displacement. The combined use of light spot contrast and imaging displacement can comprehensively evaluate the performance of reflective imaging in specific scenarios, making the imaging clearer and the imaging position more accurate.

[0111] Step S30, calibrating the target imaging cells based on the imaging index set and the target calibration parameter set, tracking the target light reflection path to mark the abnormal number of target light intersections, generating a calibration report, and completing the full-scale calibration of vehicle reflective imaging based on the calibration report.

[0112] It should be noted that the calibration report is a comprehensive evaluation document that records and summarizes the results of the comprehensive calibration of vehicle reflective imaging.

[0113] For ease of understanding, the generation of a verification report is taken as an example for explanation, wherein the information collection device is the information collection module, the storage device is the memory, and the execution device is the execution module.

[0114] The information acquisition module obtains the field of view sensitive area, verifies the target imaging cell based on the imaging index set and the target calibration parameter set, marks the cell color, determines the verification cell result and imaging position information, generates a feedback report based on the verification cell result, and detects the imaging quality according to the imaging position information and the field of view sensitive area to determine the detection result. The feedback report includes a material replacement plan or a structure modification plan, identifies abnormal cases based on the detection results, and tracks the target light reflection path to mark the abnormal number of target light intersections, and generates optimization suggestions and intersection abnormality reports.

[0115] That is, when checking the target imaging cell, automatic checking and judgment are performed.

[0116] If the spot contrast C in the imaging index set is less than the corresponding threshold Cth in the target calibration parameter set corresponding to the target imaging cell and the imaging displacement in the imaging index set is less than the preset threshold, the calibration cell result is passed, and the marked cell color is green.

[0117] If the spot contrast C in the imaging index set is greater than the corresponding threshold Cth in the target calibration parameter set corresponding to the target imaging cell, it means that the bright trim C1 is detected to be excessive in the dusk scene, the calibration cell result is failed, and the marked cell color is red. The material replacement plan is to recommend replacing the material with matte aluminum. After the replacement, GU = 35-50. A structural modification plan is generated, such as C = 0.82 before the modification and C = 0.47 after the modification.

[0118] If it does not fall into the above two situations, the calibration cell result is failed, the marked cell color is red, and the imaging quality is tested. At this time, if the imaging position information is located in the field of view sensitive area, such as the 10° field of view line or the V1 point, the test result is that the imaging quality is good, and an optimization suggestion is generated to adjust the screen tilt angle to 15°±2° and add a sunshade, such as one with a length greater than or equal to 50mm.

[0119] If the detection result shows poor imaging quality, that is, there is an abnormal case, the target light reflection path is traced and the number of target light intersections is marked as abnormal. When the light energy is greater than the preset threshold, the light path is traced and the number of intersections is counted. If the number of intersections is greater than the preset threshold, such as the preset threshold is set to 2, it is marked as a ghost abnormality and output.

[0120] A verification report is generated based on the verification cell results, the feedback report, the optimization suggestions, and the intersection anomaly report, wherein the verification report includes a visualization chart and an optimization suggestion table.

[0121] This embodiment proposes a method for all-round calibration of vehicle reflective imaging based on a dynamic optical calibration matrix, which obtains optical property information and reflective surface property information; constructs an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, simulates a target scene, and determines a target calibration parameter set and an imaging index set; calibrates the target imaging cells based on the imaging index set and the target calibration parameter set, tracks the target light reflection path, marks any abnormalities in the number of target light intersections, generates a calibration report, and completes all-round calibration of vehicle reflective imaging based on the calibration report. It solves the technical problem of how to more comprehensively cover complex light source combinations, adapt to dynamic environmental changes, and combine the visual characteristics of the human eye to conduct all-round calibration of vehicle reflective imaging. Compared with the existing technology, this application generates calibration cells by constructing an optical calibration matrix, and simulates the target scene, determines the target calibration parameter set and imaging index set, thereby calibrating the target imaging cell, tracking the target light reflection path and marking the abnormal number of intersections, generating a calibration report, and realizing a comprehensive calibration of vehicle reflective imaging. By covering complex combinations of direct and indirect light sources, it avoids calibration omissions, and supports real-time simulation of day and night alternation and weather changes, which significantly improves the calibration accuracy and efficiency. In combination with the dynamic visual threshold of the human eye, it scientifically sets the judgment benchmark, significantly reduces the misjudgment rate, and greatly shortens the design cycle through automated processes, supports rapid iteration, and significantly improves development efficiency.

[0122] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated later.

[0123] In this embodiment, refer to Figure 2 , Figure 2This is a flow chart of the second embodiment of the present application for omnidirectional verification of vehicle reflection imaging based on a dynamic optical verification matrix. Step S30 specifically includes steps S31 to S33:

[0124] Step S31, obtaining a visual field sensitive area;

[0125] It should be noted that the visual field sensitive area is a specific area where the driver or passenger's visual perception is most sensitive and has the greatest impact on safe driving during vehicle driving.

[0126] It is understandable that the field of view sensitive area may include the driver's field of view and the area that passengers need to pay attention to. During the vehicle reflective imaging verification process, priority is given to these key areas to ensure that the imaging quality meets safety and comfort requirements.

[0127] For ease of understanding, obtaining a visual field sensitive area is taken as an example for explanation, wherein the information acquisition device is an information acquisition module, the storage device is a memory, and the execution device is an execution module.

[0128] The information collection module defines the human eye's sensitivity to glare and reflections as visual field-sensitive areas based on the visual characteristics of the human eye. The human eye has different visual perception abilities in different areas. For example, the human eye is more sensitive to brightness changes and contrast at the 10° visual field line or V1 point in the center of the visual field. Therefore, the most critical areas of visual perception for the driver or passenger can be designated as visual field-sensitive areas, and subsequent processing can be performed based on these visual field-sensitive areas.

[0129] Step S32, calibrating the target imaging cell based on the imaging index set and the target calibration parameter set, marking the cell color, and determining the calibration cell result and imaging position information;

[0130] It should be noted that the calibration cell result is an evaluation conclusion obtained after calibrating the target imaging cell, and the imaging position information is the actual position of the imaging in the target imaging cell in a specific scenario.

[0131] It is understandable that by quantifying imaging indicators and clear calibration parameters, the quality of each imaging cell can be accurately evaluated to ensure that the imaging meets the requirements, and the calibration results can be intuitively displayed by marking the cell color, making it easy to quickly identify problem areas.

[0132] For ease of understanding, the following description is made by taking the determination of the verification cell results and the imaging position information as an example, wherein the information acquisition device is the information acquisition module, the storage device is the memory, and the execution device is the execution module.

[0133] The information acquisition module obtains the imaging index set and the target calibration parameter set, calibrates the target imaging cell based on the imaging index set and the target calibration parameter set, marks the cell color, determines the calibration cell result and the imaging position information, and performs an automated calibration judgment when calibrating the target imaging cell. If the spot contrast C in the imaging index set is less than the corresponding threshold Cth in the target calibration parameter set corresponding to the target imaging cell and the imaging displacement in the imaging index set is less than the preset threshold, the calibration cell result is passed, and the marked cell color is green at this time. If the spot contrast C in the imaging index set is greater than the corresponding threshold Cth in the target calibration parameter set corresponding to the target imaging cell, that is, the bright trim C1 is detected to exceed the standard in the dusk scene, the calibration cell result is failed, and the marked cell color is red at this time. If it does not fall into these two situations, the calibration cell result is failed, the marked cell color is red, the imaging quality is detected, and the imaging position information is obtained. Subsequent processing is performed based on the calibration cell result and the imaging position information.

[0134] Step S33: identifying abnormal cases based on the calibration cell results, the imaging position information and the field of view sensitive area, tracking the target light reflection path, marking the abnormal number of target light intersections, and generating a calibration report.

[0135] It can be understood that abnormal cases are specific situations that do not meet design requirements or have potential visual interference problems, which are identified during the vehicle reflective imaging verification process through comprehensive analysis of verification cell results, imaging position information and field of view sensitive areas. Among them, the abnormal cases may include imaging position deviation exceeding the allowable range, imaging spot contrast lower than environmental requirements, high-brightness reflections or glare in the field of view sensitive area, and abnormal number of intersections in the target light reflection path, such as the "ghosting" phenomenon.

[0136] For ease of understanding, the example of obtaining the verification cell results and imaging position information is used for explanation, wherein the information acquisition device is the information acquisition module, the storage device is the memory, and the execution device is the execution module.

[0137] The information acquisition module obtains the verification cell results and imaging position information, generates a feedback report based on the verification cell results, and detects the imaging quality according to the imaging position information and the field of view sensitive area to determine the detection results. The feedback report includes a material replacement plan or a structural modification plan, identifies abnormal cases based on the detection results, determines abnormal case information, and when the abnormal case information indicates that there is no abnormal case, identifies the target imaging position deviation and generates optimization suggestions. When the abnormal case information indicates that there is an abnormal case, obtains light energy information, tracks the target light reflection path based on the light energy information, determines the number of intersections, marks the target light intersection number as abnormal based on the number of intersections, and generates an intersection abnormality report. That is, when the verification cell result is not passed, generates a material replacement plan, such as recommending that the material be replaced with matte aluminum, and GU=35-50 after replacement, and generates a structural modification plan, such as C=0.82 before modification and C=0.47 after modification. Detect the imaging quality. At this time, if the imaging position information is located in the field of view sensitive area, such as the 10° field of view line or the V1 point, the detection result is that the imaging quality is good, and an optimization suggestion is generated to adjust the screen inclination to 15°±2° and add a sunshade, such as one with a length greater than or equal to 50mm. If the detection result is that the imaging quality is poor, that is, there is an abnormal case, the target light reflection path is traced to mark the abnormal number of target light intersections. When the light energy is greater than the preset threshold, the light path is traced and the number of intersections is counted. If the number of intersections is greater than the preset threshold, such as the preset threshold is set to 2, it is marked as a ghosting abnormality and output. A verification report is generated based on the verification cell results, the feedback report, the optimization suggestions and the intersection abnormality report. The verification report includes a visual chart and an optimization suggestion table.

[0138] In a feasible implementation, step S33 may include steps D11 to D13:

[0139] Step D11, generating a feedback report based on the verification cell result, and detecting the imaging quality according to the imaging position information and the field of view sensitive area to determine the detection result, wherein the feedback report includes a material replacement plan or a structural modification plan;

[0140] It should be noted that the detection result is the result obtained by detecting the imaging quality.

[0141] It is understandable that the test results can indicate whether the imaging is within the allowable contrast range, whether the imaging position is accurate, and whether there are any abnormal conditions that interfere with the driver's vision. The feedback report provides specific improvement suggestions based on the test results, such as material replacement plans or structural modification plans, to optimize the imaging quality.

[0142] Step D12, identifying abnormal cases based on the detection results, tracking the target light reflection path, marking the abnormal number of target light intersections, and generating optimization suggestions and intersection abnormality reports;

[0143] It should be noted that the optimization suggestions are targeted improvement measures proposed based on the test results, and the intersection anomaly report is a detailed record and analysis of the abnormal number of intersections in the target light reflection path.

[0144] It is understandable that the optimization suggestions may include adjusting light source parameters, replacing reflective surface materials, changing reflective surface structures, and adjusting the position of light sources or reflective surfaces, and the intersection anomaly report is used to identify "ghosting" phenomena or other light reflection anomalies that may cause visual interference, and record in detail the number, location and causes of abnormal intersections, and propose corresponding solutions.

[0145] In a feasible implementation, step D12 may include steps E11 to E13:

[0146] Step E11, identifying abnormal cases based on the detection results and determining abnormal case information;

[0147] It should be noted that the abnormal case information is other abnormal situations identified through detection results that do not meet design requirements or have potential visual interference problems.

[0148] It is understandable that the abnormal case information may include the abnormality type, the specific location of the abnormality and the severity of the abnormality, and record the specific information of the abnormal case in detail, which can accurately identify imaging quality problems, quickly locate abnormal problems, ensure the visual comfort and safety of the vehicle under different lighting conditions and driving scenarios, and reduce driving risks caused by visual interference.

[0149] Step E12: when the abnormal case information indicates that there is no abnormal case, identifying the target imaging position deviation and generating an optimization suggestion;

[0150] It is understandable that when the abnormal case information shows that there are no abnormal cases, that is, the imaging quality meets the calibration standards, but there is still a deviation in the target imaging position. This deviation is the difference between the actual imaging position and the expected design position. The optimization suggestion is to propose improvement measures for the deviation to improve imaging accuracy and visual comfort.

[0151] Step E13, when the abnormal case information indicates that an abnormal case exists, obtain light energy information, track the target light reflection path based on the light energy information, determine the number of intersections, mark the target light intersection number as abnormal based on the number of intersections, and generate an intersection abnormality report.

[0152] It is understandable that when the abnormal case information shows that there is an abnormal case, it indicates that the imaging quality does not meet the calibration standards and there is an abnormal number of intersections in the light reflection path. At this time, it is necessary to obtain light energy information to track the reflection path of the target light and determine the number of intersections to determine whether it is abnormal. An abnormal number of intersections indicates that the light has formed redundant intersections during the reflection process, resulting in "ghosting" or other visual interference problems. The abnormal intersection problem is recorded to generate an intersection abnormality report.

[0153] Step D13: Generate a verification report based on the verification cell results, the feedback report, the optimization suggestions, and the intersection anomaly report. The verification report includes a visualization chart and an optimization suggestion table.

[0154] It is understandable that the calibration report integrates the results of all calibration cells, covers various combinations of light sources and reflective surfaces inside and outside the vehicle, provides detailed optimization suggestions and visual charts, accurately identifies potential reflection problems, and quickly locates and solves the problems.

[0155] This embodiment proposes a comprehensive vehicle reflective imaging calibration method based on a dynamic optical calibration matrix, which obtains a field of view sensitive area; calibrates the target imaging cells based on the imaging index set and the target calibration parameter set, marks the cell color, and determines the calibration cell results and imaging position information; identifies abnormal cases based on the calibration cell results, the imaging position information, and the field of view sensitive area, and tracks the target light reflection path to mark the abnormal number of target light intersections, and generates a calibration report. It solves the technical problem of how to more comprehensively cover complex light source combinations, adapt to dynamic environmental changes, and combine the visual characteristics of the human eye to conduct all-round calibration of vehicle reflective imaging. Compared with the existing technology, this application obtains the field of view sensitive area, and calibrates the target imaging cell based on the imaging index set and the target calibration parameter set, marks the cell color to determine the calibration result and imaging position information, combines the calibration results, imaging position information and field of view sensitive area to identify abnormal cases, tracks the target light reflection path and marks the abnormal number of intersections, thereby generating a calibration report. By accurately locating and analyzing abnormal situations, it significantly improves the calibration accuracy and driving safety, supports rapid iteration and improvement, and significantly improves development efficiency. At the same time, through automated processes and scientific judgment criteria, it greatly shortens the design cycle, reduces the error rate, and significantly enhances the user experience.

[0156] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the all-round verification method of vehicle reflective imaging based on the dynamic optical verification matrix of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0157] This application also provides a vehicle reflection imaging omnidirectional calibration device based on a dynamic optical calibration matrix, please refer to Figure 3 The vehicle reflection imaging omnidirectional calibration device based on the dynamic optical calibration matrix includes:

[0158] An acquisition module 10 is used to acquire optical property information and reflective surface property information;

[0159] A processing module 20 is configured to construct an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, and simulate a target scene to determine a target calibration parameter set and an imaging index set;

[0160] The execution module 30 is used to calibrate the target imaging cells based on the imaging index set and the target calibration parameter set, and track the target light reflection path to mark the abnormal number of target light intersections, generate a calibration report, and complete the full-scale calibration of vehicle reflective imaging based on the calibration report.

[0161] The acquisition module 10 is further used to acquire light source information and reflective surface information;

[0162] Classifying the light source information according to a light source classification system to determine optical property information, wherein the optical property information includes direct light source property information and indirect light source property information;

[0163] Based on the reflecting surface information, imaging attributes are identified and reflection characteristics are quantified to determine reflecting surface attribute information.

[0164] The processing module 20 is further configured to construct an optical calibration matrix based on the optical property information and the reflective surface property information, and determine a calibration cell set;

[0165] Matching corresponding calibration parameters based on the calibration cell set, and inputting the scenario working condition calibration model for training to determine the target calibration parameter set;

[0166] Performing scene simulation based on the verification cell set, loading environmental variables to match the corresponding simulation target scene, and determining scene parameter information, wherein the scene parameter information includes scene background brightness information and scene reflective surface information;

[0167] An imaging index set is calculated based on the scene parameter information, where the imaging index set includes spot contrast and imaging displacement.

[0168] The processing module 20 is further configured to obtain light brightness gradient, weather influencing factors, age difference information, and gender difference information;

[0169] determining a target limit value set based on matching corresponding calibration parameters of the calibration cell set;

[0170] Based on the light illuminance gradient, the weather influencing factor and the target limit value set, an environmental dynamic parameter library is input for training, and the corresponding target comparison value is adjusted to determine an environmental calibration parameter set;

[0171] A visual recognition model is input for training based on the age difference information, the gender difference information and the environmental calibration parameter set, and the corresponding target contrast value is adjusted to obtain a target calibration parameter set.

[0172] The execution module 30 is further used to obtain the field of view sensitive area;

[0173] Calibrate the target imaging cell based on the imaging index set and the target calibration parameter set, mark the cell color, and determine the calibration cell result and imaging position information;

[0174] Based on the verification cell results, the imaging position information and the field of view sensitive area, abnormal cases are identified, and the target light reflection path is tracked to mark the abnormal number of target light intersections, and a verification report is generated.

[0175] The execution module 30 is further configured to generate a feedback report based on the verification cell result, and detect the imaging quality according to the imaging position information and the field of view sensitive area to determine the detection result, wherein the feedback report includes a material replacement plan or a structural modification plan;

[0176] Identify abnormal cases based on the detection results, track the target light reflection path, mark the abnormal number of target light intersections, and generate optimization suggestions and intersection abnormality reports;

[0177] A verification report is generated based on the verification cell results, the feedback report, the optimization suggestions, and the intersection anomaly report, wherein the verification report includes a visualization chart and an optimization suggestion table.

[0178] The execution module 30 is further configured to identify abnormal cases based on the detection results and determine abnormal case information;

[0179] When the abnormal case information indicates that there is no abnormal case, identifying the target imaging position deviation and generating an optimization suggestion;

[0180] When the abnormal case information indicates that an abnormal case exists, light energy information is obtained, the reflection path of the target light is tracked based on the light energy information, the number of intersections is determined, the number of target light intersections is marked as abnormal based on the number of intersections, and an intersection abnormality report is generated.

[0181] The all-round calibration device for vehicle reflective imaging based on a dynamic optical calibration matrix provided in this application adopts the all-round calibration method for vehicle reflective imaging based on a dynamic optical calibration matrix in the above-mentioned embodiment, which can solve the technical problem of how to more comprehensively cover complex light source combinations, adapt to dynamic environmental changes, and combine the visual characteristics of the human eye to perform all-round calibration of vehicle reflective imaging. Compared with the existing technology, the beneficial effects of the all-round calibration device for vehicle reflective imaging based on a dynamic optical calibration matrix provided in this application are the same as the beneficial effects of the all-round calibration method for vehicle reflective imaging based on a dynamic optical calibration matrix provided in the above-mentioned embodiment, and the other technical features of the all-round calibration device for vehicle reflective imaging based on a dynamic optical calibration matrix are the same as the features disclosed in the above-mentioned embodiment method, and are not further described here.

[0182] The present application provides a comprehensive vehicle reflective imaging calibration device based on a dynamic optical calibration matrix. The comprehensive vehicle reflective imaging calibration device based on a dynamic optical calibration matrix includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the comprehensive vehicle reflective imaging calibration method based on the dynamic optical calibration matrix in the above-mentioned embodiment one.

[0183] Reference below Figure 4 , which shows a schematic diagram of the structure of a omnidirectional vehicle reflective imaging calibration device based on a dynamic optical calibration matrix suitable for implementing the embodiments of the present application. The omnidirectional vehicle reflective imaging calibration device based on a dynamic optical calibration matrix in the embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The vehicle reflective imaging omnidirectional calibration device based on the dynamic optical calibration matrix shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0184] like Figure 4As shown, the omnidirectional vehicle reflective imaging calibration device based on a dynamic optical calibration matrix may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a ROM (Read Only Memory) 1002 or programs loaded from a storage device 1003 into a RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the omnidirectional vehicle reflective imaging calibration device based on a dynamic optical calibration matrix. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. Communication devices 1009 can allow the vehicle reflective imaging omnidirectional calibration device based on a dynamic optical calibration matrix to communicate wirelessly or wired with other devices to exchange data. While the figure shows the vehicle reflective imaging omnidirectional calibration device based on a dynamic optical calibration matrix with various systems, it should be understood that implementation or presence of all the illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0185] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0186] The all-round calibration device for vehicle reflective imaging based on a dynamic optical calibration matrix provided in this application adopts the all-round calibration method for vehicle reflective imaging based on a dynamic optical calibration matrix in the above-mentioned embodiment, which can solve the technical problem of how to more comprehensively cover complex light source combinations, adapt to dynamic environmental changes, and combine the visual characteristics of the human eye to perform all-round calibration of vehicle reflective imaging. Compared with the existing technology, the beneficial effects of the all-round calibration device for vehicle reflective imaging based on a dynamic optical calibration matrix provided in this application are the same as the beneficial effects of the all-round calibration method for vehicle reflective imaging based on a dynamic optical calibration matrix provided in the above-mentioned embodiment, and the other technical features of the all-round calibration device for vehicle reflective imaging based on a dynamic optical calibration matrix are the same as the features disclosed in the method of the previous embodiment, and will not be repeated here.

[0187] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0188] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0189] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the all-round verification method of vehicle reflective imaging based on the dynamic optical verification matrix in the above-mentioned embodiment.

[0190] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0191] The computer-readable storage medium may be included in the vehicle reflective imaging omnidirectional calibration device based on the dynamic optical calibration matrix; or it may exist independently without being assembled into the vehicle reflective imaging omnidirectional calibration device based on the dynamic optical calibration matrix.

[0192] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle reflective imaging all-round calibration device based on the dynamic optical calibration matrix, the vehicle reflective imaging all-round calibration device based on the dynamic optical calibration matrix: obtains optical property information and reflective surface property information; constructs an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, and simulates the target scene to determine the target calibration parameter set and the imaging index set; calibrates the target imaging cells based on the imaging index set and the target calibration parameter set, and tracks the target light reflection path to mark the abnormal number of target light intersections, generates a calibration report, and completes the vehicle reflective imaging all-round calibration based on the calibration report.

[0193] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0194] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0195] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0196] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for omnidirectional calibration of vehicle reflective imaging based on a dynamic optical calibration matrix. This method addresses the technical problem of more comprehensively covering complex light source combinations, adapting to dynamic environmental changes, and incorporating the visual characteristics of the human eye to perform omnidirectional calibration of vehicle reflective imaging. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the method for omnidirectional calibration of vehicle reflective imaging based on a dynamic optical calibration matrix provided in the aforementioned embodiments, and are not further elaborated here.

[0197] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A omnidirectional calibration method for vehicle reflective imaging based on a dynamic optical calibration matrix, characterized in that: The method includes: Obtaining optical property information and reflective surface property information; Constructing an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, and simulating a target scene to determine a target calibration parameter set and an imaging index set; The target imaging cells are calibrated based on the imaging index set and the target calibration parameter set, and the target light reflection path is tracked to mark the abnormal number of target light intersections, and a calibration report is generated. Based on the calibration report, a full-scale calibration of the vehicle reflective imaging is completed.

2. The method according to claim 1, wherein The step of obtaining optical property information and reflective surface property information comprises: Obtain light source information and reflecting surface information; Classifying the light source information according to a light source classification system to determine optical property information, wherein the optical property information includes direct light source property information and indirect light source property information; Based on the reflecting surface information, imaging attributes are identified and reflection characteristics are quantified to determine reflecting surface attribute information.

3. The method according to claim 1, wherein The steps of constructing an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, simulating a target scene, and determining a target calibration parameter set and an imaging index set include: Constructing an optical calibration matrix based on the optical property information and the reflective surface property information, and determining a calibration cell set; Matching corresponding calibration parameters based on the calibration cell set, and inputting the scenario working condition calibration model for training to determine the target calibration parameter set; Performing scene simulation based on the verification cell set, loading environmental variables to match the corresponding simulation target scene, and determining scene parameter information, wherein the scene parameter information includes scene background brightness information and scene reflective surface information; An imaging index set is calculated based on the scene parameter information, where the imaging index set includes spot contrast and imaging displacement.

4. The method according to claim 3, wherein The step of matching corresponding calibration parameters based on the calibration cell set and inputting the scenario working condition calibration model for training to determine the target calibration parameter set includes: Obtain light intensity gradient, weather influencing factors, age difference information, and gender difference information; determining a target limit value set based on matching corresponding calibration parameters of the calibration cell set; Based on the light illuminance gradient, the weather influencing factor and the target limit value set, an environmental dynamic parameter library is input for training, and the corresponding target comparison value is adjusted to determine an environmental calibration parameter set; A visual recognition model is input for training based on the age difference information, the gender difference information and the environmental calibration parameter set, and the corresponding target contrast value is adjusted to obtain a target calibration parameter set.

5. The method according to claim 1, wherein The step of calibrating the target imaging cell based on the imaging index set and the target calibration parameter set, tracking the target light reflection path and marking the abnormal number of target light intersections, and generating a calibration report includes: Get the sensitive area of vision; Calibrate the target imaging cell based on the imaging index set and the target calibration parameter set, mark the cell color, and determine the calibration cell result and imaging position information; Based on the verification cell results, the imaging position information and the field of view sensitive area, abnormal cases are identified, and the target light reflection path is tracked to mark the abnormal number of target light intersections, and a verification report is generated.

6. The method according to claim 5, wherein The step of identifying abnormal cases based on the calibration cell results, the imaging position information, and the field of view sensitive area, and tracking the target light reflection path to mark the abnormal number of target light intersections, and generating a calibration report includes: generating a feedback report based on the verification cell result, detecting imaging quality according to the imaging position information and the field of view sensitive area, and determining a detection result, wherein the feedback report includes a material replacement plan or a structural modification plan; Identify abnormal cases based on the detection results, track the target light reflection path, mark the abnormal number of target light intersections, and generate optimization suggestions and intersection abnormality reports; A verification report is generated based on the verification cell results, the feedback report, the optimization suggestions, and the intersection anomaly report, wherein the verification report includes a visualization chart and an optimization suggestion table.

7. The method according to claim 6, wherein The steps of identifying abnormal cases based on the detection results, tracking the target light reflection path, marking the abnormal number of target light intersections, and generating optimization suggestions and intersection abnormality reports include: Identify abnormal cases based on the detection results and determine abnormal case information; When the abnormal case information indicates that there is no abnormal case, identifying the target imaging position deviation and generating an optimization suggestion; When the abnormal case information indicates that an abnormal case exists, light energy information is obtained, the reflection path of the target light is tracked based on the light energy information, the number of intersections is determined, the number of target light intersections is marked as abnormal based on the number of intersections, and an intersection abnormality report is generated.

8. A omnidirectional vehicle reflection imaging calibration device based on a dynamic optical calibration matrix, characterized in that: The device comprises: An acquisition module, used to acquire optical property information and reflective surface property information; A processing module, configured to construct an optical calibration matrix based on the optical property information and the reflective surface property information to generate calibration cells, and simulate a target scene to determine a target calibration parameter set and an imaging index set; An execution module is used to calibrate the target imaging cells based on the imaging index set and the target calibration parameter set, track the target light reflection path, mark the abnormal number of target light intersections, generate a calibration report, and complete the full-scale calibration of vehicle reflective imaging based on the calibration report.

9. A vehicle reflective imaging omnidirectional calibration device based on a dynamic optical calibration matrix, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the all-round verification method for vehicle reflective imaging based on a dynamic optical verification matrix as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a machine-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the all-round verification method of vehicle reflection imaging based on a dynamic optical verification matrix as described in any one of claims 1 to 7 are implemented.

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