AR-HUD offline detection method, electronic equipment and computer storage medium

Abstract

The invention discloses an AR-HUD offline detection method, electronic equipment and a computer storage medium, the AR-HUD offline detection method is applied to AR-HUD detection equipment, and the method comprises the following steps: obtaining a detection image of AR-HUD; according to the detection image, analyzing a picture display effect of the AR-HUD; and according to the picture display effect, sending a preset instruction to the AR-HUD system so as to call a system instruction of the AR-HUD system to adjust the picture display effect of the AR-HUD. According to the technical scheme, after the detection image is analyzed, the instruction of the AR-HUD system can be directly called to complete adjustment of the picture display effect, the detection time is shortened, the detection efficiency is improved, and the detection equipment is further automated.

Description

Technical Field

[0001] The present application relates to the field of HUD detection technology, and in particular to an AR-HUD offline detection method, electronic device and computer storage medium. Background Art

[0002] At present, many vehicles on the market are equipped with AR-HUD (Augmented Reality-Head Up Display) functions, but due to the different functions included in the head-up display, the test operations required are also different. The existing AR-HUD integrates complex information. Through a specially designed internal optical system, the image information is accurately combined with the actual traffic conditions, and the tire pressure, speed, rotation speed and other information are projected onto the front windshield, so that the owner can view vehicle-related information without lowering his head while driving. The AR-HUD system is based on the projection of a multiple off-axis reflection system. There are many factors related to its imaging quality. In order to ensure that the function is intact and reliable, it is necessary to use relevant equipment to test the imaging-related conditions and perform image correction for possible virtual image distortion, ghosting, size, etc. In the related technology, the detection equipment is an independent device and cannot interact with the AR-HUD system or call commands. It is usually necessary to use the detection equipment to analyze the detection image of the AR-HUD first. If the detection is determined to be unqualified, the inspector will then connect to the AR-HUD system through a computer to control it and adjust the parameters to adjust the screen display effect. The detection equipment is not automated enough, the detection time is long, and the detection efficiency is low. Summary of the invention

[0003] The purpose of this application is to provide an AR-HUD offline detection method, electronic device and computer storage medium. After analyzing the detection image, the AR-HUD system's own instructions can be directly called to complete the adjustment of the screen display effect, shorten the detection time, improve the detection efficiency, and make the detection equipment further automated.

[0004] To achieve the above purpose, the technical solution of this application is implemented as follows:

[0005] In a first aspect, the present application provides an AR-HUD offline detection method, which is applied to an AR-HUD detection device, and the method includes:

[0006] Get the detection image of AR-HUD;

[0007] Analyzing the screen display effect of the AR-HUD according to the detection image;

[0008] According to the screen display effect, a preset instruction is sent to the AR-HUD system to call the system instruction of the AR-HUD system to adjust the screen display effect of the AR-HUD.

[0009] As one implementation manner, the detection image includes a distorted detection image, and analyzing the screen display effect of the AR-HUD according to the detection image includes:

[0010] Obtaining coordinates of four corners of the distortion detection image;

[0011] Calculate the coordinates of the center point of the distortion detection image and the standard distances between the four corners and the center point according to the coordinates of the four corners of the distortion detection image;

[0012] Calculating the maximum offset between the four corners of the distortion detection image and the center point;

[0013] According to the ratio of the maximum offset to the standard distance, the screen display effect of the AR-HUD is analyzed.

[0014] As one implementation manner, the detection image includes a lower viewing angle detection image, and analyzing the screen display effect of the AR-HUD according to the detection image includes:

[0015] Analyzing the lower viewing angle of the AR-HUD according to the lower viewing angle detection image;

[0016] The AR-HUD screen display effect is analyzed according to the lower viewing angle to see whether it meets the preset angle.

[0017] As one implementation manner, sending a preset instruction to the AR-HUD system according to the screen display effect to call the system instruction of the AR-HUD system to adjust the screen display effect of the AR-HUD includes:

[0018] If the lower viewing angle does not meet the preset angle, a lower viewing angle zero correction instruction is sent to the AR-HUD system to call the lower viewing angle adjustment instruction of the AR-HUD system to adjust the lower viewing angle until it meets the preset angle.

[0019] As one implementation manner, before acquiring the detection image of the AR-HUD, the method includes:

[0020] Obtain the vehicle's front wheel arch height and the preset front wheel arch height;

[0021] According to the difference between the front wheel eyebrow height of the vehicle and the preset front wheel eyebrow height, height compensation is performed on the camera of the AR-HUD detection device.

[0022] As one implementation manner, before acquiring the detection image of the AR-HUD, the method further includes:

[0023] Obtaining the vehicle's front wheel eyebrow height, the preset front wheel eyebrow height, the vehicle's rear wheel eyebrow height, the preset rear wheel eyebrow height and the vehicle's wheelbase;

[0024] Calculate the downward viewing angle compensation angle of the camera of the AR-HUD detection device according to the front wheel eyebrow height of the vehicle, the preset front wheel eyebrow height, the rear wheel eyebrow height of the vehicle, the preset rear wheel eyebrow height and the vehicle wheelbase;

[0025] According to the downward viewing angle compensation angle, downward viewing angle compensation is performed on the camera of the AR-HUD detection device.

[0026] In a second aspect, the present application also provides an AR-HUD offline detection method, which is applied to an AR-HUD system, and the method includes:

[0027] Receiving a preset instruction sent by an AR-HUD detection device, wherein the preset instruction is sent by the AR-HUD detection device after analyzing a screen display effect based on a detection image of the AR-HUD system;

[0028] According to the preset instructions, the corresponding system instructions are called to adjust the screen display effect of the AR-HUD.

[0029] As one implementation manner, the method further includes:

[0030] Before starting the test, adjust the output brightness value to the maximum value.

[0031] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the AR-HUD offline detection method as described in the first aspect and / or the second aspect are implemented.

[0032] In a fourth aspect, the present application provides a computer storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the AR-HUD offline detection method as described in the first aspect and / or the second aspect are implemented.

[0033] The present application provides an AR-HUD offline detection method, electronic device, and computer storage medium, which are applied to AR-HUD detection equipment. The method includes: obtaining a detection image of the AR-HUD; analyzing the screen display effect of the AR-HUD according to the detection image; and sending a preset instruction to the AR-HUD system according to the screen display effect to call the system instruction of the AR-HUD system to adjust the screen display effect of the AR-HUD. After analyzing the detection image, the technical solution of the present application can directly call the instructions of the AR-HUD system itself to complete the adjustment of the screen display effect, shortening the detection time, improving the detection efficiency, and further automating the detection equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A flowchart of an AR-HUD offline detection method provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of AR-HUD screen distortion provided in an embodiment of the present application;

[0037] Figure 3 A flowchart of another AR-HUD offline detection method provided in an embodiment of the present application;

[0038] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0039] Processor 110 , memory 111 , network interface 112 , and bus system 113 . DETAILED DESCRIPTION

[0040] The specific embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the description of the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0041] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0042] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising" and "including" indicate the presence of the described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0043] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0044] It should be noted that, in this article, step codes such as S101, S102, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing the step, those skilled in the art may execute S102 first and then S101, etc., but these should all be within the scope of protection of this application.

[0045] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] See also Figure 1 , which is a flow chart of an AR-HUD offline detection method provided in an embodiment of the present application. The AR-HUD offline detection method can be performed by an AR-HUD detection device provided in an embodiment of the present application. The AR-HUD offline detection method of the present application includes the following steps:

[0047] Step S101: Acquire a detection image of the AR-HUD;

[0048] Step S102: analyzing the display effect of the AR-HUD according to the detected image;

[0049] Step S103: according to the screen display effect, send a preset instruction to the AR-HUD system to call the system instruction of the AR-HUD system to adjust the screen display effect of the AR-HUD.

[0050] Here, after the vehicle stops in the detection area, the vehicle is connected to the AR-HUD detection device, the vehicle VIN number is scanned by the barcode scanner on the AR-HUD detection device, and the vehicle VIN number is bound to the AR-HUD detection device to start the detection device. The AR-HUD detection device can be used to call the system instructions of the AR-HUD system, and the vehicle detection results are bound and stored with the corresponding vehicle VIN number.

[0051] Among them, the detection image of AR-HUD can be a bottom viewing angle detection image, a distortion detection image, a left viewing angle detection image, an image size detection image, a projection distance detection image, a ghosting detection image, etc. The AR-HUD detection device can acquire the image output by the AR-HUD system of the vehicle to obtain the detection image of AR-HUD, and analyze whether the screen display effect of AR-HUD is abnormal based on any of the above detection images.

[0052] Specifically, the AR-HUD system has some pre-buried instructions, such as moving the lower viewing angle up or down, moving the upper left corner of the image to the lower right corner, etc. When the AR-HUD detection device analyzes that the display effect does not meet the conditions, these pre-buried instructions can be used to adjust the AR-HUD image. The detection device can call the system instructions of the AR-HUD system by sending preset instructions to the AR-HUD system to adjust the AR-HUD screen position or correct the screen, etc. Therefore, the AR-HUD system's own adjustment function can be used to directly adjust the AR-HUD system after detection to improve detection efficiency.

[0053] In summary, in the AR-HUD offline detection method provided in the above embodiment, the AR-HUD detection device obtains the detection image of the AR-HUD, then analyzes the screen display effect of the AR-HUD according to the detection image, and sends a preset instruction to the AR-HUD system according to the screen display effect, so as to call the system instruction of the AR-HUD system to adjust the screen display effect of the AR-HUD. After analyzing the detection image, the AR-HUD system's own instructions can be directly called to complete the adjustment of the screen display effect, which shortens the detection time, improves the detection efficiency, and makes the detection equipment further automated.

[0054] In one embodiment, before acquiring the detection image of the AR-HUD, the following steps are included:

[0055] Obtain the vehicle's front wheel arch height and the preset front wheel arch height;

[0056] According to the difference between the front wheel arch height of the vehicle and the preset front wheel arch height, the camera of the AR-HUD detection equipment is height compensated.

[0057] In one embodiment, before acquiring the detection image of the AR-HUD, the method further includes:

[0058] Obtain the vehicle's front wheel arch height, preset front wheel arch height, vehicle's rear wheel arch height, preset rear wheel arch height and vehicle's wheelbase;

[0059] Calculate the downward viewing angle compensation angle of the camera of the AR-HUD detection device according to the front wheel eyebrow height of the vehicle, the preset front wheel eyebrow height, the rear wheel eyebrow height of the vehicle, the preset rear wheel eyebrow height and the vehicle wheelbase;

[0060] According to the downward viewing angle compensation angle, the downward viewing angle compensation is performed on the camera of the AR-HUD detection equipment.

[0061] Here, before starting the test, the vehicle is positioned and the camera position of the AR-HUD test equipment is compensated for height and downward viewing angle. In this way, when the vehicle is positioned, the height of the wheel arches of the four wheels of the vehicle is checked, and the height error of the vehicle is imported into the camera movement data of the AR-HUD test equipment to ensure that the camera position coincides with the eye box position of the vehicle, thereby ensuring the accuracy of the image display effect detection and correction results.

[0062] Specifically, by calculating the difference between the height of the front wheel arch of the vehicle and the preset wheel arch height, the height compensation value ΔHF that needs to be input to the camera is obtained. The compensation angle of the viewing angle that needs to be input to the camera is calculated by the following formula:

[0063] α=arctan[((FA-FAS)-(RA-RAS)) / L]

[0064] Among them, α is the downward viewing angle compensation angle, arctan is the inverse sine function, FA is the height of the front wheel eyebrow of the vehicle, FAS is the preset front wheel eyebrow height, RA is the height of the rear wheel eyebrow of the vehicle, RAS is the preset rear wheel eyebrow height, and L is the wheelbase of the vehicle.

[0065] In one embodiment, the detection image includes a distorted detection image, and according to the detection image, analyzing the display effect of the AR-HUD includes:

[0066] Get the coordinates of the four corners of the distortion detection image;

[0067] According to the coordinates of the four corners of the distortion detection image, the coordinates of the center point of the distortion detection image and the standard distances between the four corners and the center point are calculated;

[0068] Calculate the maximum offset between the four corners and the center point of the distortion detection image;

[0069] According to the ratio of the maximum offset to the standard distance, the display effect of AR-HUD is analyzed.

[0070] Here, the distortion detection of the AR-HUD screen display effect is performed by calculating the distortion rate of the distortion detection image, such as Figure 2 The figure shows a schematic diagram of image distortion. First, the coordinates of the four corners of the distortion detection image are obtained as (X1, Y1), (X2, Y2), (X3, Y3), and (X4, Y4), and then the coordinates of the center point of the distortion detection image (X0, Y0) are calculated using the following formula:

[0071]

[0072] Then the standard distance D between the four corners and the center point of the distortion detection image is calculated by the following formula:

[0073] X′1=X1-X0,Y′1=Y1-Y0

[0074] X′2=X2-X0,Y′2=Y2-Y0

[0075] X′3=X3-X0,Y′3=Y3-Y0

[0076] X′4=X4-X0,Y′4=Y4-Y0

[0077]

[0078] Then the ratio of the maximum offset between the four corners and the center point of the distortion detection image and the standard distance is calculated by the following formula:

[0079]

[0080] If the ratio is greater than 5%, it is judged as NG (Not Good) and the test is exited; if the ratio is greater than 3% and not greater than 5%, it is judged that the image needs to be distorted; if the ratio is less than or equal to 3%, the distortion test is judged to be qualified. In this way, there is no need to set up an additional reference plate (standard grid pattern) in front of the detection equipment. During imaging calibration, the image on the reference plate is collected for calibration, and the reference plate is used as a standard image to compare with the actual image. The image distortion rate is calculated by analyzing the difference between the actual image and the theoretical image calculated based on the actual image, so that distortion detection can be achieved without comparing with the actual object, reducing the complexity of the equipment and shortening the detection time.

[0081] In one embodiment, the detection image includes a lower viewing angle detection image, and according to the detection image, analyzing the screen display effect of the AR-HUD includes:

[0082] Analyze the downward viewing angle of the AR-HUD based on the downward viewing angle detection image;

[0083] Analyze whether the AR-HUD screen display effect meets the preset angle based on the downward viewing angle.

[0084] In one embodiment, according to the screen display effect, a preset instruction is sent to the AR-HUD system to call the system instruction of the AR-HUD system to adjust the screen display effect of the AR-HUD, including:

[0085] If the lower viewing angle does not meet the preset angle, a lower viewing angle zero correction instruction is sent to the AR-HUD system to call the lower viewing angle adjustment instruction of the AR-HUD system to adjust the lower viewing angle until it meets the preset angle.

[0086] Here, the lower viewing angle detection is performed on the AR-HUD screen display effect, and the lower viewing angle of the AR-HUD is analyzed based on the lower viewing angle detection image to see if it meets the preset angle. If not, a zero position correction instruction is sent to the AR-HUD system to call the lower viewing angle adjustment instruction of the AR-HUD system to adjust the lower viewing angle, and the lower viewing angle detection is performed again. If the detection fails three times, it is determined to be NG and the detection is exited. In this way, when the AR-HUD screen display effect does not meet the conditions, zero position correction can be automatically performed, making the detection equipment more automated.

[0087] See also Figure 3 , which is a flow chart of another AR-HUD offline detection method provided in an embodiment of the present application. The AR-HUD offline detection method can be executed by an AR-HUD system provided in an embodiment of the present application. The AR-HUD system can be implemented in software and / or hardware. The AR-HUD offline detection method of the present application includes the following steps:

[0088] Step S201: receiving a preset instruction sent by an AR-HUD detection device, where the preset instruction is sent by the AR-HUD detection device after analyzing the screen display effect based on the detection image of the AR-HUD system;

[0089] Step S202: According to the preset instructions, call the corresponding system instructions to adjust the screen display effect of the AR-HUD.

[0090] Here, after the vehicle stops in the detection area, the vehicle is connected to the AR-HUD detection device, the vehicle VIN number is scanned by the barcode scanner on the AR-HUD detection device, and the vehicle VIN number is bound to the detection device to start the detection device. The system instructions of the AR-HUD system can be called through the detection device, and the AR-HUD system can be switched to the detection mode. In this mode, the display parameters of the AR-HUD can be adjusted by calling the system instructions to adjust the screen display effect of the AR-HUD, and the vehicle detection results can be bound and stored with the corresponding vehicle VIN number.

[0091] Among them, the detection image can be a lower viewing angle detection image, a distortion detection image, a left viewing angle detection image, an image size detection image, a projection distance detection image, a ghosting detection image, etc. After the detection image is output to the AR-HUD detection device, when the AR-HUD detection device analyzes that the display effect of the screen is poor, by receiving the preset instructions sent by the AR-HUD detection device, the corresponding system instructions are called to adjust the screen position of the AR-HUD or correct the screen, etc. Here, the AR-HUD system has pre-buried some instructions, such as moving the lower viewing angle up and down, moving the upper left corner of the image to the lower right corner, etc.

[0092] In summary, in the AR-HUD offline detection method provided in the above embodiment, the AR-HUD system receives the preset instructions sent by the AR-HUD detection device after analyzing the screen display effect based on the detection image of the AR-HUD system, and calls the corresponding system instructions according to the preset instructions to adjust the screen display effect of the AR-HUD, so that the AR-HUD system's own instructions can be directly called to complete the adjustment of the screen display effect, which shortens the detection time, improves the detection efficiency, and makes the detection equipment further automated.

[0093] In one embodiment, the method further comprises:

[0094] Before starting the test, adjust the output brightness value to the maximum value.

[0095] Based on the same inventive concept as the above embodiments, the present application embodiment provides an electronic device, such as Figure 4 As shown, the electronic device includes: a processor 110 and a memory 111 for storing a computer program that can be run on the processor 110; wherein, Figure 4 The processor 110 shown in the figure is not used to indicate that the number of the processor 110 is one, but is only used to indicate the positional relationship of the processor 110 relative to other devices. In actual applications, the number of the processor 110 may be one or more; similarly, Figure 4 The memory 111 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 111 relative to other devices. In practical applications, the number of memories 111 can be one or more. The processor 110 is used to implement the AR-HUD offline detection method when running the computer program.

[0096] The electronic device may also include: at least one network interface 112. The various components in the electronic device are coupled together via a bus system 113. It is understood that the bus system 113 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 113 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, in Figure 4 Various buses are labeled as bus system 113 .

[0097] The memory 111 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. The nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 111 described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memories.

[0098] The memory 111 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device. Examples of these data include: any computer program used to operate on the electronic device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and handle hardware-based tasks. The application program may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. Here, the program that implements the method of the embodiment of the present application may be included in the application program.

[0099] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer storage medium, in which a computer program is stored. The computer storage medium may be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM) or the like; it may also be various devices including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer storage medium is run by the processor, the above-mentioned AR-HUD offline detection method is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The description of the illustrated embodiment will not be repeated here.

[0100] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An AR-HUD offline detection method, applied to AR-HUD detection equipment, characterized in that: The method comprises: Get the detection image of AR-HUD; Analyzing the screen display effect of the AR-HUD according to the detection image; According to the screen display effect, a preset instruction is sent to the AR-HUD system to call the system instruction of the AR-HUD system to adjust the screen display effect of the AR-HUD.

2. The AR-HUD offline detection method according to claim 1, characterized in that: The detection image includes a distorted detection image, and analyzing the screen display effect of the AR-HUD according to the detection image includes: Obtaining coordinates of four corners of the distortion detection image; Calculate the coordinates of the center point of the distortion detection image and the standard distances between the four corners and the center point according to the coordinates of the four corners of the distortion detection image; Calculating the maximum offset between the four corners of the distortion detection image and the center point; According to the ratio of the maximum offset to the standard distance, the screen display effect of the AR-HUD is analyzed.

3. The AR-HUD offline detection method according to claim 1, characterized in that: The detection image includes a lower viewing angle detection image, and analyzing the screen display effect of the AR-HUD according to the detection image includes: Analyzing the lower viewing angle of the AR-HUD according to the lower viewing angle detection image; The AR-HUD screen display effect is analyzed according to the lower viewing angle to see whether it meets the preset angle.

4. The AR-HUD offline detection method according to claim 3, characterized in that: According to the screen display effect, sending a preset instruction to the AR-HUD system to call the system instruction of the AR-HUD system to adjust the screen display effect of the AR-HUD includes: If the lower viewing angle does not meet the preset angle, a lower viewing angle zero correction instruction is sent to the AR-HUD system to call the lower viewing angle adjustment instruction of the AR-HUD system to adjust the lower viewing angle until it meets the preset angle.

5. The AR-HUD offline detection method according to claim 1, characterized in that: Before obtaining the detection image of the AR-HUD, the following steps are included: Obtain the vehicle's front wheel arch height and the preset front wheel arch height; According to the difference between the front wheel eyebrow height of the vehicle and the preset front wheel eyebrow height, height compensation is performed on the camera of the AR-HUD detection device.

6. The AR-HUD offline detection method according to claim 5, characterized in that: Before acquiring the detection image of the AR-HUD, the method further includes: Obtaining the vehicle's front wheel eyebrow height, the preset front wheel eyebrow height, the vehicle's rear wheel eyebrow height, the preset rear wheel eyebrow height and the vehicle's wheelbase; Calculate the downward viewing angle compensation angle of the camera of the AR-HUD detection device according to the front wheel eyebrow height of the vehicle, the preset front wheel eyebrow height, the rear wheel eyebrow height of the vehicle, the preset rear wheel eyebrow height and the vehicle wheelbase; According to the downward viewing angle compensation angle, downward viewing angle compensation is performed on the camera of the AR-HUD detection device.

7. An AR-HUD offline detection method, applied to an AR-HUD system, characterized in that: The method comprises: Receiving a preset instruction sent by an AR-HUD detection device, wherein the preset instruction is sent by the AR-HUD detection device after analyzing a screen display effect based on a detection image of the AR-HUD system; According to the preset instructions, the corresponding system instructions are called to adjust the screen display effect of the AR-HUD.

8. The AR-HUD offline detection method according to claim 7, characterized in that: The method further comprises: Before starting the test, adjust the output brightness value to the maximum value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the AR-HUD offline detection method according to any one of claims 1 to 8 are implemented.

10. A computer storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the AR-HUD offline detection method according to any one of claims 1 to 8 are implemented.

Images