Traffic sign generation method, device and equipment

Abstract

The present application relates to a method, device and equipment for generating traffic signs. The method includes: obtaining at least two images, each of which contains at least one common road sign; performing road sign recognition and road sign corner point recognition on each image respectively, obtaining the position information of the road sign in at least two images respectively, and the pixel coordinates of the road sign corner points in at least two images respectively; clustering all the identified road sign corner points according to the position information of the road sign in at least two images respectively and the pixel coordinates of the road sign corner points in at least two images respectively, and determining the road sign to which each road sign corner point corresponds; generating the geographic coordinates of each road sign according to the pixel coordinates of each road sign corner point corresponding to each road sign in at least two images respectively, and the geographic location information of the camera. The solution provided by the present application can automatically generate road signs and realize the rapid generation of at least one road sign, thereby improving the efficiency of drawing high-precision maps.

Description

Technical Field

[0001] The present application relates to the field of navigation technology, and in particular to a method, device and equipment for generating traffic signs. Background Art

[0002] With the development of technologies such as artificial intelligence and autonomous driving, building smart transportation has also become a research hotspot, and high-precision maps are an indispensable part of building smart transportation data. High-precision maps can contain a variety of traffic signs. For example, detailed lane maps can be used to express ground feature elements such as lane lines, stop lines, and crosswalks in the real world, as well as high-altitude feature elements such as road signs and traffic lights, so as to provide data support for navigation in application scenarios such as autonomous driving.

[0003] Traffic signs are information carriers of urban geographic entities, with navigation functions such as place names, routes, distances and directions. As infrastructure distributed at intersections of urban roads, they have their own particularity in space and are a good carrier of the city's basic Internet of Things. Correctly and efficiently generating traffic signs is critical to the drawing of high-precision maps.

[0004] However, in the related art, the images obtained by using the vehicle's camera cannot fully automatically generate the road signs contained in the image. The process usually requires human intervention, which in turn affects the efficiency of high-precision map drawing. Summary of the invention

[0005] In order to solve or partially solve the problems existing in the related technology, the present application provides a method, device and equipment for generating traffic signs, which can automatically generate road signs and realize the rapid generation of at least one road sign, thereby improving the efficiency of drawing high-precision maps.

[0006] The first aspect of the present application provides a method for generating a traffic sign, comprising:

[0007] Acquire at least two images, each of the images containing at least one common road sign;

[0008] Performing road sign recognition and road sign corner point recognition on each of the images respectively, to obtain the position information of the road sign in at least two of the images respectively, and the pixel coordinates of the road sign corner points in at least two of the images respectively;

[0009] Clustering all the identified road sign corner points according to the position information of the road sign in at least two of the images and the pixel coordinates of the road sign corner points in at least two of the images, and determining the road sign to which each road sign corner point corresponds;

[0010] The geographic coordinates of each road sign are generated according to the pixel coordinates of each road sign corner point corresponding to each road sign in the at least two images, and the geographic location information of the camera when the at least two images are taken.

[0011] In one embodiment, each of the images contains at least one common road sign, including:

[0012] Each of the images contains at least two common road signs.

[0013] In one implementation, the performing road sign recognition and road sign corner point recognition on each of the images respectively includes:

[0014] The preset target detection model is used to perform road sign recognition and road sign corner point recognition on each of the images.

[0015] In one embodiment, the preset target detection model is a preset target detection model after training; the training method of the preset target detection model includes training the preset target detection model using a preset training data set, wherein the preset training data set includes multiple training images with marked road signs and road sign corners.

[0016] In one implementation, the preset target detection model is a YOLO target detection model.

[0017] In one implementation, the location information of the road sign in at least two of the images includes:

[0018] The center point of the road sign is respectively at pixel coordinates in at least two of the images.

[0019] In one embodiment, clustering all the identified road sign corner points according to the position information of the road sign in at least two of the images and the pixel coordinates of the road sign corner points in at least two of the images to determine the road sign to which each road sign corner point corresponds includes:

[0020] According to the position information of the road sign in at least two of the images and the pixel coordinates of the road sign corner points in at least two of the images, a preset clustering algorithm is used to cluster all the identified road sign corner points to determine the road sign to which each road sign corner point corresponds.

[0021] A second aspect of the present application provides a device for generating a traffic sign, comprising:

[0022] An acquisition module, used for acquiring at least two images, each of which contains at least one common road sign;

[0023] A recognition module, used to perform road sign recognition and road sign corner point recognition on each image acquired by the acquisition module, to obtain the position information of the road sign in at least two of the images, and the pixel coordinates of the road sign corner points in at least two of the images;

[0024] A determination module, configured to cluster all the identified road sign corner points according to the position information of the road sign in at least two of the images and the pixel coordinates of the road sign corner points in at least two of the images obtained by the recognition module, and determine the road sign to which each road sign corner point corresponds;

[0025] A generation module is used to generate the geographic coordinates of each road sign according to the pixel coordinates of each road sign corner point corresponding to each road sign determined by the determination module in at least two images, and the geographic location information of the camera when taking the at least two images.

[0026] A third aspect of the present application provides an electronic device, including:

[0027] Processor; and

[0028] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0029] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0030] The technical solution provided by this application may have the following beneficial effects:

[0031] The method provided by the present application obtains at least two images, and performs road sign recognition and road sign corner point recognition on each image respectively, so as to obtain the location information of the road signs in at least two images respectively, and the pixel coordinates of the road sign corner points in at least two images respectively, and then clusters all the road sign corner points obtained by recognition, determines the road sign to which each road sign corner point corresponds, and generates the geographic coordinates of each road sign according to the pixel coordinates of each road sign corner point corresponding to each road sign in at least two images respectively, and the geographic location information of the camera when taking at least two images. In this way, the automatic generation of road signs is realized, and the rapid generation of at least one road sign is realized, so that multiple road signs can be generated at the same time, which effectively improves the efficiency of drawing high-precision maps.

[0032] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0034] Figure 1 It is a flow chart of a method for generating a traffic sign according to an embodiment of the present application;

[0035] Figure 2 It is a structural schematic diagram of a traffic sign generating device shown in an embodiment of the present application;

[0036] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0038] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0039] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this 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 application, 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. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0040] In the related art, the images obtained by using the vehicle's camera cannot fully and automatically generate the road signs contained in the images. The process usually requires human intervention, which in turn affects the efficiency of high-precision map drawing.

[0041] In response to the above problems, an embodiment of the present application provides a method for generating traffic signs, which can automatically generate road signs and improve the efficiency of drawing high-precision maps.

[0042] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0043] Figure 1 It is a flow chart of a method for generating a traffic sign shown in an embodiment of the present application.

[0044] See also Figure 1 , the method comprising:

[0045] Step S101: Acquire at least two images, each of which contains at least one common road sign.

[0046] In the embodiment of the present application, the video data during driving can be collected by a camera device, wherein the camera device may include but is not limited to a driving recorder, a camera or a driver's mobile phone installed on the vehicle and other devices with a camera function. Among them, the camera device can be a monocular camera device. The camera device can be set at the front of the vehicle to record the road sign in front of the vehicle, so as to obtain a continuous video image containing the road sign, so as to obtain at least two images in this step. In order to process the image later, it is necessary to extract frames from the video data containing the road sign obtained during the vehicle driving. Usually, the frame rate of the video is 30 frames per second, and the video can be extracted according to a preset rule, for example, 10 frames, 15 frames, 20 frames or other values ​​are extracted per second, and the time interval between two adjacent frames of the image is the frame extraction time interval, so as to obtain multiple images taken. In addition, the camera device will record the shooting time of the image while shooting the image. In the embodiment of the present application, the camera device for collecting images is regarded as a camera.

[0047] In this step, two images may be acquired, or more than two images may be acquired. Each image may contain at least one common road sign. The common road sign is a road sign that exists in each image. For example, a road sign A in the real world is captured by a camera and imaged in each of at least two images, then the common road sign contained in each image may be road sign A.

[0048] Further, in one embodiment, each image may include at least two common road signs. For example, for road signs A and B in the real world, both road signs A and B are captured by the camera and imaged in each image, and the common road signs included in each image are road signs A and B. For another example, for road signs A, B, and C in the real world, both road signs A, B, and C are captured by the camera and imaged in each image, and the common road signs included in each image are road signs A, B, and C.

[0049] Step S102: Perform road sign recognition and road sign corner point recognition on each image to obtain location information of the road sign in at least two images and pixel coordinates of the road sign corner points in at least two images.

[0050] In one embodiment, a preset target detection model can be used to perform road sign recognition and road sign corner point recognition on each image, thereby obtaining the position information of the road sign in at least two images and the pixel coordinates of the road sign corner points in at least two images.

[0051] The preset target detection model may be a YOLO target detection model, a Faster R-CNN model, or an SSD model, etc. Preferably, the preset target detection model is a YOLO target detection model. In this embodiment, the preset target detection model is described as a YOLO target detection model.

[0052] In one embodiment, the preset target detection model is a preset target detection model after training; the training method of the preset target detection model includes training the preset target detection model using a preset training data set, wherein the preset training data set includes a plurality of training images in which road signs and road sign corner points have been annotated. For example, if the training image contains two road signs, namely road sign A and road sign B, then the center point of road sign A in the training image can be annotated and a bounding box of road sign A can be drawn. Similarly, the center point of road sign B in the training image can be annotated and a bounding box of road sign B can be drawn. At the same time, the positions of each road sign corner point in road sign A and road sign B in the training image are also annotated. In this way, using the preset target detection model after training to perform road sign recognition and road sign corner point recognition on each image can make the recognition results of road signs and road sign corner points more accurate.

[0053] Among them, the position information of the road sign in at least two images is the result obtained by performing road sign recognition on each image respectively. The position information of the road sign in at least two images respectively may include: the pixel coordinates of the center point of the road sign in at least two images respectively. That is to say, after detection and recognition by the preset target detection model, the pixel coordinates of the center point of the road sign in at least two images respectively can be obtained, that is, the position (pixel coordinates) of the center point of the road sign in each image. For example, each of the at least two images contains road sign A and road sign B, then after performing road sign recognition, the pixel coordinates of the center point of road sign A in each image and the pixel coordinates of the center point of road sign B can be obtained. Furthermore, the position information of the road sign in at least two images respectively may also include: the width and height of the enclosing box corresponding to the center point of the road sign in at least two images respectively. The enclosing box is a rectangular box that encloses the road sign in the image, and the center point of the enclosing box is the center point of the road sign in the image.

[0054] The road sign corner points may be the turning points or corner points on the road sign. For example, for a quadrilateral road sign, the road sign corner points may be the four turning points (or vertices) of the quadrilateral road sign; for a triangular road sign, the road sign corner points may be the three turning points (or vertices) of the triangular road sign; for a circular road sign, the road sign corner points may be any corner points (or edge points) on the edge of the circular road sign. The pixel coordinates of the road sign corner points in at least two images are the results obtained by identifying the road sign corner points in each image. For example, if each of at least two images contains road sign A and road sign B, then after road sign corner point recognition, the pixel coordinates of each corner point in road sign A in each image can be obtained (for example, if road sign A is a triangular road sign, the pixel coordinates of the three corner points a1, a2, and a3 of the triangular road sign A can be obtained; for another example, if road sign A is a rectangular road sign, the pixel coordinates of the four corner points a1, a2, a3, and a4 of the rectangular road sign A can be obtained) and the pixel coordinates of each corner point in road sign B.

[0055] In another embodiment, the first target detection model can be used to identify road signs in each image, and the second target detection model can be used to identify road sign corners in each image, so as to obtain the position information of the road signs in at least two images, and the pixel coordinates of the road sign corners in at least two images. In other words, the position information of the road signs in at least two images is obtained based on the detection results of the first target detection model, and the pixel coordinates of the road sign corners in at least two images are obtained based on the detection results of the second target detection model. Among them, the first target detection model and the second target detection model can be the same or different target detection models. For example, the first target detection model and the second target detection model are both YOLO target detection models; for another example, the first target detection model is the YOLO target detection model, and the second target detection model is the Faster R-CNN model.

[0056] Step S103: cluster all identified road sign corner points according to the position information of the road sign in at least two images and the pixel coordinates of the road sign corner points in at least two images, and determine the road sign to which each road sign corner point corresponds.

[0057] In one of the optional implementations, all identified road sign corner points can be clustered using a preset clustering algorithm based on the position information of the road sign in at least two images and the pixel coordinates of the road sign corner points in at least two images to determine the road sign to which each road sign corner point corresponds.

[0058] The preset clustering algorithm may be a clustering algorithm such as K-Means clustering. All identified road sign corner points may be clustered according to the pixel coordinates of the center points of each road sign in the image to identify which road sign corner points belong to the same road sign and determine the road sign to which each road sign corner point belongs.

[0059] For example, the pixel coordinates of all road sign corner points obtained by identifying an image include: pixel coordinates of a1, a2, a3, a4, b1, b2, b3, and b4. Further, based on the pixel coordinates of the center point of road sign A and the pixel coordinates of the center point of road sign B in the road sign recognition result of this image, it is possible to divide the road sign corner points a1, a2, a3, and a4 into road sign A, and the road sign corner points b1, b2, b3, and b4 into road sign B.

[0060] Step S104: Generate the geographic coordinates of each road sign according to the pixel coordinates of each road sign corner point corresponding to each road sign in the at least two images and the geographic location information of the camera when taking the at least two images.

[0061] The road sign corner points corresponding to each road sign may include at least three road sign corner points corresponding to each road sign, for example, four road sign corner points on a quadrilateral road sign, and three road sign corner points on a triangular road sign.

[0062] In one embodiment, at least two images are two images. Based on the two images, the rotation matrix and the translation matrix between the two images can be calculated. Based on the pixel coordinates of each road sign corner point corresponding to each road sign in the two images, and the calculated rotation matrix and translation matrix between the two images, the spatial coordinates of each road sign corner point corresponding to each road sign relative to the camera can be calculated. Using the spatial coordinates of each road sign corner point corresponding to each road sign relative to the camera and the geographical location information of the camera when taking the two images, the geographical coordinates of each road sign can be generated, that is, the geographical coordinates of the road sign corner point of each road sign are generated, so as to realize the generation and production of the road sign. It can be understood that the spatial coordinates of each road sign corner point corresponding to each road sign relative to the camera can be calculated more accurately through multiple images. In this step, the calculation method of the rotation matrix and the translation matrix, the calculation method of the spatial coordinates of each road sign corner point relative to the camera, and the calculation method of the geographical coordinates of the road sign corner point can refer to the introduction in the relevant technology, and this application will not repeat it.

[0063] It should be noted that in this embodiment, the geographic location information of the vehicle or camera can be collected by a positioning device configured by a vehicle or mobile phone, wherein the positioning device can be implemented by existing devices such as GPS (Global Positioning System), Beidou, RTK (Real Time Kinematic), etc., and this application is not limited. The geographic location information of the vehicle (or camera) may include but is not limited to the geographic coordinates (such as GPS coordinates, longitude and latitude coordinates, etc.), orientation, heading angle, direction and other information of the vehicle (or camera). The method provided in the embodiment of the present application can be applied to the vehicle computer, and can also be applied to other devices with computing and processing functions, such as computers, computers, mobile phones, etc. Here, taking the vehicle computer as an example, the camera and the positioning device can be built into the vehicle computer, or can be set outside the vehicle computer, and a communication connection is established with the vehicle computer. While the camera is shooting an image, the positioning device collects the geographic location information of the vehicle or camera and transmits it to the vehicle computer together. According to the shooting time of the image, the geographic location information obtained by the positioning device at the same time can be found. It can be understood that the time of the camera and the positioning device can be synchronized in advance, and the purpose is to make the captured image accurately correspond to the position of the vehicle or camera at that time.

[0064] It can be seen from this embodiment that the method provided in the present application realizes the automatic generation of road signs and the rapid generation of at least one road sign, so that multiple road signs can be generated at the same time, effectively improving the efficiency of drawing high-precision maps.

[0065] Corresponding to the aforementioned application function implementation method embodiment, the present application also provides a traffic sign generation device, an electronic device and corresponding embodiments.

[0066] Figure 2 It is a structural schematic diagram of a traffic sign generating device shown in an embodiment of the present application.

[0067] See also Figure 2 A device for generating a traffic sign includes: an acquisition module 201, an identification module 202, a determination module 203, and a generation module 204.

[0068] The acquisition module 201 is used to acquire at least two images, each of which contains at least one common road sign.

[0069] The recognition module 202 is used to perform road sign recognition and road sign corner point recognition on each image acquired by the acquisition module 201, and obtain the position information of the road sign in at least two images and the pixel coordinates of the road sign corner points in at least two images.

[0070] The determination module 203 is used to cluster all the identified road sign corner points according to the position information of the road sign in at least two images and the pixel coordinates of the road sign corner points in at least two images obtained by the recognition module 202, and determine the road sign to which each road sign corner point corresponds.

[0071] The generating module 204 is used to generate the geographic coordinates of each road sign according to the pixel coordinates of each road sign corner point corresponding to each road sign determined by the determining module 203 in at least two images, and the geographic location information of the camera when taking the at least two images.

[0072] It can be seen from this embodiment that the device provided in the present application realizes the automatic generation of road signs and the rapid generation of at least one road sign, so that multiple road signs can be generated at the same time, effectively improving the efficiency of drawing high-precision maps.

[0073] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0074] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application.

[0075] See also Figure 3, the electronic device 300 includes a memory 310 and a processor 320.

[0076] The processor 320 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0077] The memory 310 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 320 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 310 may include any combination of computer-readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 310 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0078] The memory 310 stores executable codes, and when the executable codes are processed by the processor 320 , the processor 320 can execute part or all of the methods described above.

[0079] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0080] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by a processor of an electronic device (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0081] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for generating a traffic sign, characterized in that: include: Acquire at least two images, each of the images containing at least one common road sign; Performing road sign recognition and road sign corner point recognition on each of the images respectively, to obtain the position information of the road sign in at least two of the images respectively, and the pixel coordinates of the road sign corner points in at least two of the images respectively; Clustering all the identified road sign corner points according to the position information of the road sign in at least two of the images and the pixel coordinates of the road sign corner points in at least two of the images to determine the road sign to which each road sign corner point corresponds, including: clustering all the identified road sign corner points according to the pixel coordinates of the center point of each road sign in the image using a preset clustering algorithm according to the position information of the road sign in at least two of the images and the pixel coordinates of the road sign corner points in at least two of the images to determine the road sign to which each road sign corner point corresponds; The geographic coordinates of each road sign are generated according to the pixel coordinates of each road sign corner point corresponding to each road sign in the at least two images, and the geographic location information of the camera when the at least two images are taken.

2. The method according to claim 1, characterized in that Each of the images contains at least one common road sign, including: Each of the images contains at least two common road signs.

3. The method according to claim 1, characterized in that The performing road sign recognition and road sign corner point recognition on each of the images respectively includes: The preset target detection model is used to perform road sign recognition and road sign corner point recognition on each of the images.

4. The method according to claim 3, characterized in that: The preset target detection model is a preset target detection model after training is completed; the training method of the preset target detection model includes training the preset target detection model using a preset training data set, wherein the preset training data set includes multiple training images with road signs and road sign corner points marked.

5. The method according to claim 3, characterized in that: The preset target detection model is the YOLO target detection model.

6. The method according to claim 1, characterized in that The location information of the road sign in at least two of the images respectively includes: The center point of the road sign is respectively at pixel coordinates in at least two of the images.

7. A device for generating a traffic sign, characterized in that: include: An acquisition module, used for acquiring at least two images, each of which contains at least one common road sign; A recognition module, used to perform road sign recognition and road sign corner point recognition on each image acquired by the acquisition module, to obtain the position information of the road sign in at least two of the images, and the pixel coordinates of the road sign corner points in at least two of the images; A determination module, for clustering all the identified road sign corner points according to the position information of the road sign in at least two images and the pixel coordinates of the road sign corner points in at least two images obtained by the recognition module, and determining the road sign to which each road sign corner point corresponds, comprising: clustering all the identified road sign corner points according to the pixel coordinates of the center point of each road sign in the image using a preset clustering algorithm according to the position information of the road sign in at least two images and the pixel coordinates of the road sign corner points in at least two images, and determining the road sign to which each road sign corner point corresponds; A generation module is used to generate the geographic coordinates of each road sign according to the pixel coordinates of each road sign corner point corresponding to each road sign determined by the determination module in at least two images, and the geographic location information of the camera when taking the at least two images.

8. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: An executable code is stored thereon, and when the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as claimed in any one of claims 1 to 6.

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