Method and system for automatically adjusting license plate recognition angle, medium, product and terminal

Abstract

The invention provides a method and system for automatically adjusting a license plate recognition angle, a medium, a product and a terminal, and the method comprises the steps: judging whether to call a pre-constructed license plate recognition angle model or not according to the license plate angle data of a to-be-recognized vehicle and the light angle data, and carrying out the angle adjustment of an image collection device according to a judgment result. Through establishing a model among the license plate angle, the light ray angle and the optimal identification angle of the image acquisition equipment, and based on the license plate angle and the light ray angle detected in real time, the shooting angle of the image acquisition equipment can be automatically adjusted. The dynamic adjustment of the shooting angle of the image acquisition equipment is realized, and clear license plate images can be captured under various illumination conditions, so that complex environments such as different illumination conditions and different license plate angles can be dealt with, the adaptability of the vehicle identification system to different environments is improved, and the vehicle identification efficiency is improved. The reliability, stability and accuracy of license plate recognition are improved.

Description

Technical Field

[0001] The present application relates to the field of license plate recognition technology, and in particular to a method, system, medium, product and terminal for automatically adjusting a license plate recognition angle. Background Art

[0002] License plate recognition technology, as a core component of intelligent transportation systems, has received widespread attention and application in recent years. Especially in scenarios such as parking lot management and toll booth collection, the static license plate recognition system has greatly improved the efficiency and accuracy of traffic management with its high efficiency and automation. However, the current static license plate recognition system still has many limitations in technical implementation and application scenarios. These limitations are mainly reflected in the following aspects:

[0003] First, static license plate recognition systems mainly rely on fixed cameras for high-resolution image capture. Although this fixed installation method ensures the stability and clarity of the image to a certain extent, it also brings many problems. Due to the fixed angle and position of the camera, when facing different lighting conditions (such as strong sunlight, backlight, etc.), the brightness, contrast and other parameters of the license plate image will change significantly, thus affecting the accuracy of license plate recognition. Although existing visual algorithms have been optimized and adjusted a lot, under the constraints of extreme physical conditions, these algorithms often cannot completely overcome the recognition difficulties caused by lighting changes.

[0004] Secondly, the existing license plate recognition equipment is usually designed with only the basic installation angle of the camera in mind, but fails to fully consider the specific vehicle position and changes in light angle. This results in that in actual applications, when a vehicle approaches at different angles or directions, the license plate image may be deformed or blurred due to changes in viewing angle, thus affecting the accuracy of recognition. In particular, when a vehicle approaches at an angle, the effective recognition rate of the license plate often drops significantly.

[0005] In addition, existing systems also face severe challenges when dealing with backlit and low-light conditions. In backlit conditions, the light in the license plate area is often drowned out by strong light, resulting in the inability to effectively extract the license plate information; while in low-light conditions, the license plate image may become blurred due to insufficient light, which also affects the accuracy of recognition. These problems not only reduce the success rate of license plate recognition, but also limit the application of static license plate recognition systems in more complex scenarios. Summary of the invention

[0006] In view of the shortcomings of the prior art mentioned above, the purpose of the present application is to provide a method, system, medium, product and terminal for automatically adjusting the license plate recognition angle, so as to solve the problems existing in the existing license plate recognition technology, such as inaccurate license plate recognition due to failure to fully consider changes in vehicle position and light angle, and limited recognition performance.

[0007] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides a method for automatically adjusting the license plate recognition angle, which is applied to a computer device, wherein the computer device is communicatively connected to an image acquisition device, and the method includes: obtaining real-time position image data and real-time lighting condition data of a vehicle to be identified; calculating the license plate angle data of the vehicle to be identified based on the real-time position image data of the vehicle to be identified; calculating the light angle data of the vehicle to be identified based on the real-time lighting condition data of the vehicle to be identified; judging whether to call a pre-built license plate recognition angle model based on the license plate angle data and the light angle data of the vehicle to be identified, and adjusting the angle of the image acquisition device based on the judgment result so that the image acquisition device is at the best viewing angle for recognizing the license plate.

[0008] In some embodiments of the first aspect of the present application, the method for determining whether to call a pre-built license plate recognition angle model includes: determining whether the license plate angle data of the vehicle to be identified is within a first preset threshold range, and whether the light angle data of the vehicle to be identified is within a second preset threshold range; if the license plate angle data of the vehicle to be identified is within the first preset threshold range, and the light angle data of the vehicle to be identified is within the second preset threshold range, the pre-built license plate recognition angle model is not called; if the license plate angle data of the vehicle to be identified is not within the first preset threshold range, or the light angle data of the vehicle to be identified is not within the second preset threshold range, the pre-built license plate recognition angle model is called.

[0009] In some embodiments of the first aspect of the present application, the method of adjusting the angle of the image acquisition device according to the judgment result so that the image acquisition device is in the optimal viewing angle for recognizing license plates includes: when calling a pre-built license plate recognition angle model, based on the license plate recognition angle model, and according to the license plate angle data of the vehicle to be identified and the light angle data, calculating the optimal recognition angle data of the image acquisition device; and adjusting the angle of the image acquisition device according to the optimal recognition angle data of the image acquisition device so that the image acquisition device is in the optimal viewing angle for recognizing license plates; when not calling the pre-built license plate recognition angle model, adjusting the angle of the image acquisition device according to the preset recognition angle data so that the image acquisition device is in the optimal viewing angle for recognizing license plates.

[0010] In some embodiments of the first aspect of the present application, the computer device is also communicatively connected to a pan-tilt device, and the pan-tilt device is connected to the image acquisition device; wherein, the angle of the image acquisition device is adjusted by controlling the pan-tilt device.

[0011] In some embodiments of the first aspect of the present application, the method for constructing the license plate recognition angle model includes: collecting a historical vehicle image data set; the historical vehicle image data set includes historical license plate angle data, historical light angle data and historical recognition angle data corresponding to the image acquisition device of different vehicles; annotating the data in the historical vehicle image data set to obtain a license plate recognition angle training data set; using the license plate recognition angle training data set to train a neural network model to obtain a license plate recognition angle model.

[0012] In some embodiments of the first aspect of the present application, the method also includes: when the image acquisition device is at the best license plate recognition viewing angle, causing the image acquisition device to capture the license plate image of the vehicle to be identified, and obtaining the license plate of the vehicle to be identified from the license plate image transmitted back by the image acquisition device.

[0013] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides a system for automatically adjusting the license plate recognition angle, comprising: a computer device; an image acquisition device, wherein the image acquisition device is used to acquire images of a vehicle to be identified; the computer device is communicatively connected to the image acquisition device; wherein the computer device obtains real-time position image data and real-time lighting condition data of the vehicle to be identified; based on the real-time position image data of the vehicle to be identified, the license plate angle data of the vehicle to be identified is calculated; based on the real-time lighting condition data of the vehicle to be identified, the light angle data of the vehicle to be identified is calculated; based on the license plate angle data of the vehicle to be identified and the light angle data thereof, it is determined whether to call a pre-built license plate recognition angle model, and the image acquisition device is adjusted in angle according to the determination result, so that the image acquisition device is at the best viewing angle for recognizing the license plate.

[0014] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for automatically adjusting the license plate recognition angle as described above.

[0015] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer program product, which includes a computer program code. When the computer program code is run on a computer, the computer implements the method of automatically adjusting the license plate recognition angle as described above.

[0016] To achieve the above-mentioned purpose and other related purposes, the fifth aspect of the present application provides an electronic terminal, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program to implement the method of automatically adjusting the license plate recognition angle as described above.

[0017] As described above, the method, system, medium, product and terminal for automatically adjusting the license plate recognition angle of the present application have the following features:

[0018] Beneficial effects:

[0019] According to the license plate angle data of the vehicle to be identified and the light angle data, it is determined whether to call the pre-built license plate recognition angle model, and the angle of the image acquisition device is adjusted according to the determination result so that the image acquisition device is in the best license plate recognition viewing angle. By establishing a model between the license plate angle, the light angle and the best recognition angle of the image acquisition device, and based on the license plate angle and light angle detected in real time, the shooting angle of the image acquisition device can be automatically adjusted, thereby realizing dynamic adjustment of the shooting angle of the image acquisition device, ensuring that clear license plate images can be captured under various lighting conditions, so that it can cope with complex environments such as different lighting conditions and different license plate angles, thereby improving the adaptability of the vehicle recognition system to different environments, avoiding problems such as license plate recognition failure or recognition error due to changes in the viewing angle of the license plate image, and improving the reliability, stability and accuracy of license plate recognition. In addition, the present application can identify changes in license plate angles and light angles, and automatically adjust the shooting angle of the image acquisition device, reducing the time and effort required for manual adjustment of the shooting angle, avoiding cumbersome procedures, and making the installation and maintenance of the equipment simpler. Operators no longer need to frequently perform manual calibration of the image acquisition device, thereby improving work efficiency and the response speed of license plate recognition.

[0020] Furthermore, the present application provides a dynamically adjusted license plate recognition solution based on the relationship between the license plate angle, light angle and recognition angle of the image acquisition device, so that the license plate recognition system can not only intelligently respond to different lighting and vehicle conditions, but also meet the effective recognition needs under different road and weather conditions. The adaptability is significantly enhanced, and the application scenarios of the system are expanded. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Shown is a scene schematic diagram of a method for automatically adjusting the license plate recognition angle in one embodiment of the present application.

[0022] Figure 2 Shown is a schematic diagram of the process of automatically adjusting the license plate recognition angle in one embodiment of the present application.

[0023] Figure 3Shown is a schematic diagram of a process for adjusting the recognition angle of an image acquisition device in one embodiment of the present application.

[0024] Figure 4 Shown is a schematic block diagram of a system for automatically adjusting the license plate recognition angle in an embodiment of the present application.

[0025] Figure 5 Shown is a schematic diagram of the structure of an electronic terminal in one embodiment of the present application. DETAILED DESCRIPTION

[0026] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0027] Existing license plate recognition technology mainly uses static license plate recognition technology, which is limited by fixed cameras and is difficult to adapt to different lighting conditions (such as strong sunlight, backlight, etc.), resulting in changes in parameters such as brightness and contrast of the license plate image, affecting the recognition accuracy. The design of license plate recognition equipment does not fully consider the changes in vehicle position and light angle, resulting in deformation and blurring of license plate images when vehicles approach at different angles, reducing recognition accuracy. In backlight conditions, license plate information is submerged by strong light; in low light conditions, license plate images are blurred, which seriously affects recognition accuracy and limits the application of the system in complex scenarios.

[0028] In order to solve the problems in the above-mentioned background technology, the present invention provides a method, system, medium, product and terminal for automatically adjusting the license plate recognition angle, aiming to solve the problems existing in the existing license plate recognition technology, such as inaccurate license plate recognition caused by failure to fully consider changes in vehicle position and light angle, and limited recognition performance.

[0029] To facilitate understanding of the embodiments of the present application, first Figure 1 Detailed description. Figure 1 A schematic diagram of a scenario of the method for automatically adjusting the license plate recognition angle in an embodiment of the present invention is shown. The scenario applicable to the method for automatically adjusting the license plate recognition angle described in this application includes: a computer device 401, an image acquisition device 402, a vehicle to be recognized, and a light source. The image acquisition device 402 is used to acquire an image of the vehicle to be recognized; the computer device 401 is communicatively connected to the image acquisition device 402.

[0030] The computer device 401 obtains the real-time position image data and real-time illumination condition data of the vehicle to be identified; calculates the license plate angle data of the vehicle to be identified based on the real-time position image data of the vehicle to be identified; calculates the light angle data of the vehicle to be identified based on the real-time illumination condition data of the vehicle to be identified; determines whether to call the pre-built license plate recognition angle model based on the license plate angle data and the light angle data of the vehicle to be identified, and adjusts the angle of the image acquisition device 402 based on the judgment result, so that the image acquisition device 402 is in the best license plate recognition viewing angle, so that the shooting angle of the image acquisition device 402 can be automatically adjusted, and the dynamic adjustment of the shooting angle of the image acquisition device 402 is realized, ensuring that clear license plate images can be captured under various illumination conditions. And at the best license plate recognition viewing angle, the image acquisition device 402 is ordered to perform license plate image acquisition on the vehicle to be identified, and obtain the license plate of the vehicle to be identified from the license plate image transmitted back by the image acquisition device 402.

[0031] The following will further explain the technical solution of the present application in detail in combination with specific method embodiments, system embodiments, and equipment embodiments.

[0032] like Figure 2 As shown, a flow chart of the method for automatically adjusting the license plate recognition angle in an embodiment of the present invention is shown. In this embodiment, the method for automatically adjusting the license plate recognition angle is applied to a computer device, and the computer device is communicatively connected to an image acquisition device. The method for automatically adjusting the license plate recognition angle mainly includes the following steps:

[0033] S201: Acquire real-time position image data and real-time lighting condition data of the vehicle to be identified.

[0034] In this embodiment, the computer device is also communicatively connected to a light sensor; wherein, the method of obtaining the real-time position image data and real-time lighting condition data of the vehicle to be identified includes: when it is monitored that the vehicle to be identified is in the detection position, the image acquisition device is used to acquire the real-time position image data of the vehicle to be identified, and the light sensor is used to acquire the real-time lighting condition data of the vehicle to be identified; the real-time position image data of the vehicle to be identified is acquired from the image acquisition device, and the real-time lighting condition data of the vehicle to be identified is acquired from the light sensor.

[0035] In this embodiment, the light sensor detects the light intensity of the surrounding environment and outputs a corresponding electrical signal according to the change of the light intensity.

[0036] In this embodiment, the image acquisition device includes a camera module; the camera module includes a camera device, a storage device and a processing device; the camera device includes but is not limited to: a camera, a webcam, a video camera, a camera module integrated with an optical system or a CCD chip, a camera module integrated with an optical system and a CMOS chip, etc.

[0037] S202: Calculate license plate angle data of the vehicle to be identified based on the real-time position image data of the vehicle to be identified.

[0038] In this embodiment, the license plate angle data of the vehicle to be identified refers to the angle data of the license plate of the vehicle to be identified relative to the image acquisition device.

[0039] (1) Preprocessing the acquired real-time position image data of the vehicle to be identified, including grayscale conversion, denoising, contrast enhancement, etc., to improve image quality and reduce the difficulty of subsequent processing.

[0040] (2) Based on the edge detection algorithm, the license plate area image is obtained from the real-time position image data of the vehicle to be identified after preprocessing.

[0041] In this embodiment, the edge detection algorithm is a key technology in the field of computer vision and image processing, which aims to identify areas in an image where features such as grayscale value, color or texture change significantly, i.e., edges. These edges usually correspond to the outline of an object, a change in shape, or the boundary between different objects. In an image, edge areas are often accompanied by a sharp change in grayscale value (or color value). Therefore, the algorithm detects this change by calculating the grayscale difference between each pixel in the image and its neighboring pixels. When this difference exceeds a preset threshold, the pixel is considered an edge point.

[0042] In this embodiment, the pre-processed real-time position image data of the vehicle to be identified is processed using an edge detection algorithm. The edge detection algorithm can identify the area where the gray value in the real-time position image data of the vehicle to be identified changes significantly, that is, the edge, so as to locate the outline of the license plate. In the edge detection result, by analyzing the shape, position and other characteristics of the edge, the approximate position of the license plate area can be preliminarily determined, such as the size, shape, relative position of the license plate in the vehicle, etc. Finally, according to the located license plate area, the area is cropped from the original image to obtain a license plate area image.

[0043] (3) Extracting license plate feature points in the license plate area image. The license plate feature points may be the four corners of the license plate, the intersection of the edges, or other significant features.

[0044] (4) Match the extracted license plate feature points with the feature points in the known license plate template or standard license plate image.

[0045] (5) Based on the matching results of the license plate feature points, calculate the angle data of the license plate of the vehicle to be identified relative to the image acquisition device.

[0046] In this embodiment, the relative position relationship between the characteristic points of the license plate is calculated, and a geometric transformation (such as an affine transformation algorithm) is used to solve the angle data of the license plate of the vehicle to be identified relative to the image acquisition device.

[0047] In this embodiment, the affine transformation algorithm refers to a process in geometry where a vector space is transformed into another vector space by a linear transformation followed by a translation. The affine transformation algorithm is often used to correct tilted license plate area images. By calculating the relative position relationship between the feature points of the license plate, the tilt angle of the license plate can be determined. Once the tilt angle of the license plate is determined, an affine transformation matrix can be constructed, which describes how to rotate the tilted license plate image to the correct position. Then, the affine transformation matrix is ​​applied to transform the license plate area image to obtain a corrected license plate area image. In this process, although the affine transformation matrix is ​​directly solved, since the affine transformation algorithm includes a rotation operation, the angle data of the license plate of the vehicle to be identified relative to the image acquisition device can be indirectly obtained through the affine transformation matrix.

[0048] S203: Calculate the light angle data of the vehicle to be identified based on the real-time lighting condition data of the vehicle to be identified.

[0049] In this embodiment, an illumination model is established based on the real-time illumination condition data of the vehicle to be identified collected by the light sensor. The illumination model describes the distribution and changes of light in three-dimensional space. Using the illumination model, combined with the real-time illumination condition data of the vehicle to be identified collected by the light sensor, the incident angle of the light relative to the vehicle to be identified is calculated. By analyzing the real-time position image data of the vehicle to be identified collected by the image acquisition device, the direction of the light can be identified. For example, an image processing algorithm (such as an edge detection algorithm) is used to extract light features in the real-time position image data, and calculate the direction angle of the light relative to the vehicle to be identified. The incident angle and the direction angle are comprehensively calculated to obtain the complete angle data of the light relative to the vehicle to be identified, that is, the light angle data where the vehicle to be identified is located.

[0050] S204: Determine whether to call a pre-built license plate recognition angle model based on the license plate angle data of the vehicle to be identified and the light angle data, and adjust the angle of the image acquisition device based on the determination result so that the image acquisition device is at the best viewing angle for identifying the license plate.

[0051] In this embodiment, if Figure 3FIG. 2 is a flow chart showing a method of adjusting the recognition angle of an image acquisition device in an embodiment of the present invention. The method of adjusting the recognition angle of an image acquisition device includes two parts: (1) determining whether to call a license plate recognition angle model. (2) adjusting the angle of the image acquisition device according to the determination result.

[0052] In this embodiment, the method of determining whether to call the pre-built license plate recognition angle model includes:

[0053] S2041: Determine whether the license plate angle data of the vehicle to be identified is within a first preset threshold range, and whether the light angle data of the vehicle to be identified is within a second preset threshold range.

[0054] S2042: If the license plate angle data of the vehicle to be identified is within the first preset threshold range, and the light angle data of the vehicle to be identified is within the second preset threshold range, the pre-built license plate recognition angle model is not called, and step S2045 is executed.

[0055] S2043: If the license plate angle data of the vehicle to be identified is not within the first preset threshold range, or the light angle data of the vehicle to be identified is not within the second preset threshold range, a pre-built license plate recognition angle model is called to execute step S2044.

[0056] In this embodiment, the method of adjusting the angle of the image acquisition device according to the judgment result so that the image acquisition device is at the best viewing angle for recognizing the license plate includes:

[0057] S2044: when calling the pre-built license plate recognition angle model, based on the license plate recognition angle model and according to the license plate angle data of the vehicle to be recognized and the angle data of the light, calculate the optimal recognition angle data of the image acquisition device; and according to the optimal recognition angle data of the image acquisition device, adjust the angle of the image acquisition device so that the image acquisition device is at the best license plate recognition viewing angle;

[0058] S2045: When the pre-built license plate recognition angle model is not called, the angle of the image acquisition device is adjusted according to the preset recognition angle data so that the image acquisition device is at the best viewing angle for recognizing the license plate.

[0059] In this embodiment, the first preset threshold range is used to determine whether the license plate angle is within an acceptable range to ensure that the license plate of the vehicle to be identified is at a better recognition angle relative to the image acquisition device, so that the image acquisition device can obtain a clear license plate image and enhance the license plate recognition rate. The second preset threshold range is used to determine whether the light conditions of the vehicle to be identified are suitable for license plate recognition.

[0060] In this embodiment, if the license plate angle data of the vehicle to be identified is within the first preset threshold range, and the light angle data of the vehicle to be identified is within the second preset threshold range, this means that the current license plate angle and light conditions are both suitable, and the license plate recognition can be performed directly. In this case, there is no need to call the pre-built license plate recognition angle model to obtain the recognition angle of the image acquisition device, because the existing angle is good enough, and the preset recognition angle data can be directly used to fine-tune the image acquisition device to ensure the best recognition effect.

[0061] In this embodiment, if the license plate angle data of the vehicle to be identified is not within the first preset threshold range, it means that the current license plate angle is not conducive to license plate recognition. If the light angle data of the vehicle to be identified is not within the second preset threshold range, it means that the current light conditions are not suitable for license plate recognition. When any of the above conditions is not met, it is necessary to call a pre-built license plate recognition angle model to calculate the optimal recognition angle data of the image acquisition device based on the license plate angle data of the vehicle to be identified and the light angle data. The optimal recognition angle data represents the angle to which the image acquisition device should be adjusted to ensure that it captures the license plate image at the optimal viewing angle. Based on the calculated optimal recognition angle data, the computer device adjusts the angle of the image acquisition device by controlling the pan-tilt device so that the image acquisition device is in the optimal license plate recognition viewing angle, so that the image acquisition device can capture the best license plate image and enhance the vehicle recognition rate.

[0062] In this embodiment, the computer device is also communicatively connected to a pan-tilt device, and the pan-tilt device is connected to the image acquisition device; wherein, the angle of the image acquisition device is adjusted by controlling the pan-tilt device.

[0063] In this embodiment, the pan-tilt device is a mechanical device that can control the image acquisition device (such as a camera) to rotate in the horizontal and vertical directions. The pan-tilt device can accurately adjust the angle of the image acquisition device by receiving instructions from the computer device to ensure that the image acquisition device captures the license plate image with the best viewing angle.

[0064] In this embodiment, the method of constructing the license plate recognition angle model includes:

[0065] (1) Collecting a historical vehicle image dataset; the historical vehicle image dataset includes historical license plate angle data, historical light angle data, and historical recognition angle data corresponding to the image acquisition device of different vehicles.

[0066] (2) Annotating the data in the historical vehicle image dataset to obtain a license plate recognition angle training dataset.

[0067] (3) Using the license plate recognition angle training data set to train the neural network model to obtain a license plate recognition angle model.

[0068] In this embodiment, historical image data including different vehicles, different license plate angles, different light conditions, and corresponding recognition angles of the image acquisition device are collected. The historical vehicle image data set includes: historical license plate angle data (angle data of the license plate relative to the image acquisition device), light angle data (angle data of light relative to the vehicle), and historical recognition angle data (angle data of the image acquisition device when capturing the license plate image).

[0069] In this embodiment, each vehicle image data is annotated, including vehicle type, lighting conditions, and optimal image acquisition device shooting angle, etc., to obtain a license plate recognition angle training data set. The annotated license plate recognition angle training data set is input into the neural network model to enable it to learn the optimal relationship between vehicle position, lighting conditions, and image acquisition device shooting angle. During the training process, the validation data set is used to evaluate the performance of the model, and after the training is completed, the test data set is used for final evaluation to obtain a converged license plate recognition angle model. The license plate recognition angle model can calculate the optimal recognition angle data of the image acquisition device based on the license plate angle data and the light angle data.

[0070] In this embodiment, the neural network model includes: a convolutional neural network model. The convolutional neural network model is a very important network structure in deep learning, and is particularly suitable for processing image data. Through structures such as convolutional layers, pooling layers, and fully connected layers, it can automatically learn feature representations in images, such as edges, textures, shapes, etc. In the license plate recognition angle model, the convolutional neural network model can learn the angle characteristics and light characteristics of the license plate to predict the optimal recognition angle. The convolutional neural network model is only one of the embodiments in this application, which is mainly used for explanation and description, rather than for limiting the scope of protection of this application.

[0071] In this embodiment, the method also includes: when the image acquisition device is at the best license plate recognition viewing angle, causing the image acquisition device to capture the license plate image of the vehicle to be identified, and obtaining the license plate of the vehicle to be identified from the license plate image transmitted back by the image acquisition device.

[0072] In this embodiment, after the computer device controls the pan-tilt device to adjust the angle of the image acquisition device, the image acquisition device is at the best viewing angle for identifying the license plate. At this time, the computer device sends an image acquisition instruction to the image acquisition device to enable the image acquisition device to perform the license plate image acquisition task. The computer device receives the license plate image from the image acquisition device to extract the license plate information of the vehicle to be identified.

[0073] It is worth noting that the method for automatically adjusting the license plate recognition angle of the present application determines whether to call a pre-built license plate recognition angle model based on the license plate angle data of the vehicle to be identified and the light angle data, and adjusts the angle of the image acquisition device based on the judgment result so that the image acquisition device is in the best license plate recognition viewing angle. By establishing a model between the license plate angle, light angle and the best recognition angle of the image acquisition device, and based on the real-time detected license plate angle and light angle, the shooting angle of the image acquisition device can be automatically adjusted, thereby realizing dynamic adjustment of the shooting angle of the image acquisition device, ensuring that clear license plate images can be captured under various lighting conditions, so that it can cope with complex environments such as different lighting conditions and different license plate angles, thereby improving the adaptability of the vehicle recognition system to different environments, avoiding problems such as license plate recognition failure or recognition error due to changes in the viewing angle of the license plate image, and improving the reliability, stability and accuracy of license plate recognition. In addition, the present application can identify changes in license plate angles and light angles, and automatically adjust the shooting angle of the image acquisition device, reducing the time and effort required for manual adjustment of the shooting angle, avoiding cumbersome procedures, and making the installation and maintenance of the equipment simpler. Operators no longer need to frequently perform manual calibration of the image acquisition device, thereby improving work efficiency and the response speed of license plate recognition.

[0074] Furthermore, the present application provides a dynamically adjusted license plate recognition solution based on the relationship between the license plate angle, light angle and recognition angle of the image acquisition device, so that the license plate recognition system can not only intelligently respond to different lighting and vehicle conditions, but also meet the effective recognition needs under different road and weather conditions. The adaptability is significantly enhanced, and the application scenarios of the system are expanded.

[0075] In the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially identical functions and effects. For example, the first preset threshold range and the second preset threshold range are only used to distinguish between different preset threshold ranges, and do not limit their order. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0076] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0077] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0078] Figure 4 is a schematic block diagram of a system for automatically adjusting license plate recognition angle provided in an embodiment of the present application. Figure 4 As shown, the automatic license plate recognition angle adjustment system 400 includes:

[0079] Computer device 401.

[0080] The image acquisition device 402 is used to acquire images of the vehicle to be identified; the computer device 401 is communicatively connected to the image acquisition device 402 .

[0081] Among them, the computer device 401 obtains the real-time position image data and real-time lighting condition data of the vehicle to be identified; calculates the license plate angle data of the vehicle to be identified based on the real-time position image data of the vehicle to be identified; calculates the light angle data of the vehicle to be identified based on the real-time lighting condition data of the vehicle to be identified; determines whether to call a pre-built license plate recognition angle model based on the license plate angle data of the vehicle to be identified and the light angle data, and adjusts the angle of the image acquisition device 402 based on the determination result, so that the image acquisition device 402 is at the best license plate recognition viewing angle.

[0082] In this embodiment, the automatic license plate recognition angle adjustment system 400 further includes a light sensor 403 that is communicatively connected to the computer device 401 , and the light sensor 403 is used to collect real-time lighting condition data of the vehicle to be recognized.

[0083] In this embodiment, the automatic adjustment license plate recognition angle system 400 also includes a pan-tilt device 404 that is communicatively connected to the computer device 401, and the pan-tilt device 404 is connected to the image acquisition device 402; wherein, by controlling the pan-tilt device 404, the angle of the image acquisition device 402 is adjusted.

[0084] It should be understood that the specific process of each module executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0085] It should also be understood that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0086] Figure 5 is a schematic block diagram of an electronic terminal provided in an embodiment of the present application. The electronic terminal includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the above-mentioned method for automatically adjusting the license plate recognition angle. Figure 5 As shown, the electronic terminal 500 includes: at least one processor 501, a memory 502, at least one network interface 503 and a user interface 505. The various components in the device are coupled together through a bus system 504. It can be understood that the bus system 504 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 504 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 5 In the specification, various buses are labeled as bus systems.

[0087] The user interface 505 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0088] It is understood that the memory 502 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0089] The memory 502 in the embodiment of the present invention is used to store various types of data to support the operation of the electronic terminal 500. Examples of these data include: any executable program used to operate on the electronic terminal 500, such as an operating system 5021 and an application 5022; the operating system 5021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 5022 can include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The method for automatically adjusting the license plate recognition angle provided in the embodiment of the present invention can be included in the application 5022.

[0090] The method disclosed in the above embodiment of the present invention can be applied to the processor 501, or implemented by the processor 501. The processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 501 or the instruction in the form of software. The above processor 501 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 501 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor 501 may be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0091] In an exemplary embodiment, the electronic terminal 500 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0092] According to the method provided in the embodiment of the present application, the present application also provides a computer program product, which includes: a computer program code, when the computer program code is run on a computer, the computer executes Figures 2 to 3 A method according to any one of the embodiments shown.

[0093] According to the method provided in the embodiment of the present application, the present application also provides a computer-readable storage medium, which stores a program code, and when the program code is run on a computer, the computer executes Figures 2 to 3 A method according to any one of the embodiments shown.

[0094] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0095] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0096] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0097] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0100] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When loading and executing computer program instructions (programs) on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integrations. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs), etc.).

[0101] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[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 should be based on the protection scope of the claims.

[0103] In summary, the present application provides a method, system, medium, product and terminal for automatically adjusting the license plate recognition angle. According to the license plate angle data of the vehicle to be identified and the light angle data, it is determined whether to call a pre-built license plate recognition angle model, and the angle of the image acquisition device is adjusted according to the judgment result so that the image acquisition device is in the best license plate recognition viewing angle. By establishing a model between the license plate angle, the light angle and the best recognition angle of the image acquisition device, and based on the license plate angle and light angle detected in real time, the shooting angle of the image acquisition device can be automatically adjusted, thereby realizing dynamic adjustment of the shooting angle of the image acquisition device, ensuring that clear license plate images can be captured under various lighting conditions, so that it can cope with complex environments such as different lighting conditions and different license plate angles, improving the adaptability of the vehicle recognition system to different environments, avoiding problems such as license plate recognition failure or recognition error due to changes in the viewing angle of the license plate image, and improving the reliability, stability and accuracy of license plate recognition. In addition, the present application can identify changes in license plate angles and light angles, and automatically adjust the shooting angle of the image acquisition device, reducing the time and effort required for manual adjustment of the shooting angle, avoiding cumbersome procedures, making the installation and maintenance of the equipment simpler, and operators no longer need to frequently perform manual calibration of the image acquisition device, thereby improving operating efficiency and the response speed of license plate recognition. Therefore, the present application effectively overcomes various shortcomings in the prior art and has a high industrial utilization value.

[0104] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for automatically adjusting the license plate recognition angle, characterized in that: Applied to a computer device, the computer device is communicatively connected to an image acquisition device, and the method comprises: Obtaining real-time position image data and real-time lighting condition data of the vehicle to be identified; Calculating license plate angle data of the vehicle to be identified based on the real-time position image data of the vehicle to be identified; Calculating the light angle data of the vehicle to be identified based on the real-time lighting condition data of the vehicle to be identified; According to the license plate angle data of the vehicle to be identified and the light angle data, it is determined whether to call a pre-built license plate recognition angle model, and the angle of the image acquisition device is adjusted according to the determination result so that the image acquisition device is at the best license plate recognition viewing angle.

2. The method for automatically adjusting the license plate recognition angle according to claim 1, characterized in that: Methods for determining whether to call the pre-built license plate recognition angle model include: Determine whether the license plate angle data of the vehicle to be identified is within a first preset threshold range, and whether the light angle data of the vehicle to be identified is within a second preset threshold range; If the license plate angle data of the vehicle to be identified is within the first preset threshold range, and the light angle data of the vehicle to be identified is within the second preset threshold range, the pre-built license plate recognition angle model is not called; If the license plate angle data of the vehicle to be identified is not within the first preset threshold range, or the light angle data of the vehicle to be identified is not within the second preset threshold range, a pre-built license plate recognition angle model is called.

3. The method for automatically adjusting the license plate recognition angle according to claim 1, characterized in that: The method of adjusting the angle of the image acquisition device according to the judgment result so that the image acquisition device is at the best viewing angle for recognizing the license plate includes: When calling a pre-built license plate recognition angle model, based on the license plate recognition angle model and according to the license plate angle data of the vehicle to be recognized and the light angle data, the optimal recognition angle data of the image acquisition device is calculated; and according to the optimal recognition angle data of the image acquisition device, the angle of the image acquisition device is adjusted so that the image acquisition device is at the best license plate recognition viewing angle; When the pre-built license plate recognition angle model is not called, the angle of the image acquisition device is adjusted according to the preset recognition angle data so that the image acquisition device is at the best viewing angle for recognizing the license plate.

4. The method for automatically adjusting the license plate recognition angle according to claim 1, characterized in that: The computer device is also communicatively connected to a pan-tilt device, and the pan-tilt device is connected to the image acquisition device; wherein, the angle of the image acquisition device is adjusted by controlling the pan-tilt device.

5. The method for automatically adjusting the license plate recognition angle according to claim 1, characterized in that: The method of constructing the license plate recognition angle model includes: Collecting a historical vehicle image data set; the historical vehicle image data set includes historical license plate angle data, historical light angle data of different vehicles, and historical recognition angle data corresponding to the image acquisition device; Annotating the data in the historical vehicle image dataset to obtain a license plate recognition angle training dataset; The license plate recognition angle training data set is used to train a neural network model to obtain a license plate recognition angle model.

6. The method for automatically adjusting the license plate recognition angle according to claim 1, characterized in that: The method also includes: when the image acquisition device is at the best license plate recognition viewing angle, causing the image acquisition device to capture the license plate image of the vehicle to be identified, and obtaining the license plate of the vehicle to be identified from the license plate image transmitted back by the image acquisition device.

7. A system for automatically adjusting the license plate recognition angle, characterized in that: include: Computer equipment; An image acquisition device, the image acquisition device is used to acquire an image of a vehicle to be identified; The computer device is communicatively connected to the image acquisition device; Among them, the computer device obtains real-time position image data and real-time lighting condition data of the vehicle to be identified; calculates the license plate angle data of the vehicle to be identified based on the real-time position image data of the vehicle to be identified; calculates the light angle data of the vehicle to be identified based on the real-time lighting condition data of the vehicle to be identified; determines whether to call a pre-built license plate recognition angle model based on the license plate angle data of the vehicle to be identified and the light angle data, and adjusts the angle of the image acquisition device based on the determination result, so that the image acquisition device is at the best viewing angle for recognizing the license plate.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for automatically adjusting the license plate recognition angle according to any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that The computer program product includes computer program codes, and when the computer program codes are executed on a computer, the computer implements the method for automatically adjusting the license plate recognition angle according to any one of claims 1 to 6.

10. An electronic terminal comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method for automatically adjusting the license plate recognition angle according to any one of claims 1 to 6.

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