CN113963360A - License plate recognition method and device, electronic equipment and readable storage medium - Google Patents
License plate recognition method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The disclosure provides a license plate recognition method and device, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as cloud service, image processing and deep learning. The license plate recognition method comprises the following steps: acquiring an image to be detected to obtain a license plate image in the image to be detected; obtaining a target image in the license plate image according to the height value of the license plate image; obtaining a first recognition result according to the target image, and obtaining a second recognition result according to the license plate image; and combining the first recognition result and the second recognition result, and taking the combined result as the license plate recognition result of the image to be detected. The method and the device can improve the accuracy and the robustness of license plate recognition.
Description
Technical Field
The present disclosure relates to the field of data processing technology, and in particular, to the field of artificial intelligence technologies such as cloud services, image processing, and deep learning. A license plate recognition method, a license plate recognition device, an electronic device and a readable storage medium are provided.
Background
In the prior art, when license plate recognition is carried out, the license plate recognition is easily influenced by factors such as illumination, weather, stains, vehicle speed and the like, so that the accuracy of the license plate recognition is low, and the robustness of the license plate recognition is poor.
Disclosure of Invention
According to a first aspect of the present disclosure, there is provided a license plate recognition method, including: acquiring an image to be detected to obtain a license plate image in the image to be detected; obtaining a target image in the license plate image according to the height value of the license plate image; obtaining a first recognition result according to the target image, and obtaining a second recognition result according to the license plate image; and combining the first recognition result and the second recognition result, and taking the combined result as the license plate recognition result of the image to be detected.
According to a second aspect of the present disclosure, there is provided a license plate recognition device including: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring an image to be detected to obtain a license plate image in the image to be detected; the processing unit is used for obtaining a target image in the license plate image according to the height value of the license plate image; the recognition unit is used for obtaining a first recognition result according to the target image and obtaining a second recognition result according to the license plate image; and the combination unit is used for combining the first recognition result and the second recognition result and taking the combined result as the license plate recognition result of the image to be detected.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.
According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described above.
According to the technical scheme, the license plate recognition is split into two recognition processes, so that the accuracy and the robustness of the license plate recognition can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;
FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure;
FIG. 3 is a schematic diagram according to a third embodiment of the present disclosure;
FIG. 4 is a schematic diagram according to a fourth embodiment of the present disclosure;
FIG. 5 is a schematic diagram according to a fifth embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing a license plate recognition method according to an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure. As shown in fig. 1, the license plate recognition method of the embodiment may specifically include the following steps:
s101, obtaining an image to be detected to obtain a license plate image in the image to be detected;
s102, obtaining a target image in the license plate image according to the height value of the license plate image;
s103, obtaining a first recognition result according to the target image, and obtaining a second recognition result according to the license plate image;
and S104, combining the first recognition result and the second recognition result, and taking the combined result as the license plate recognition result of the image to be detected.
According to the license plate recognition method, after the license plate image in the image to be detected is obtained, the target image in the license plate image is obtained according to the height value of the license plate image, then the first recognition result and the second recognition result are obtained according to the target image and the license plate image respectively, finally the combination result of the first recognition result and the second recognition result is used as the license plate recognition result of the image to be detected, and the accuracy and the robustness of license plate recognition can be improved by splitting the license plate recognition into two recognition processes.
In this embodiment, when S101 is executed to acquire an image to be detected, an image input by a user may be used as the image to be detected, or an image captured in real time may be used as the image to be detected.
In this embodiment, when the license plate image in the image to be detected is obtained by executing S101, a known license plate positioning method may be used to obtain the license plate image by intercepting from the image to be detected according to the positioning result after the license plate in the image to be detected is positioned. It can be understood that the license plate in the embodiment is a vehicle license plate containing Chinese characters (corresponding to provinces), letters and numbers.
In the embodiment, after the license plate image in the image to be detected is obtained by executing the step S101, the step S102 is executed to obtain the target image in the license plate image according to the height value of the license plate image. The target image obtained in the embodiment is an image at least containing Chinese characters in the license plate.
Specifically, in this embodiment, when S102 is executed to obtain the target image in the license plate image according to the height value of the license plate image, an optional implementation manner that can be adopted is as follows: determining the height value of the license plate image; according to a preset position, an image corresponding to the determined height value is intercepted from the license plate image; and taking the intercepted image as a target image.
In order to avoid the influence of the posture of the license plate image and improve the accuracy of the obtained target image, in this embodiment, when the step S102 is executed to determine the height value of the license plate image, the optional implementation manner that may be adopted is: correcting the obtained license plate image; and determining the height value of the license plate image according to the correction result of the license plate image.
In this embodiment, when the step S102 is executed to intercept an image corresponding to the determined height value from the license plate image according to the preset position, the image corresponding to the determined height value may be intercepted from the license plate image by using the upper left corner of the license plate image as an origin; and taking the left boundary of the license plate image as an initial position, and intercepting an image corresponding to the determined height value from the license plate image.
In this embodiment, when the image corresponding to the determined height value is cut out from the license plate image in S102, an image with a size of (height value × height value) may be cut out from the license plate image.
That is to say, in the embodiment, the target image is obtained according to the height value and the preset position of the license plate image, so that the obtained target image includes the license plate Chinese characters, and it is ensured that the first recognition result corresponding to the license plate Chinese characters can be obtained according to the target image.
In this embodiment, after the target image in the license plate image is obtained in S102, S103 is performed to obtain a first recognition result according to the target image, and obtain a second recognition result according to the license plate image.
In this embodiment, the first recognition result obtained by executing S103 is a Chinese character in the license plate, and the second recognition result is a letter and a number in the license plate except the Chinese character.
In this embodiment, when S103 is executed to obtain the first recognition result according to the target image, the optional implementation manner that may be adopted is: and inputting the target image into a first recognition model obtained by pre-training, and taking an output result of the first recognition model as a first recognition result.
In this embodiment, the first recognition model obtained by pre-training can output the chinese characters in the image according to the input image, where the output chinese characters correspond to different provinces.
In addition, when S103 is executed to obtain the first recognition result according to the target image, the present embodiment may further match the target image with different chinese character images, and use the chinese character corresponding to the chinese character image with the highest matching degree as the first recognition result.
In this embodiment, when S103 is executed to obtain the second recognition result according to the license plate image, the optional implementation manner that may be adopted is: and inputting the license plate image into a second recognition model obtained by pre-training, and taking an output result of the second recognition model as a second recognition result.
In this embodiment, the second recognition model trained in advance can output letters and numbers except for the chinese characters in the image according to the input image.
In this embodiment, after the first recognition result and the second recognition result are obtained by executing S103, the obtained first recognition result and the obtained second recognition result are combined by executing S104, and the combined result is used as the license plate recognition result of the image to be detected.
In the embodiment, when the step S104 is executed to combine the first recognition result and the second recognition result, the combination may be performed according to a preset sequence, where the preset sequence is the first recognition result and the second recognition result.
According to the method provided by the embodiment, the license plate recognition is divided into two recognition processes in a mode that the target image and the license plate image obtained from the license plate image are respectively obtained as the first recognition result and the second recognition result, so that the accuracy and the robustness of the license plate recognition can be improved.
Fig. 2 is a schematic diagram according to a second embodiment of the present disclosure. As shown in fig. 2, the first recognition model is obtained by pre-training in the following manner in the present embodiment:
s201, obtaining a first training set, wherein the first training set comprises a plurality of first license plate images and Chinese character labeling results of the first license plate images;
s202, respectively obtaining target images in the first license plate images according to the height values of the first license plate images;
s203, training the neural network model by using the target images in the first images and the Chinese character labeling results of the first images to obtain the first recognition model.
In the first training set obtained in step S201, a plurality of first license plate images are license plate images corresponding to different provinces, and a chinese character labeling result of the first license plate image is a chinese character of the province to which the license plate belongs.
In this embodiment, when S202 is executed to obtain the target images in the plurality of first license plate images according to the height values of the plurality of first license plate images, the optional implementation manner that may be adopted is: for each first license plate image, intercepting an image corresponding to the height value from the first license plate image according to a preset position; and taking the intercepted image as a target image. The preset position in this embodiment may be the upper left corner of the first license plate image, or the left boundary of the first license plate image.
In this embodiment, when performing S203 to train the neural network model by using the target images in the plurality of first images and the chinese character labeling results of the plurality of first images to obtain the first recognition model, an optional implementation manner that can be adopted is as follows: respectively inputting target images in the first license plate images into a neural network model to obtain a Chinese character prediction result output by the neural network model aiming at each first license plate image; and adjusting parameters of the neural network model according to the loss function values obtained by calculating the Chinese character prediction results and the Chinese character marking results of the plurality of first license plate images until the neural network model converges to obtain a first recognition model.
According to the method and the device, the first recognition model can be obtained through training only by acquiring the first license plate images corresponding to different provinces, and the Chinese characters in the license plate images can be extracted more accurately by using the first recognition model.
Fig. 3 is a schematic diagram according to a third embodiment of the present disclosure. As shown in fig. 3, the second recognition model is obtained by pre-training in the following manner in the present embodiment:
s301, obtaining a second training set, wherein the second training set comprises a plurality of second license plate images and non-Chinese character labeling results of the plurality of second license plate images;
s302, training the neural network model by using non-Chinese character labeling results of the plurality of second images and the plurality of first images to obtain the second recognition model.
In the second training set obtained in step S301, the plurality of second license plate images may be license plate images of any province, and the non-chinese character labeling result of the second license plate image is letters and numbers in the license plate except for chinese characters.
In this embodiment, when S302 is executed to train the neural network model by using the plurality of second images and the non-chinese character labeling results of the plurality of second images to obtain the second recognition model, an optional implementation manner that can be adopted is as follows: respectively inputting the second license plate images into the neural network model to obtain a non-Chinese character prediction result output by the neural network model aiming at each second license plate image; and adjusting parameters of the neural network model according to the loss function values obtained by calculating the non-Chinese character prediction results and the non-Chinese character marking results of the second license plate images until the neural network model converges to obtain a second recognition model.
According to the embodiment, the second identification model can be obtained through training only by acquiring the second license plate image of any province, and letters and numbers in the license plate image can be extracted more accurately by using the second identification model.
Through the first recognition model and the second recognition model, the method for separately recognizing the Chinese characters of the license plate and the non-Chinese characters (letters and numbers) of the license plate can reduce the acquisition cost of training data, and improves the accuracy of license plate recognition on the basis of not acquiring a large number of license plate images of different provinces.
Fig. 4 is a schematic diagram according to a fourth embodiment of the present disclosure. Fig. 4 shows a flowchart of license plate recognition performed in the present embodiment: obtaining a target image from the license plate image; a in the license plate image represents a Chinese character corresponding to a province in the license plate, Y in the license plate image represents a letter in the license plate, and X in the license plate image represents a number in the license plate; inputting a target image into a first recognition model, and inputting a license plate image into a second recognition model; and combining the first recognition result 'A' output by the first recognition model with the second recognition result 'YXXXYX' output by the second recognition model, and taking the combined result 'AYXXXYX' as a license plate recognition result.
Fig. 5 is a schematic diagram according to a fifth embodiment of the present disclosure. As shown in fig. 5, the license plate recognition device 500 of the present embodiment includes:
the acquiring unit 501 is configured to acquire an image to be detected to obtain a license plate image in the image to be detected;
the processing unit 502 is configured to obtain a target image in the license plate image according to the height value of the license plate image;
the recognition unit 503 is configured to obtain a first recognition result according to the target image, and obtain a second recognition result according to the license plate image;
the combining unit 504 is configured to combine the first recognition result and the second recognition result, and use the combined result as a license plate recognition result of the image to be detected.
When acquiring an image to be detected, the acquiring unit 501 may use an image input by a user as the image to be detected, or may use an image shot in real time as the image to be detected.
When obtaining the license plate image in the image to be detected, the obtaining unit 501 may use a known license plate positioning method to obtain the license plate image by intercepting from the image to be detected according to the positioning result after positioning the license plate in the image to be detected. It can be understood that the license plate in the embodiment is a vehicle license plate containing Chinese characters (corresponding to provinces), letters and numbers.
In this embodiment, after the license plate image in the image to be detected is obtained by the obtaining unit 501, the processing unit 502 obtains the target image in the license plate image according to the height value of the license plate image. The target image obtained by the processing unit 502 is an image at least containing Chinese characters in the license plate.
Specifically, when the processing unit 502 obtains the target image in the license plate image according to the height value of the license plate image, the optional implementation manner that can be adopted is as follows: determining the height value of the license plate image; according to a preset position, an image corresponding to the determined height value is intercepted from the license plate image; and taking the intercepted image as a target image.
In order to avoid the influence of the posture of the license plate image and improve the accuracy of the obtained target image, when the processing unit 502 determines the height value of the license plate image, the optional implementation manner that can be adopted is as follows: correcting the license plate image; and determining the height value of the license plate image according to the correction result of the license plate image.
When the processing unit 502 intercepts the image corresponding to the determined height value from the license plate image according to the preset position, the processing unit may intercept the image corresponding to the determined height value from the license plate image by using the upper left corner of the license plate image as an origin; and taking the left boundary of the license plate image as an initial position, and intercepting an image corresponding to the determined height value from the license plate image.
When the image corresponding to the determined height value is cut out from the license plate image, the processing unit 502 may cut out an image having a size of (height value × height value) from the license plate image.
That is to say, the processing unit 502 obtains the target image according to the height value and the preset position of the license plate image, so that the obtained target image contains the license plate Chinese characters, and it is ensured that the first recognition result corresponding to the license plate Chinese characters can be obtained according to the target image.
In this embodiment, after the processing unit 502 obtains the target image in the license plate image, the recognition unit 503 obtains a first recognition result according to the target image, and obtains a second recognition result according to the license plate image.
The first recognition result obtained by the recognition unit 503 is a Chinese character in the license plate, and the second recognition result is a letter and a number in the license plate except the Chinese character.
When obtaining the first recognition result according to the target image, the recognition unit 503 may adopt the following optional implementation manners: and inputting the target image into a first recognition model obtained by pre-training, and taking an output result of the first recognition model as a first recognition result.
The first recognition model used by the recognition unit 503 can output the chinese characters in the image according to the input image, and the output chinese characters correspond to different provinces.
In addition, when obtaining the first recognition result from the target image, the recognition unit 503 may match the target image with different chinese character images, and may use a chinese character corresponding to the chinese character image with the highest matching degree as the first recognition result.
When obtaining the second recognition result according to the license plate image, the recognition unit 503 may adopt an optional implementation manner as follows: and inputting the license plate image into a second recognition model obtained by pre-training, and taking an output result of the second recognition model as a second recognition result.
The second recognition model used by the recognition unit 503 can output letters and numbers other than chinese characters in the input image according to the input image.
In this embodiment, after the first recognition result and the second recognition result are obtained by the recognition unit 503, the obtained first recognition result and the second recognition result are combined by the combination unit 504, and the combination result is used as the license plate recognition result of the image to be detected.
The combining unit 504 may combine the first recognition result and the second recognition result in a preset order, where the preset order is the first recognition result and the second recognition result.
The license plate recognition device 500 of the present embodiment may further include a first training unit 505, configured to obtain a first recognition model through pre-training in the following manner: acquiring a first training set, wherein the first training set comprises a plurality of first license plate images and Chinese character labeling results of the first license plate images; respectively obtaining target images in the first license plate images according to the height values of the first license plate images; and training the neural network model by using the target images in the first images and the Chinese character labeling results of the first images to obtain a first recognition model.
The first training unit 505 obtains a plurality of first license plate images included in the first training set, where the first license plate images correspond to license plate images of different provinces, and a Chinese character labeling result of the first license plate image is a Chinese character of the province to which the license plate belongs.
When the first training unit 505 obtains the target images of the plurality of first license plate images according to the height values of the plurality of first license plate images, the following optional implementation manners may be adopted: for each first license plate image, intercepting an image corresponding to the height value from the first license plate image according to a preset position; and taking the intercepted image as a target image. The preset position in this embodiment may be the upper left corner of the first license plate image, or the left boundary of the first license plate image.
When the first training unit 505 trains the neural network model by using the target images of the plurality of first images and the chinese character labeling results in the plurality of first images to obtain the first recognition model, the optional implementation manners that can be adopted are: respectively inputting target images in the first license plate images into a neural network model to obtain a Chinese character prediction result output by the neural network model aiming at each first license plate image; and adjusting parameters of the neural network model according to the loss function values obtained by calculating the Chinese character prediction results and the Chinese character marking results of the plurality of first license plate images until the neural network model converges to obtain a first recognition model.
The first training unit 505 can train to obtain the first recognition model by only acquiring the first license plate image corresponding to different provinces, and the first recognition model can be used for more accurately extracting the Chinese characters in the license plate image.
The license plate recognition device 500 of the embodiment may further include a second training unit 506, configured to pre-train to obtain a second recognition model in the following manner: acquiring a second training set, wherein the second training set comprises a plurality of second license plate images and non-Chinese character labeling results of the plurality of second license plate images; and training the neural network model by using the non-Chinese character labeling results of the plurality of second images and the plurality of first images to obtain a second recognition model.
The plurality of second license plate images included in the second training set acquired by the second training unit 506 may be license plate images of any province, and the non-chinese character labeling result of the second license plate image is letters and numbers except chinese characters in the license plate.
When the second training unit 506 trains the neural network model by using the plurality of second images and the letter and character labeling results of the plurality of second images to obtain the second recognition model, the optional implementation manner that can be adopted is as follows: respectively inputting the second license plate images into the neural network model to obtain a non-Chinese character prediction result output by the neural network model aiming at each second license plate image; and adjusting parameters of the neural network model according to the loss function values obtained by calculating the non-Chinese character prediction results and the non-Chinese character marking results of the second license plate images until the neural network model converges to obtain a second identification model.
The second training unit 506 can train to obtain the second recognition model by only acquiring the second license plate image of any province, and the second recognition model can be used for more accurately extracting letters and numbers in the license plate image.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
Fig. 6 is a block diagram of an electronic device according to a license plate recognition method of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as the license plate recognition method. For example, in some embodiments, the license plate recognition method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608.
In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM602 and/or the communication unit 609. When the computer program is loaded into the RAM603 and executed by the computing unit 601, one or more steps of the license plate recognition method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the license plate recognition method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable license plate recognition device such that the program codes, when executed by the processor or controller, cause the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (15)
1. A license plate recognition method includes:
acquiring an image to be detected to obtain a license plate image in the image to be detected;
obtaining a target image in the license plate image according to the height value of the license plate image;
obtaining a first recognition result according to the target image, and obtaining a second recognition result according to the license plate image;
and combining the first recognition result and the second recognition result, and taking the combined result as the license plate recognition result of the image to be detected.
2. The method of claim 1, wherein the obtaining of the target image in the license plate image according to the height value of the license plate image comprises:
determining a height value of the license plate image;
according to a preset position, an image corresponding to the height value is intercepted from the license plate image;
and taking the intercepted image as the target image.
3. The method of claim 1, wherein said deriving a first recognition result from the target image comprises:
and inputting the target image into a first recognition model obtained by pre-training, and taking an output result of the first recognition model as the first recognition result.
4. The method of claim 1, wherein the obtaining a second recognition result according to the license plate image comprises:
and inputting the license plate image into a second recognition model obtained by pre-training, and taking an output result of the second recognition model as the second recognition result.
5. The method of claim 3, wherein the first recognition model is pre-trained by:
acquiring a first training set, wherein the first training set comprises a plurality of first license plate images and Chinese character labeling results of the first license plate images;
respectively obtaining target images in the first license plate images according to the height values of the first license plate images;
and training the neural network model by using the target images in the first images and the Chinese character labeling results of the first images to obtain the first recognition model.
6. The method of claim 4, wherein the second recognition model is pre-trained by:
acquiring a second training set, wherein the second training set comprises a plurality of second license plate images and non-Chinese character labeling results of the plurality of second license plate images;
and training the neural network model by using the non-Chinese character labeling results of the plurality of second images and the plurality of first images to obtain the second recognition model.
7. A license plate recognition device comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring an image to be detected to obtain a license plate image in the image to be detected;
the processing unit is used for obtaining a target image in the license plate image according to the height value of the license plate image;
the recognition unit is used for obtaining a first recognition result according to the target image and obtaining a second recognition result according to the license plate image;
and the combination unit is used for combining the first recognition result and the second recognition result and taking the combined result as the license plate recognition result of the image to be detected.
8. The apparatus according to claim 7, wherein the processing unit, when obtaining the target image in the license plate image according to the height value of the license plate image, specifically performs:
determining a height value of the license plate image;
according to a preset position, an image corresponding to the height value is intercepted from the license plate image;
and taking the intercepted image as the target image.
9. The apparatus according to claim 7, wherein the recognition unit, when obtaining the first recognition result from the target image, specifically performs:
and inputting the target image into a first recognition model obtained by pre-training, and taking an output result of the first recognition model as the first recognition result.
10. The apparatus according to claim 7, wherein the recognition unit, when obtaining a second recognition result from the license plate image, specifically performs:
and inputting the license plate image into a second recognition model obtained by pre-training, and taking an output result of the second recognition model as the second recognition result.
11. The apparatus according to claim 9, further comprising a first training unit for pre-training the first recognition model by:
acquiring a first training set, wherein the first training set comprises a plurality of first license plate images and Chinese character labeling results of the first license plate images;
respectively obtaining target images in the first license plate images according to the height values of the first license plate images;
and training the neural network model by using the target images in the first images and the Chinese character labeling results of the first images to obtain the first recognition model.
12. The apparatus according to claim 10, further comprising a second training unit for pre-training the second recognition model by:
acquiring a second training set, wherein the second training set comprises a plurality of second license plate images and non-Chinese character labeling results of the plurality of second license plate images;
and training the neural network model by using the non-Chinese character labeling results of the plurality of second images and the plurality of first images to obtain the second recognition model.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111054674.6A CN113963360A (en) | 2021-09-09 | 2021-09-09 | License plate recognition method and device, electronic equipment and readable storage medium |
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| CN202111054674.6A CN113963360A (en) | 2021-09-09 | 2021-09-09 | License plate recognition method and device, electronic equipment and readable storage medium |
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