CN110866524A - License plate detection method, device, equipment and storage medium - Google Patents
License plate detection method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN110866524A CN110866524A CN201911119925.7A CN201911119925A CN110866524A CN 110866524 A CN110866524 A CN 110866524A CN 201911119925 A CN201911119925 A CN 201911119925A CN 110866524 A CN110866524 A CN 110866524A
- Authority
- CN
- China
- Prior art keywords
- license plate
- key point
- information
- image
- recognized
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/63—Scene text, e.g. street names
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/625—License plates
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Multimedia (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the disclosure discloses a license plate detection method, a license plate detection device, license plate detection equipment and a storage medium. The method comprises the following steps: carrying out license plate recognition on the image to be recognized to obtain license plate information; the license plate information comprises license plate position information and a first confidence coefficient; if the first confidence coefficient is larger than a first set threshold value, license plate key point recognition is carried out on the area where the license plate is located, and license plate key point information is obtained; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient; and if the second confidence coefficient is greater than a second set threshold value, the image to be recognized comprises a license plate. According to the license plate detection method provided by the embodiment of the disclosure, the license plate detection is firstly carried out on the image to be recognized, and then the key point recognition of the license plate is carried out, so that whether the image to be recognized contains the license plate or not is determined, the false detection rate of the license plate can be reduced, and the accuracy of the license plate detection is improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of license plate detection, in particular to a license plate detection method, a license plate detection device, license plate detection equipment and a storage medium.
Background
At present, whether in various traffic occasions, the field of public safety management or the field of unmanned driving, license plate detection can bring convenience to orderly management of urban health. Although the license plate detection is widely applied, the license plate detection technology still has a plurality of difficulties, and the performances of all aspects have great improvement space.
In the prior art, when a license plate in an image is detected, feature recognition is performed on the image, and if features close to the features of the license plate exist in the image, the license plate is considered to exist in the image. However, in practical situations, there are many objects with similar characteristics to the license plate, and this way makes the false detection rate of the license plate detection very high, affecting the precision of the license plate detection.
Disclosure of Invention
The present disclosure provides a license plate detection method, apparatus, device and storage medium to realize the detection of a license plate in an image, and can improve the accuracy of license plate detection.
In a first aspect, an embodiment of the present disclosure provides a license plate detection method, including:
carrying out license plate recognition on the image to be recognized to obtain license plate information; the license plate information comprises license plate position information and a first confidence coefficient;
if the first confidence coefficient is larger than a first set threshold value, license plate key point recognition is carried out on the area where the license plate is located, and license plate key point information is obtained; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient;
and if the second confidence coefficient is greater than a second set threshold value, the image to be recognized comprises a license plate.
In a second aspect, an embodiment of the present disclosure further provides a license plate detection device, including:
the license plate information acquisition module is used for identifying the license plate of the image to be identified and acquiring license plate information; the license plate information comprises license plate position information and a first confidence coefficient;
the license plate key point information acquisition module is used for identifying license plate key points in the area where the license plate is located to acquire license plate key point information when the first confidence coefficient is greater than a first set threshold value; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient;
and the license plate determining module is used for determining that the image to be recognized contains a license plate if the second confidence coefficient is greater than a second set threshold value.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
one or more processing devices;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processing devices, the one or more processing devices are enabled to implement the license plate detection method according to the embodiment of the disclosure.
In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processing device, implements the license plate detection method according to the embodiment of the present disclosure.
According to the embodiment of the disclosure, firstly, license plate recognition is carried out on an image to be recognized to obtain license plate information; the license plate information comprises license plate position information and a first confidence coefficient; if the first confidence coefficient is larger than a first set threshold value, license plate key point recognition is carried out on the area where the license plate is located, and license plate key point information is obtained; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient; if the second confidence coefficient is larger than a second set threshold value, the image to be recognized comprises a license plate. According to the license plate detection method provided by the embodiment of the disclosure, the license plate detection is firstly carried out on the image to be recognized, and then the key point recognition of the license plate is carried out, so that whether the image to be recognized contains the license plate or not is determined, the false detection rate of the license plate can be reduced, and the accuracy of the license plate detection is improved.
Drawings
Fig. 1 is a flowchart of a license plate detection method in a first embodiment of the disclosure;
fig. 2 is an exemplary diagram of license plate information in a first embodiment of the disclosure;
fig. 3 is a schematic structural diagram of a license plate detection device in a second embodiment of the disclosure;
fig. 4 is a schematic structural diagram of an electronic device in a third embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units. [ ordinal numbers ]
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Example one
Fig. 1 is a flowchart of a license plate detection method according to an embodiment of the present disclosure, where the present embodiment is applicable to a situation of detecting a license plate in an image, and the method may be executed by a license plate detection device, where the device may be composed of hardware and/or software, and may be generally integrated in a device having a license plate detection function, where the device may be an electronic device such as a server, a mobile terminal, or a server cluster. As shown in fig. 1, the method specifically includes the following steps:
and 110, identifying the license plate of the image to be identified to obtain license plate information.
The license plate information comprises license plate position information and a first confidence coefficient. The position information of the license plate can be coordinate information of four vertexes of a region where the license plate is located in the image to be recognized, and the first confidence coefficient can represent the probability that the region is the license plate. For example, fig. 2 is an exemplary diagram of the license plate information in the embodiment, as shown in fig. 2, the confidence of the license plate information detected in the image to be recognized is 0.8, and the coordinates of four vertices of the area where the license plate is located are (x1, y1), (x2, y2), (x3, y3), and (x4, y4), respectively. Specifically, feature recognition is carried out on the image to be recognized, license plate features in the image to be recognized are extracted, and license plate information contained in the image to be recognized is obtained.
Optionally, the license plate recognition is performed on the image to be recognized, and the license plate information is obtained in the following manner: and inputting the image to be recognized into a set license plate recognition model to obtain license plate information.
The set license plate recognition model is obtained through the following steps: acquiring an image marked with license plate information as a positive sample; acquiring an image without labeling license plate information as a negative sample; and training the set neural network according to the positive sample and the negative sample to obtain a set license plate recognition model.
Wherein, the setting neural network can be a convolution neural network or a deep neural network. Specifically, after a positive sample and a negative sample are obtained, the positive sample and the negative sample are input into a set neural network to adjust parameters in the set neural network, so that training of the set neural network is achieved until the set neural network has a function of detecting a license plate, and a set license plate recognition model is obtained.
In the embodiment, the image to be recognized is adjusted to the size meeting the requirements of the license plate recognition model, then the image to be recognized after the size is adjusted is input into the license plate recognition model, and license plate position information and a first confidence coefficient of the license plate contained in the image to be recognized are output.
And 120, if the first confidence coefficient is greater than a first set threshold value, performing license plate key point recognition on the area where the license plate is located to obtain license plate key point information.
The license plate key point information comprises a license plate key point coordinate and a second confidence coefficient. The license plate key point coordinates can comprise coordinates of four vertexes of the license plate, and the second confidence coefficient is used for representing the probability that the region contains the license plate. The first set threshold may be set to any value between 0.8 and 0.9. Specifically, when the first confidence is greater than a first set threshold, license plate key point recognition is performed on the image of the region where the license plate is located, and coordinates of four vertexes of the license plate and a second confidence of the license plate are obtained.
Optionally, the license plate key point identification is performed on the area where the license plate is located, and the license plate key point information is obtained in a manner that: and inputting the region where the license plate is located into a set license plate key point recognition model to obtain license plate key point information.
The license plate key point identification model is set to be obtained through the following steps: acquiring an image for marking key point information of a license plate as a positive sample; acquiring an image without marking the key point information of the license plate as a negative sample; and training the set neural network according to the positive sample and the negative sample to obtain a set license plate recognition model.
Wherein, the setting neural network can be a convolution neural network or a deep neural network. Specifically, after a positive sample and a negative sample are obtained, the positive sample and the negative sample are input into a set neural network to adjust parameters in the set neural network, so that training of the set neural network is achieved until the set neural network has a function of detecting key points of a license plate, and a license plate recognition model is obtained.
In the embodiment, the image of the area where the license plate is located is adjusted to the size meeting the requirements of the license plate key point recognition model, then the image after size adjustment is input into the license plate key point recognition model, and the coordinate information of the license plate key point and the second confidence coefficient of the license plate contained in the image are output.
Optionally, the license plate key point identification is performed on the area where the license plate is located, and the process of obtaining the license plate key point information may be: and expanding the area of the license plate by a set proportion, and identifying the license plate key points in the expanded license plate area to obtain the license plate key point information.
Wherein the set ratio may be any value between 1.5 and 3 times. For example, assuming that the set proportion is 2 times, the rectangular frame of the region where the license plate is located is enlarged to 2 times of the original rectangular frame by taking the center of the region as a reference, and an image captured by the rectangular frame enlarged by 2 times is an image of the region enlarged by the set proportion. Specifically, after the area where the license plate is located is enlarged by a set proportion, the enlarged license plate area is input into a set license plate key point recognition model, and license plate key point information is obtained.
And step 130, if the second confidence is greater than a second set threshold, the image to be recognized comprises a license plate.
Wherein the second set threshold value can be set to any value between 0.9 and 0.95. The second set point may be greater than the first set point. In this embodiment, when the second confidence is greater than the second set threshold, it may be determined that the image to be recognized includes a license plate, and a region surrounded by the key points of the license plate is determined as the license plate.
According to the technical scheme of the embodiment of the invention, license plate recognition is firstly carried out on an image to be recognized to obtain license plate information; the license plate information comprises license plate position information and a first confidence coefficient; if the first confidence coefficient is larger than a first set threshold value, license plate key point recognition is carried out on the area where the license plate is located, and license plate key point information is obtained; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient; if the second confidence coefficient is larger than a second set threshold value, the image to be recognized comprises a license plate. According to the license plate detection method provided by the embodiment of the disclosure, the license plate detection is firstly carried out on the image to be recognized, and then the key point recognition of the license plate is carried out, so that whether the image to be recognized contains the license plate or not is determined, the false detection rate of the license plate can be reduced, and the accuracy of the license plate detection is improved.
Optionally, the license plate recognition is performed on the image to be recognized, and the license plate information is obtained in the following manner: if the image to be recognized is a video frame, judging whether the interval between the current video frame and the video frame for recognizing the license plate information last time is smaller than a set value; and if the number plate information is smaller than the preset number plate information, determining the last recognized number plate information as the number plate information of the current video frame.
The video frame can be a video frame in the monitoring video, and the set value can be any value between 10 and 20. In an application scene, a monitor in the underground garage monitors vehicles entering and leaving and detects license plates, so that the position information of the license plates does not change greatly in a short time even if the vehicles move, and if the license plates are detected for each video frame, the data processing amount is increased, and the resource distribution pressure is increased. In this embodiment, if the license plate in the video is identified, it may be determined whether an interval between a current video frame and a video frame from which the license plate information was identified last time is smaller than a set value; if the number plate information is smaller than the number plate information, the last recognized number plate information is determined as the number plate information of the current video frame, and if the number plate information is larger than the number plate information, the number plate detection is continuously carried out on the current video frame. Optionally, the license plate information identified last time is determined as the license plate information of the current video frame, where the license plate information identified last time is determined as the license plate information of the current video frame after the area where the license plate is located is enlarged by a set proportion. And the license plate detection is carried out on the video frames again at intervals of the set video frame number, so that the technical data volume can be reduced, and the resource loss is low.
Example two
Fig. 3 is a schematic structural diagram of a license plate detection device according to a second embodiment of the disclosure. As shown in fig. 3, the apparatus includes: a license plate information acquisition module 210, a license plate key point information acquisition module 220 and a license plate determination module 230.
The license plate information acquisition module 210 is configured to perform license plate identification on the image to be identified to obtain license plate information; the license plate information comprises license plate position information and a first confidence coefficient;
the license plate key point information obtaining module 220 is configured to, when the first confidence is greater than a first set threshold, perform license plate key point recognition on a region where a license plate is located, and obtain license plate key point information; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient;
the license plate determining module 230 is configured to determine that the image to be recognized includes a license plate if the second confidence is greater than a second set threshold.
Optionally, the license plate information obtaining module 210 is further configured to:
inputting an image to be recognized into a set license plate recognition model to obtain license plate information;
the set license plate recognition model is obtained through the following steps:
acquiring an image marked with license plate information as a positive sample; acquiring an image without labeling license plate information as a negative sample;
and training the set neural network according to the positive sample and the negative sample to obtain a set license plate recognition model.
Optionally, the license plate key point information obtaining module 220 is further configured to:
inputting a license plate key point recognition model in a region where a license plate is located, and acquiring license plate key point information;
the license plate key point identification model is set to be obtained through the following steps:
acquiring an image for marking key point information of a license plate as a positive sample; acquiring an image without marking the key point information of the license plate as a negative sample;
and training the set neural network according to the positive sample and the negative sample to obtain a set license plate recognition model.
Optionally, the license plate key point information obtaining module 220 is further configured to:
and expanding the area of the license plate by a set proportion, and identifying the license plate key points in the expanded license plate area to obtain the license plate key point information.
Optionally, the license plate information obtaining module 210 is further configured to:
if the image to be recognized is a video frame, judging whether the interval between the current video frame and the video frame for recognizing the license plate information last time is smaller than a set value;
and if the number plate information is smaller than the preset number plate information, determining the last recognized number plate information as the number plate information of the current video frame.
Optionally, the license plate key point coordinates include coordinates of four vertices of the license plate.
The device can execute the methods provided by all the embodiments of the disclosure, and has corresponding functional modules and beneficial effects for executing the methods. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in all the foregoing embodiments of the disclosure.
EXAMPLE III
Referring now to FIG. 4, a block diagram of an electronic device 300 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like, or various forms of servers such as a stand-alone server or a server cluster. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 300 may include a processing means (e.g., central processing unit, graphics processor, etc.) 301 that may perform various appropriate actions and processes in accordance with a program stored in a read-only memory device (ROM)302 or a program loaded from a storage device 305 into a random access memory device (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device 300 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 300 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program containing program code for performing a method for recommending words. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 309, or installed from the storage means 305, or installed from the ROM 302. The computer program, when executed by the processing device 301, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: the method comprises the following steps: carrying out license plate recognition on the image to be recognized to obtain license plate information; the license plate information comprises license plate position information and a first confidence coefficient; if the first confidence coefficient is larger than a first set threshold value, license plate key point recognition is carried out on the area where the license plate is located, and license plate key point information is obtained; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient; and if the second confidence coefficient is greater than a second set threshold value, the image to be recognized comprises a license plate.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
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.
According to one or more embodiments of the disclosed embodiments, the disclosed embodiments provide a license plate detection method, including:
carrying out license plate recognition on the image to be recognized to obtain license plate information; the license plate information comprises license plate position information and a first confidence coefficient;
if the first confidence coefficient is larger than a first set threshold value, license plate key point recognition is carried out on the area where the license plate is located, and license plate key point information is obtained; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient;
and if the second confidence coefficient is greater than a second set threshold value, the image to be recognized comprises a license plate.
Further, the license plate recognition is carried out on the image to be recognized, and license plate information is obtained, wherein the license plate recognition comprises the following steps:
inputting an image to be recognized into a set license plate recognition model to obtain license plate information;
the set license plate recognition model is obtained through the following steps:
acquiring an image marked with license plate information as a positive sample; acquiring an image without labeling license plate information as a negative sample;
and training a set neural network according to the positive sample and the negative sample to obtain a set license plate recognition model.
Further, license plate key point identification is carried out on the area where the license plate is located, and license plate key point information is obtained, wherein the license plate key point information comprises the following steps:
inputting a license plate key point recognition model in a region where a license plate is located, and acquiring license plate key point information;
the set license plate key point recognition model is obtained through the following steps:
acquiring an image for marking key point information of a license plate as a positive sample; acquiring an image without marking the key point information of the license plate as a negative sample;
and training a set neural network according to the positive sample and the negative sample to obtain a set license plate recognition model.
Further, license plate key point identification is performed on the area where the license plate is located, and license plate key point information is obtained, wherein the license plate key point information comprises the following steps:
and expanding the area of the license plate by a set proportion, and identifying the license plate key points in the expanded license plate area to obtain the license plate key point information.
Further, the license plate recognition is carried out on the image to be recognized, and license plate information is obtained, wherein the license plate recognition comprises the following steps:
if the image to be recognized is a video frame, judging whether the interval between the current video frame and the video frame for recognizing the license plate information last time is smaller than a set value;
and if the number plate information is smaller than the preset number plate information, determining the last recognized number plate information as the number plate information of the current video frame.
Further, the license plate key point coordinates comprise coordinates of four vertexes of the license plate.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present disclosure and the technical principles employed. Those skilled in the art will appreciate that the present disclosure is not limited to the particular embodiments described herein, and that various obvious changes, adaptations, and substitutions are possible, without departing from the scope of the present disclosure. Therefore, although the present disclosure has been described in greater detail with reference to the above embodiments, the present disclosure is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present disclosure, the scope of which is determined by the scope of the appended claims.
Claims (10)
1. A license plate detection method is characterized by comprising the following steps:
carrying out license plate recognition on the image to be recognized to obtain license plate information; the license plate information comprises license plate position information and a first confidence coefficient;
if the first confidence coefficient is larger than a first set threshold value, license plate key point recognition is carried out on the area where the license plate is located, and license plate key point information is obtained; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient;
and if the second confidence coefficient is greater than a second set threshold value, the image to be recognized comprises a license plate.
2. The method of claim 1, wherein identifying the license plate of the image to be identified to obtain the license plate information comprises:
inputting an image to be recognized into a set license plate recognition model to obtain license plate information;
the set license plate recognition model is obtained through the following steps:
acquiring an image marked with license plate information as a positive sample; acquiring an image without labeling license plate information as a negative sample;
and training a set neural network according to the positive sample and the negative sample to obtain a set license plate recognition model.
3. The method of claim 1, wherein identifying key points of the license plate in the area of the license plate to obtain key point information of the license plate comprises:
inputting a license plate key point recognition model in a region where a license plate is located, and acquiring license plate key point information;
the set license plate key point recognition model is obtained through the following steps:
acquiring an image for marking key point information of a license plate as a positive sample; acquiring an image without marking the key point information of the license plate as a negative sample;
and training a set neural network according to the positive sample and the negative sample to obtain a set license plate recognition model.
4. The method according to claim 1 or 3, wherein the license plate key point recognition is performed on the area where the license plate is located to obtain the license plate key point information, and the method comprises the following steps:
and expanding the area of the license plate by a set proportion, and identifying the license plate key points in the expanded license plate area to obtain the license plate key point information.
5. The method of claim 1, wherein identifying the license plate of the image to be identified to obtain the license plate information comprises:
if the image to be recognized is a video frame, judging whether the interval between the current video frame and the video frame for recognizing the license plate information last time is smaller than a set value;
and if the number plate information is smaller than the preset number plate information, determining the last recognized number plate information as the number plate information of the current video frame.
6. The method of claim 1, wherein the license plate keypoint coordinates comprise coordinates of four license plate vertices.
7. A license plate detection device, comprising:
the license plate information acquisition module is used for identifying the license plate of the image to be identified and acquiring license plate information; the license plate information comprises license plate position information and a first confidence coefficient;
the license plate key point information acquisition module is used for identifying license plate key points in the area where the license plate is located to acquire license plate key point information when the first confidence coefficient is greater than a first set threshold value; the license plate key point information comprises a license plate key point coordinate and a second confidence coefficient;
and the license plate determining module is used for determining that the image to be recognized contains a license plate if the second confidence coefficient is greater than a second set threshold value.
8. The apparatus of claim 7, wherein the license plate key point information obtaining module is further configured to:
and expanding the area of the license plate by a set proportion, and identifying the license plate key points in the expanded license plate area to obtain the license plate key point information.
9. An electronic device, characterized in that the electronic device comprises:
one or more processing devices;
storage means for storing one or more programs;
when executed by the one or more processing devices, cause the one or more processing devices to implement the license plate detection method of any of claims 1-6.
10. A computer-readable medium, on which a computer program is stored, characterized in that the program, when executed by a processing device, implements a license plate detection method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911119925.7A CN110866524A (en) | 2019-11-15 | 2019-11-15 | License plate detection method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911119925.7A CN110866524A (en) | 2019-11-15 | 2019-11-15 | License plate detection method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110866524A true CN110866524A (en) | 2020-03-06 |
Family
ID=69654565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911119925.7A Pending CN110866524A (en) | 2019-11-15 | 2019-11-15 | License plate detection method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110866524A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990197A (en) * | 2021-03-17 | 2021-06-18 | 浙江商汤科技开发有限公司 | License plate recognition method and device, electronic equipment and storage medium |
| WO2022048209A1 (en) * | 2020-09-03 | 2022-03-10 | 平安科技（深圳）有限公司 | License plate recognition method and apparatus, electronic device, and storage medium |
| CN114399766A (en) * | 2022-01-18 | 2022-04-26 | 平安科技（深圳）有限公司 | Optical character recognition model training method, device, equipment and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104318225A (en) * | 2014-11-19 | 2015-01-28 | 深圳市捷顺科技实业股份有限公司 | License plate detection method and device |
| CN107832762A (en) * | 2017-11-06 | 2018-03-23 | 广西科技大学 | A kind of License Plate based on multi-feature fusion and recognition methods |
| US20180114337A1 (en) * | 2016-10-20 | 2018-04-26 | Sun Yat-Sen University | Method and system of detecting and recognizing a vehicle logo based on selective search |
| CN110020651A (en) * | 2019-04-19 | 2019-07-16 | 福州大学 | Car plate detection localization method based on deep learning network |
-
2019
- 2019-11-15 CN CN201911119925.7A patent/CN110866524A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104318225A (en) * | 2014-11-19 | 2015-01-28 | 深圳市捷顺科技实业股份有限公司 | License plate detection method and device |
| US20180114337A1 (en) * | 2016-10-20 | 2018-04-26 | Sun Yat-Sen University | Method and system of detecting and recognizing a vehicle logo based on selective search |
| CN107832762A (en) * | 2017-11-06 | 2018-03-23 | 广西科技大学 | A kind of License Plate based on multi-feature fusion and recognition methods |
| CN110020651A (en) * | 2019-04-19 | 2019-07-16 | 福州大学 | Car plate detection localization method based on deep learning network |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022048209A1 (en) * | 2020-09-03 | 2022-03-10 | 平安科技（深圳）有限公司 | License plate recognition method and apparatus, electronic device, and storage medium |
| CN112990197A (en) * | 2021-03-17 | 2021-06-18 | 浙江商汤科技开发有限公司 | License plate recognition method and device, electronic equipment and storage medium |
| CN114399766A (en) * | 2022-01-18 | 2022-04-26 | 平安科技（深圳）有限公司 | Optical character recognition model training method, device, equipment and medium |
| CN114399766B (en) * | 2022-01-18 | 2024-05-10 | 平安科技（深圳）有限公司 | Optical character recognition model training method, device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110413812B (en) | Neural network model training method and device, electronic equipment and storage medium | |
| CN110222775B (en) | Image processing method, image processing device, electronic equipment and computer readable storage medium | |
| CN110826567B (en) | Optical character recognition method, device, equipment and storage medium | |
| CN111784712B (en) | Image processing method, device, equipment and computer readable medium | |
| CN110866524A (en) | License plate detection method, device, equipment and storage medium | |
| CN110852258A (en) | Object detection method, device, equipment and storage medium | |
| CN114494298A (en) | Object segmentation method, device, equipment and storage medium | |
| WO2022105622A1 (en) | Image segmentation method and apparatus, readable medium, and electronic device | |
| CN110287817B (en) | Target recognition and target recognition model training method and device and electronic equipment | |
| CN113610034B (en) | Method and device for identifying character entities in video, storage medium and electronic equipment | |
| CN111126159A (en) | Method, apparatus, electronic device, and medium for tracking pedestrian in real time | |
| CN111797822B (en) | Text object evaluation method and device and electronic equipment | |
| CN110852242A (en) | Watermark identification method, device, equipment and storage medium based on multi-scale network | |
| CN115408609A (en) | Parking route recommendation method and device, electronic equipment and computer readable medium | |
| CN113033552B (en) | Text recognition method and device and electronic equipment | |
| CN110796144B (en) | License plate detection method, device, equipment and storage medium | |
| CN114612909A (en) | Character recognition method and device, readable medium and electronic equipment | |
| CN111680754B (en) | Image classification method, device, electronic equipment and computer readable storage medium | |
| CN111694875B (en) | Method and device for outputting information | |
| CN110334763B (en) | Model data file generation method, model data file generation device, model data file identification device, model data file generation apparatus, model data file identification apparatus, and model data file identification medium | |
| CN114004229A (en) | Text recognition method and device, readable medium and electronic equipment | |
| CN113705386A (en) | Video classification method and device, readable medium and electronic equipment | |
| CN112070034A (en) | Image recognition method and device, electronic equipment and computer readable medium | |
| CN112418233A (en) | Image processing method, image processing device, readable medium and electronic equipment | |
| CN110991312A (en) | Method, apparatus, electronic device, and medium for generating detection information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200306 |
|
| RJ01 | Rejection of invention patent application after publication |