CN111950659B - Double-layer license plate image processing method and device, electronic equipment and storage medium - Google Patents
Double-layer license plate image processing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a double-layer license plate image processing method, a double-layer license plate image processing device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring an original license plate image, and generating an image training sample; putting the image training sample into a convolutional neural network to obtain standard feature points; performing tilt correction according to the preset boundary characteristic points and the standard characteristic points to generate affine transformation parameters; and cutting the original license plate image according to the affine transformation parameters to generate a target image. The method solves the problems of single source of license plate data, low reliability of calculation results, narrow application range and low precision in the prior art.
Description
Technical Field
The application relates to the field of computer data storage, in particular to a double-layer license plate image processing method and device, electronic equipment and a storage medium.
Background
The existing license plate cutting method generally adopts the following procedures: 1. roughly positioning the license plate by a certain method to obtain the initial position of the license plate; 2. then, radon (Radon) transformation or horizontal and vertical projection of the regions is used for the initial license plate regions one by one; 3. calculating the approximate horizontal and vertical inclination angles of the license plate according to the calculated characteristic information; 4. performing inclination correction on the initial license plate area through the calculated horizontal and vertical inclination angles; 5. acquiring horizontal and vertical projection views of the license plate after inclination correction; 6. and (4) cutting the license plates one by one through projection.
The existing method has the following defects: 1. the dependence on license plate positioning is large, and the subsequent inclination angle calculation is directly influenced by the positioning accuracy; 2. the inclination angle calculation method depends on a fixed operator, and the calculation result is unreliable under the conditions of fouling, shielding and the like of the license plate; 3. the inclination angle calculation method has large calculation amount and memory requirements, is carried out region by region, and is not suitable for the conditions of a plurality of license plates (a checkpoint, illegal parking snapshot and the like); 4. the inclination angle calculation method is designed for a single-layer license plate, and has a poor effect on a double-layer license plate; 5. the inclination correction is only rough angle correction, and the removal work of an invalid region cannot be carried out, so that great interference is brought to the subsequent license plate cutting work.
Disclosure of Invention
An embodiment of the present application aims to provide a method and an apparatus for processing a double-layer license plate image, an electronic device, and a storage medium, so as to solve technical problems in the prior art.
In a first aspect, an embodiment of the present application provides a method for processing a double-layer license plate image, including: acquiring an original license plate image, and generating an image training sample; putting the image training sample into a convolutional neural network to obtain standard feature points; performing inclination correction according to the preset boundary characteristic points and the standard characteristic points to generate affine transformation parameters; and cutting the original license plate image according to the affine transformation parameters to generate a target image.
In one embodiment, the putting the image training samples into a convolutional neural network to obtain standard feature points includes: randomly generating a plurality of pieces of initial characteristic point information according to the image training sample; and counting the position information of a preset number of initial characteristic points according to the initial characteristic point information to generate standard characteristic points.
In an embodiment, performing tilt correction according to the preset boundary feature points and the standard feature points to generate affine transformation parameters includes: dividing an original license plate image into a first layer of correction area and a second layer of correction area according to preset boundary characteristic points; calculating offset information of the preset boundary characteristic points according to the image training samples and the preset boundary characteristic points; and generating a first affine transformation parameter of the first layer correction area and a second affine transformation parameter of the second layer correction area according to the first layer correction area, the second layer correction area and the offset information.
In an embodiment, the cutting an original license plate image according to affine transformation parameters to generate a target image includes: performing affine transformation inverse operation on the first affine transformation parameters to generate a first corrected image; and performing affine transformation inverse operation on the second affine transformation parameters to generate a second corrected image.
In a second aspect, an embodiment of the present application further provides a double-layer license plate image processing apparatus, including: the sample generation module is used for acquiring an original license plate image and generating an image training sample; the characteristic acquisition module is used for putting the image training samples into a convolutional neural network to acquire standard characteristic points; the affine transformation module is used for performing inclination correction according to the preset boundary characteristic points and the standard characteristic points to generate affine transformation parameters; and the image generation module is used for cutting the original license plate image according to the affine transformation parameters to generate a target image.
In an embodiment, the feature obtaining module is further configured to: randomly generating a plurality of pieces of initial characteristic point information according to the image training sample; and counting the position information of a preset number of initial characteristic points according to the initial characteristic point information to generate standard characteristic points.
In an embodiment, the affine transformation module is further configured to: dividing an original license plate image into a first layer of correction area and a second layer of correction area according to preset boundary characteristic points; calculating offset information of the preset boundary characteristic points according to the image training samples and the preset boundary characteristic points; and generating a first affine transformation parameter of the first layer correction area and a second affine transformation parameter of the second layer correction area according to the first layer correction area, the second layer correction area and the offset information.
In an embodiment, the image generation module is further configured to: performing affine transformation inverse operation on the first affine transformation parameters to generate a first corrected image; and performing affine transformation inverse operation on the second affine transformation parameters to generate a second corrected image.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a memory to store a computer program; a processor configured to perform the method of any of the preceding embodiments.
In a fourth aspect, an embodiment of the present application further provides a non-transitory electronic device readable storage medium, including: a program which, when run by an electronic device, causes the electronic device to perform the method of any of the preceding embodiments.
The double-layer license plate image processing method, the double-layer license plate image processing device, the electronic equipment and the storage medium overcome the defects of the conventional method through algorithm optimization, improve the robustness of the license plate inclination correction algorithm, reduce the time consumption and the memory requirement, and simultaneously completely support the inclination correction of the double-layer license plate. Specifically, the following aspects are provided:
1. and obtaining the inclination information of the license plate in a license plate feature point positioning mode.
2. Aiming at the problem of inaccurate positioning of the double-layer license plate, the number of the feature points can be increased to 6.
3. The double-layer license plate is directly decoupled into two independent single-layer license plates through the feature points distributed in different ways.
4. The license plate feature point positioning function and the license plate detection are combined into the same full-volume machine network, so that the calculation amount and the memory consumption caused by a single feature point calculation process are avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 2 is a schematic view of an application scene of a double-layer license plate image processing method according to an embodiment of the present application;
FIG. 3 is a flowchart of a double-layer license plate image processing method according to an embodiment of the present disclosure;
FIG. 4 is a flowchart of another double-deck license plate image processing method according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a double-layer license plate image processing device according to an embodiment of the present disclosure.
Icon: the system comprises an electronic device 1, a bus 10, a processor 11, a memory 12, a user terminal 100, a server 200, a double-layer license plate image processing device 500, an image acquisition module 501, a target detection module 502, a feature acquisition module 503 and a feature matching module 504.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
As shown in fig. 1, the present embodiment provides an electronic apparatus 1 including: at least one processor 11 and a memory 12, one processor being exemplified in fig. 1. Processor 11 and memory 12 are connected by bus 10, and memory 12 stores instructions executable by processor 11 and executed by processor 11.
In an embodiment, the electronic device 1 may combine the license plate location and the license plate feature point location into the same full-convolution network, calculate the specific position of the feature point in the picture through the offset, input the obtained feature point information and the standard feature point information into the tilt correction algorithm to calculate two sets of affine transformation parameters, and perform affine transformation inverse operation through the two sets of correction parameters, thereby obtaining two sets of independent image information.
Fig. 2 is a schematic view of an application scenario of the double-layer license plate image processing method provided in this embodiment. As shown in fig. 2, the application scenario may include a user terminal 100, and the user terminal 100 may be a smartphone, a tablet computer, or a drone with a photographing function. The user terminal 100 can execute the double-layer license plate image processing method provided by the application, and the calculation efficiency of double-layer license plate image processing is accelerated.
According to the requirement, the application scenario may further include a server 200, and the server 200 may be a server, a server cluster, or a cloud computing center. The server 200 may receive the image uploaded by the user terminal 100, execute the image processing method provided by the present application, and perform segmentation and classification according to the captured image.
Please refer to fig. 3, which is a flowchart illustrating a method for processing a dual-layer license plate image according to an embodiment of the present disclosure, where the method is executed by the electronic device 1 shown in fig. 1 and is used in the interactive scenario shown in fig. 2. The method comprises the following steps:
step 301: and acquiring an original license plate image, and generating an image training sample.
In this step, license plate location and license plate feature point location can be combined into the same full convolution network, offset prediction of a plurality of feature points is added on the basis of original license plate location output, and specific positions of the feature points in the picture are calculated through the offsets.
In an embodiment, the offset prediction of 6 feature points is added on the basis of the original license plate positioning output, namely four vertexes M1, M2, M5 and M6 of the license plate and two endpoints M3 and M4 of a boundary line between two upper lines and two lower lines of characters of a double-layer license plate. Among them, the frame ranges of M1, M2, M3, and M4 are upper layer divided images, and the frame ranges of M3, M4, M5, and M6 are lower layer divided images. For example, the two characters representing the administrative region in the first line of the double-layer license plate are "chuan a" and the second line is "12345", and the characters can be converted into a line form of "chuan a12345" after being split and recombined.
Step 302: and putting the image training sample into a convolutional neural network to obtain standard feature points.
In the step, in the stage of generating training, a group of standard feature point position information is counted by randomly generating a large amount of feature point information, and is used as a reference standard, which is called as a standard feature point.
Step 303: and performing tilt correction according to the preset boundary characteristic points and the standard characteristic points to generate affine transformation parameters.
In the step, the obtained characteristic point information and the standard characteristic point information are input into a tilt correction algorithm, and the tilt correction algorithm calculates two groups of affine transformation parameters according to the affine transformation principle. The first layer of license plate correction parameters from M1 to M4 and the second layer of license plate correction parameters from M3 to M6 are respectively.
Step 304: and cutting the original license plate image according to the affine transformation parameters to generate a target image.
In the step, two groups of independent image information can be obtained by running affine transformation inverse operation through two groups of correction parameters, wherein the first group is an upper half image of the license plate, and the second group is a lower half image of the license plate. While correcting, the algorithm can also remove invalid license plate information.
After passing through the license plate rectification portion, the double layer license plate has been cut into two separate single layer license plates. And the license plates are aligned in the horizontal direction, so that the cut license plates can be obtained only by calculating one more projection in the vertical direction.
Please refer to fig. 4, which is a flowchart illustrating another method for processing a dual-layer license plate image according to an embodiment of the present disclosure, which can be executed by the electronic device 1 shown in fig. 1 and used in the interactive scenario shown in fig. 2. The method comprises the following steps:
step 401: and acquiring an original license plate image, and generating an image training sample. For details, refer to the description of step 301 in the above embodiment.
Step 402: and randomly generating a plurality of pieces of initial feature point information according to the image training samples.
In this step, in the generation training stage, a large amount of feature point information needs to be randomly generated to count a set of standard feature point position information.
Step 403: and counting the position information of a preset number of initial characteristic points according to the initial characteristic point information to generate standard characteristic points.
In this step, a large amount of feature point information is randomly generated to count up a set of standard feature point position information as a reference standard.
Step 404: and dividing the original license plate image into a first layer of correction area and a second layer of correction area according to the preset boundary characteristic points.
In this step, four vertices M1, M2, M5, M6 of the license plate and two endpoints M3, M4 of the boundary line between the upper and lower lines of characters of the double-layer license plate are obtained. Wherein, the frame body ranges of M1, M2, M3 and M4 are the first layer of correction area, and the frame body ranges of M3, M4, M5 and M6 are the second layer of correction area.
Step 405: and calculating offset information of the preset boundary characteristic points according to the image training samples and the preset boundary characteristic points.
In this step, license plate location and license plate feature point location may be combined into the same fully-convoluted network, offset prediction of a plurality of feature points is added on the basis of original license plate location output, and the specific positions of the feature points in the picture are calculated through offsets.
Step 406: and generating a first affine transformation parameter of the first layer correction area and a second affine transformation parameter of the second layer correction area according to the first layer correction area, the second layer correction area and the offset information.
In the step, the obtained feature point information and the standard feature point information are input into a tilt correction algorithm, and the tilt correction algorithm calculates two groups of affine transformation parameters according to an affine transformation principle. Parameters for correcting the first-layer license plates of M1 to M4 and parameters for correcting the second-layer license plates of M3 to M6, respectively.
Step 407: and performing affine transformation inverse operation on the first affine transformation parameters to generate a first corrected image.
Step 408: and performing affine transformation inverse operation on the second affine transformation parameters to generate a second corrected image.
In the step, two groups of independent image information can be obtained by performing affine transformation inverse operation on two groups of correction parameters, wherein the first group is the upper half part image of the license plate, and the second group is the lower half part image of the license plate. While correcting, the algorithm can also remove invalid license plate information.
In one embodiment, after passing through the license plate correcting portion, the double layer license plate has been cut into two separate single layer license plates. And the license plate information is aligned in the horizontal direction, so that the completely cut license plate information can be obtained only by calculating one more projection in the vertical direction.
Please refer to fig. 5, which is a block diagram of an embodiment of the present application, and further provides a double-layer license plate image processing apparatus 500, which can be executed by the electronic device 1 shown in fig. 1 and used in the interactive scenario shown in fig. 2, so as to combine license plate positioning and license plate feature point positioning into a same full-convolution network, calculate a specific position of a feature point in a picture through an offset, input obtained feature point information and standard feature point information into a tilt correction algorithm to calculate two sets of affine transformation parameters, and perform an affine transformation inverse operation through the two sets of correction parameters, thereby obtaining two sets of independent image information. The double-deck license plate image processing apparatus 500 includes: a sample generation module 501, a feature acquisition module 502, an affine transformation module 503, and an image generation module 504. The specific principle relationship is as follows:
the sample generating module 501 is configured to obtain an original license plate image and generate an image training sample. Please refer to the description of step 301 in the above embodiment.
And the feature obtaining module 502 is configured to put the image training samples into a convolutional neural network to obtain standard feature points. Please refer to the description of step 302 in the above embodiment.
In an embodiment, the feature obtaining module 502 is further configured to: randomly generating a plurality of pieces of initial characteristic point information according to the image training sample; and counting the position information of a preset number of initial characteristic points according to the initial characteristic point information to generate standard characteristic points. Please refer to the description of steps 401 and 402 in the above embodiments.
And the affine transformation module 503 is configured to perform tilt correction according to the preset boundary feature points and the standard feature points, and generate affine transformation parameters. Please refer to the description of step 303 in the above embodiment.
In an embodiment, the affine transformation module 503 is further configured to: dividing an original license plate image into a first layer of correction area and a second layer of correction area according to preset boundary characteristic points; calculating offset information of the preset boundary characteristic points according to the image training samples and the preset boundary characteristic points; and generating a first affine transformation parameter of the first layer correction area and a second affine transformation parameter of the second layer correction area according to the first layer correction area, the second layer correction area and the offset information. Please refer to the description of steps 403-406 in the above embodiment.
And the image generation module 504 is configured to cut the original license plate image according to the affine transformation parameters to generate a target image. Please refer to the description of step 304 in the above embodiment.
In an embodiment, the image generation module 504 is further configured to: performing affine transformation inverse operation on the first affine transformation parameters to generate a first corrected image; and performing affine transformation inverse operation on the second affine transformation parameters to generate a second corrected image. Please refer to the description of steps 407 and 408 in the above embodiment.
The double-layer license plate image processing method, the double-layer license plate image processing device, the electronic equipment and the storage medium overcome the defects of the conventional method through algorithm optimization, improve the robustness of the license plate inclination correction algorithm, reduce the time consumption and the memory requirement, and simultaneously completely support the inclination correction of the double-layer license plate. Specifically, the following aspects are provided:
1. and obtaining the inclination information of the license plate in a license plate feature point positioning mode.
2. Aiming at the problem of inaccurate positioning of the double-layer license plate, the number of the feature points can be increased to 6.
3. And directly decoupling the double-layer license plate into two independent single-layer license plates through the feature points distributed in different ways.
4. The license plate feature point positioning function and the license plate detection are combined into the same full-volume computer network, so that the calculation amount and the memory consumption caused by a single feature point calculation flow are avoided.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units into only one type of logical function may be implemented in other ways, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should be noted that, if the functions are implemented in the form of software functional modules and sold or used as independent products, the functions may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A double-layer license plate image processing method is characterized by comprising the following steps:
acquiring an original license plate image, and generating an image training sample;
putting the image training sample into a convolutional neural network to obtain standard feature points;
performing tilt correction according to preset boundary feature points and the standard feature points to generate affine transformation parameters;
cutting the original license plate image according to the affine transformation parameters to generate a target image;
the inclination correction is carried out according to the preset boundary characteristic points and the standard characteristic points to generate affine transformation parameters, and the method comprises the following steps:
dividing the original license plate image into a first layer of correction area and a second layer of correction area according to the preset boundary characteristic points;
calculating offset information of the preset boundary feature points according to the image training samples and the preset boundary feature points;
generating a first affine transformation parameter of the first layer correction area and a second affine transformation parameter of the second layer correction area according to the first layer correction area, the second layer correction area and the offset information;
the preset boundary characteristic points are the top points of a double-layer license plate in the license plate image and the end points of the boundary lines of the upper line and the lower line of characters of the double-layer license plate.
2. The method of claim 1, wherein the putting the image training samples into a convolutional neural network to obtain standard feature points comprises:
randomly generating a plurality of pieces of initial characteristic point information according to the image training samples;
and counting the position information of the initial characteristic points in a preset quantity according to the initial characteristic point information to generate the standard characteristic points.
3. The method of claim 1, wherein the cutting the original license plate image according to the affine transformation parameters to generate a target image comprises:
performing affine transformation inverse operation on the first affine transformation parameters to generate a first corrected image;
and performing affine transformation inverse operation on the second affine transformation parameters to generate a second corrected image.
4. A double-deck license plate image processing apparatus, comprising:
the sample generation module is used for acquiring an original license plate image and generating an image training sample;
the characteristic acquisition module is used for putting the image training sample into a convolutional neural network to acquire standard characteristic points;
the affine transformation module is used for performing inclination correction according to preset boundary feature points and the standard feature points to generate affine transformation parameters;
the image generation module is used for cutting the original license plate image according to the affine transformation parameters to generate a target image;
the affine transformation module is further to:
dividing the original license plate image into a first layer of correction area and a second layer of correction area according to the preset boundary characteristic points;
calculating offset information of the preset boundary feature points according to the image training samples and the preset boundary feature points;
generating a first affine transformation parameter of the first layer correction area and a second affine transformation parameter of the second layer correction area according to the first layer correction area, the second layer correction area and the offset information;
the preset boundary characteristic points are the top points of a double-layer license plate in the license plate image and the end points of the boundary lines of the upper line and the lower line of characters of the double-layer license plate.
5. The apparatus of claim 4, wherein the feature acquisition module is further configured to:
randomly generating a plurality of pieces of initial characteristic point information according to the image training samples;
and counting the position information of the initial characteristic points in a preset quantity according to the initial characteristic point information to generate the standard characteristic points.
6. The apparatus of claim 4, wherein the image generation module is further configured to:
performing affine transformation inverse operation on the first affine transformation parameters to generate a first corrected image;
and performing affine transformation inverse operation on the second affine transformation parameters to generate a second corrected image.
7. An electronic device, comprising:
a memory to store a computer program;
a processor to perform the method of any one of claims 1 to 3.
8. A non-transitory electronic device readable storage medium, comprising: program which, when run by an electronic device, causes the electronic device to carry out the method of any one of claims 1 to 3.
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| CN107679531A (en) * | 2017-06-23 | 2018-02-09 | 平安科技（深圳）有限公司 | Licence plate recognition method, device, equipment and storage medium based on deep learning |
| CN110728283A (en) * | 2019-10-11 | 2020-01-24 | 高新兴科技集团股份有限公司 | License plate type identification method and device |
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