KR102272279B1 - Method for recognizing vehicle license plate - Google Patents

Abstract

The present invention relates to a method for recognizing a vehicle license plate using an artificial intelligence algorithm. According to the present invention, the method recognizes a vehicle license plate into a three-dimensional (3D) through an image depth-based vehicle license plate 3D learning model reflecting various vehicles, license plates, and surrounding environments so that the vehicle license plate is recognized according to an on-site situation, thereby reducing a data processing amount and increasing a recognition rate in comparison with a conventional color scheme. For example, the recognition rate is increased even when weather conditions such as fog, fine dust, and the like are different from usual. In addition, such a license plate 3D learning model converts data related to various places, hours, and surrounding environments into attributes, thereby increasing the recognition rate. In addition, since the vehicle and the vehicle license plate are extracted by using a different elliptical equation fit to each 3D object of the vehicle and the vehicle license plate, various types of depth images is expressed with a smaller amount of digital codes than before, thereby quickly performing a recognition process. The method comprises an information extraction step, an environment information collection step, and a 3D objectification step.

Description

Method for recognizing vehicle license plate using artificial intelligence algorithm

The content disclosed in the present specification relates to the field of license plate recognition technology, and more particularly, to a technology for performing traffic control by recognizing a license plate from a camera image when monitoring a vehicle traveling on a road.

Unless otherwise indicated herein, the material described in this section is not prior art to the claims of this application, and inclusion in this section is not an admission that it is prior art.

Recently, the vehicle number recognition system has been used in illegal parking enforcement devices, vehicle speed monitoring devices, vehicle access systems in buildings, and CCTV security systems. The vehicle number recognition system includes a camera, which is a photographing device, and an information processing unit for recognizing a vehicle number by analyzing an image photographed through the camera. The camera can be installed fixedly or mobile.

In addition, the vehicle number here is a unique number assigned to each vehicle for identification, and in terms of a person, it is the same as a resident registration number. Basic information such as vehicle name/manufacturer/year/month/displacement amount/motor type can be checked through these vehicle numbers, and accident history/maintenance history (in this case, only in the case of insurance processing or repair at a designated maintenance office)/ It is also possible to check important information such as the status of vehicle registration (inquiry of invincible vehicles and stolen vehicles is also possible).

Recently, a number of devices have been introduced to check the state and entry details of a specific vehicle or an unspecified vehicle for a specific or unspecified place.

On the other hand, conventional methods for detecting the license plate of a vehicle include a character-based detection method, an edge-based detection method, and the like. In particular, among these methods, there is a color-based detection method in an improved form. However, this color-based detection method is sensitive to changes in lighting or shadows.

In particular, sudden changes in illuminance caused by fog, clouds, snow, rain, etc. cause a decrease in the recognition rate of license plates.

When looking at the related prior art, the prior art document with this background is to the extent that the following patent document is available for reference without directly searching for the related prior art as the related prior art by examining the prior art from the patent document.

(Patent Document 1) KR1020190143548 A

For reference, the technology of Patent Document 1 relates to a vehicle recognition system in which a license plate recognition rate is improved through learning a license plate number, a type, and a color, and real-time learning is possible.

The disclosed content is to provide a license plate recognition method using an artificial intelligence algorithm that requires less data processing and a higher recognition rate than the conventional method using colors when recognizing a license plate, and enables rapid recognition.

A vehicle license plate recognition method using an artificial intelligence algorithm according to an embodiment,

From the 3D learning model of license plate based on image depth that reflects the types of various vehicles, license plates and surrounding environment, the vehicle and license plate are integrated into 3D objects to recognize license plates according to the field situation.

In this case, such a license plate 3D learning model attributes data related to various places, time zones, and surrounding environments.

In addition, at this time, since the vehicle and the license plate are extracted using different equations of the ellipse fit for each three-dimensional object of the license plate of the vehicle, it is characterized in that it expresses various types of depth images with a relatively small amount of digital code than in the prior art.

According to the embodiments, various vehicles, license plates, and license plates based on image depth reflecting the type of environment are recognized from the three-dimensional learning model of the vehicle and the license plate to three-dimensional objects to recognize the license plate according to the field situation, so the conventional color It requires less data processing and higher recognition rate than the previous method.

For example, the recognition rate is increased even when weather conditions such as fog and fine dust are different from usual.

And, in this case, such a license plate 3D learning model attributes data related to various places, time zones, and surrounding environments, so that the recognition rate is further increased.

In addition, in this case, since the vehicle and the license plate are extracted using different equations of the ellipse that fit each three-dimensional object of the license plate of the vehicle, the recognition process is quickly performed by expressing various types of depth images with a smaller amount of digital code than before. do.

1 is a diagram for conceptually explaining a vehicle license plate recognition method using an artificial intelligence algorithm according to an embodiment;
2 is a view showing the overall system to which the vehicle license plate recognition method using an artificial intelligence algorithm according to an embodiment is applied;
3A is a block diagram showing the configuration of a camera to which a license plate recognition method using an artificial intelligence algorithm is applied according to an embodiment;
3b is a block diagram showing the configuration of a central control center to which a vehicle license plate recognition method using an artificial intelligence algorithm is applied according to an embodiment;
4 is a flowchart sequentially illustrating a vehicle license plate recognition method using an artificial intelligence algorithm according to an embodiment;

1 is a diagram for conceptually explaining a vehicle license plate recognition method using an artificial intelligence algorithm according to an embodiment.

As shown in Figure 1, the vehicle license plate recognition method of an embodiment, when recognizing a license plate, various vehicles, license plates, and vehicles from a three-dimensional learning model of license plates based on image depth information reflecting types of surrounding environments, Since the license plate is converted into a three-dimensional object and the license plate is recognized according to the on-site situation, the amount of data processing is lower than that of the conventional color scheme and the recognition rate is increased.

In this case, such a license plate 3D learning model attributes data related to various image monitoring places, time zones, surrounding environments, or each type, so that the recognition rate is further increased.

And, in addition, in one embodiment, in this case, since the vehicle and the license plate are extracted using differently the equation of the ellipse fit for each three-dimensional object of the license plate of the vehicle, various types of depth images are expressed with a smaller amount of digital code than in the prior art. Accelerate recognition processing.

For reference, the recognition content according to this embodiment will be described in detail with reference to FIGS. 3 and 4 below.

In addition, the system to which such a license plate recognition method is applied will be schematically described with respect to an operation generally used.

In such a system, the license plate recognition method according to an embodiment is implemented in the image monitoring device, that is, the camera 100 itself, or is implemented in the central control center.

For reference, the central control center entirely manages the video monitoring operation of the cameras 100 installed in various monitoring areas from a remote location.

A more detailed description of these cameras is given below.

The camera 100 basically collects vehicle image information from the image monitoring area of each road and transmits it to the central control center when the vehicle is driving on a road, etc., so that traffic control can be performed through the central control center.

For example, when a plurality of vehicles (V-1 to V-3) drive on a multi-lane road (L-1 to L-4), the camera 100 is configured to operate in the image monitoring areas S-1 and S- In 2), surveillance images are collected and provided to the central control center.

So, through this, the central control center performs traffic control, such as recognizing a vehicle number or tracking a vehicle in which anomalies are found.

2 is a view showing the overall system to which the license plate recognition method using an artificial intelligence algorithm according to an embodiment is applied.

As shown in Figure 2, the system to which the license plate recognition method using an artificial intelligence algorithm according to an embodiment is applied is a plurality of cameras (100-1 ~ 100-n, etc.) installed for a plurality of different image monitoring areas, and the central It includes a control center (200).

The cameras (100-1 to 100-n, etc.) are installed in each of a plurality of different video surveillance areas, for example, at a major intersection, an access road, an intersection, and the like. These cameras (100-1 to 100-n, etc.) acquire a surveillance image in their own image monitoring area and provide it to the central control center 200 through their own network. Additionally, the cameras 100-1 to 100-n may recognize a vehicle number by using the vehicle license plate recognition method according to an embodiment within itself.

The central control center 200 basically collects surveillance images from the cameras (100-1 to 100-n, etc.) and recognizes the license plate or determines whether the vehicle is abnormal and tracks dangerous vehicles, etc., traffic control is implemented. In this case, in order to determine whether the vehicle is abnormal, the central control center 200 is provided with violations under the Road Traffic Act, such as signal violations, speeding, and parking violations. And, when a vehicle abnormality is detected, various control activities such as rotation of the camera (100-1 to 100-n, etc.), magnification, stop, text display, and status check are performed. Additionally, the central control center 200 may provide the traffic control results and the like to the disaster prevention management office or an external linkage organization. For example, the external linkage is a traffic situation control information processing device, a police station information processing device, a transportation agency information processing device, or an emergency rescue information processing device, a fire station information processing device, and the like.

3A and 3B are block diagrams illustrating the configuration of an apparatus to which a license plate recognition method using an artificial intelligence algorithm is applied according to an embodiment.

Specifically, FIG. 3A is a block diagram illustrating the configuration of a camera to which a license plate recognition method according to an embodiment is applied. And, Figure 3b is a block diagram showing the configuration of the central control center to which the license plate recognition method is applied.

As shown in FIG. 3A , first, the camera 100 according to an embodiment includes a camera module 101 , a DSP unit 102 , an artificial intelligence unit 103 , a control unit 104 , and a communication unit 105 . do.

In particular, the camera module 101 is installed in a stereo type to acquire image depth information. So, the camera module 101 acquires a stereo image for a video monitoring area designated in advance through the manager.

The DSP unit 102 recognizes the license plate at high speed by actually applying the license plate recognition method according to an embodiment in this stereo image. In this case, the DSP unit 102 uses the license plate 3D learning model according to an embodiment in conjunction with the artificial intelligence unit. So, specifically, the DSP unit 102 sequentially performs vehicle detection, license plate detection, character segmentation, character recognition, and image distortion correction accordingly.

The artificial intelligence unit 103 provides a license plate 3D learning model according to an embodiment when the license plate is recognized by the DSP unit 102 .

The control unit 104 provides the vehicle number recognized by the DSP unit 102 in connection with the central control center 200 to the central control center 200, and receives a control signal from the central control center 200 accordingly. It is provided to control the operation of the camera module 102 . Alternatively, in another form, the control unit 104 may provide only the monitoring image processed by the DSP unit 102 to the central control center 200 .

The communication unit 105 transmits and receives information to and from the central control center 200 registered in advance under the control of the control unit 104 according to a communication protocol.

Next, as shown in FIG. 3B , the central control center 200 according to an embodiment includes an I/F unit 201 , a main control unit 202 , and a database 203 .

The I/F unit 201 transmits/receives information to and from the camera 100 registered in advance.

The main control unit 202 receives the stereo image from the camera 100 and actually applies the license plate recognition method according to an embodiment in the current stereo image, so it recognizes the license plate at high speed. In this case, the main control unit 202 uses the license plate 3D learning model according to an embodiment. Accordingly, vehicle detection, license plate detection, character segmentation, character recognition, and image distortion correction are sequentially performed. Then, the main control unit 202 controls the operation of the camera 100 corresponding to the case in which an abnormal state is detected in a specific vehicle according to the vehicle number recognition result, for example, image enlargement, zoom-in, etc. do. So, through this, it monitors dangerous vehicles, etc. and performs traffic control.

The database 203 registers a vehicle license plate three-dimensional learning model according to an embodiment when such a license plate is recognized. In addition, the database 203 registers registration information of the camera 100 and an external linkage organization and violations under the Road Traffic Act in order to perform traffic control.

4 is a flowchart sequentially illustrating a vehicle license plate recognition method using an artificial intelligence algorithm according to an embodiment.

As shown in Figure 4, the license plate recognition method of an embodiment is first, when a stereo image of a vehicle passing through the image monitoring area is input, the type of vehicle and license plate in the corresponding stereo image, image structure features and images Depth information is extracted (S401).

And, when the type of vehicle and license plate, image structure characteristics, and image depth information are extracted in this way, environmental information from the image monitoring area is collected (S402).

So, through this, the license plate of the vehicle is 3D objectified according to the vehicle license plate 3D learning model according to the embodiment below by the type of vehicle and license plate, image intrinsic features and image depth, and environmental information of the image monitoring area ( S403).

Through this, one embodiment recognizes the vehicle and the license plate as a three-dimensional object from the vehicle license plate 3D learning model based on the image depth information reflecting the various vehicles, the license plate, and the surrounding environment to recognize the license plate according to the field situation, so that the conventional It requires less data processing and higher recognition rate than the color method.

The license plate three-dimensional learning model is made as follows.

a) First, when the license plate 3D learning model recognizes a license plate, a number of different vehicles, a vehicle according to the type of the surrounding environment for each license plate type, and a license plate are classified into image structure features and image depth, and three-dimensional objectify

For example, these vehicle types are passenger cars, vans, buses, trucks, excavators, and the like. In addition, the types of the surrounding environment are weather (eg fog, rain, snow, etc.), temperature and humidity, fine dust, season, etc.

And, by generating an equation of an ellipse on the image plane in correspondence with such a vehicle and each three-dimensional object of the license plate, the corresponding information is extracted to define a learning model.

At this time, the equation of the ellipse is obtained by extracting the elliptic factor for each different image depth, and becomes the coordinates on the image plane and the parameters. And, these parameters are determined according to the image depth information, and the determined result becomes an elliptic factor.

So, through this, the vehicle and the license plate are extracted using different equations of the ellipse that fit each three-dimensional object of the vehicle and the license plate, so that the recognition process can be performed quickly by expressing various types of depth images with a smaller amount of digital code than before. do.

b) Next, extract the image structure features and image depth datasets of vehicles and license plates by a number of different vehicles, license plates, and surrounding environment types.

c) Then, attribute these datasets to a number of different surveillance locations, time zones, and types of surroundings. That is, the image structure features and image depth of the vehicle and the license plate are attribute.

For example, such video surveillance places are general roads, tunnels, and mountainous areas. In addition, the time zone is dawn or night, morning and afternoon, sunset, etc., and the surrounding environment is the same as the above-described surrounding environment information. And, based on this information, types of information indicating similar properties are made.

d) So, based on these attribution results, the image structure features and image depth properties of the vehicle and license plate are determined for each of a number of different learning models.

e) Then, normalize the determined result.

f) And, by setting the image structure features and image depth of the vehicle and the license plate by a number of different learning models, it is set as an independent and dependent variable for objectification according to a number of different vehicles and the three-dimensional object of the license plate.

g) Next, the set result is generated as learning and training data.

h) So, through this, a 3D learning model of license plate based on deep learning is generated from these results.

And, by extracting the license plate number from the three-dimensional object of the license plate of the vehicle, it is recognized (S404 and S405).

For reference, since vehicle number recognition belongs to the prior art, a description thereof will be omitted here.

As described above, one embodiment recognizes the vehicle and license plate as a three-dimensional object from the vehicle license plate 3D learning model based on the image depth based on the image depth reflecting the types of various vehicles, license plates, and surrounding environment to recognize the license plate according to the field situation, so that the conventional It requires less data processing and higher recognition rate than the color method.

In this case, such a license plate 3D learning model attributes data related to various places, time zones, and surrounding environments, so that the recognition rate is further increased.

And, in addition, in this case, since one embodiment extracts the vehicle and the license plate by using the equation of the ellipse that fits each three-dimensional object of the vehicle and the license plate differently, various types of depth images with a smaller amount of digital code than in the prior art. Express the recognition process quickly.

On the other hand, additionally, such a license plate recognition method extracts a plurality of license plates, that is, a license plate for a multi-target, in batches when extracting a license plate from a stereo image, so even if there are many targets in a specific monitoring area find it quickly

To this end, this license plate recognition method generates a background part by accumulating stereo images when a plurality of vehicles (multi-targets) are first found in a stereo image.

Then, pixels exceeding a preset difference value are calculated based on the background part.

Next, pixel regions having such a difference value equal to or greater than a threshold are collected and acquired as a foreground part.

Then, the noise of the foreground part is deleted through the morphology filter.

Next, there are several objects in the foreground part from which the noise has been removed. In this state, partial contours for each different object are extracted from the image distribution diagram.

Then, in the partial contour of the object extracted in this way, an area smaller than a preset minimum size is combined with an adjacent area to generate a sub-body of the object.

Therefore, by comparing the sub-body of this object with the sub-body of a preset vehicle, only the sub-body of the current vehicle is all batch-extracted.

Then, when the sub-body of the vehicle is extracted, the overall size and position of the current vehicle are estimated from the sub-body of the vehicle by a preset format for estimating the overall size and position of the vehicle for each sub-body of a different vehicle.

So, by using the vehicle, the vehicle by license plate type, and the license plate image structure feature set in advance from the entire vehicle estimated in this way, the type of the current vehicle and the license plate and the image structure feature are integrally extracted for each vehicle.

Accordingly, in another embodiment, when extracting a license plate from a surveillance image, a plurality of license plates, i.e., a license plate for multiple targets, are collectively tracked and extracted, so even if there are many targets in a specific monitoring area, it is quickly found. pay

Additionally, this embodiment can also immediately identify a dangerous vehicle or the like during a tracking operation for such multi-targets.

Specifically, in this identification operation, when the overall size and position of the vehicle are estimated by the image distribution map, the image distribution map of the current vehicle is compared from a preset abnormal vehicle image distribution map.

Therefore, as a result of the comparison, an alarm is issued when the difference between the image distribution diagram of the abnormal vehicle and the image distribution diagram of the current vehicle is greater than a preset difference value.

On the other hand, when the difference value between the image distribution diagram of the abnormal vehicle and the image distribution diagram of the current vehicle is less than the difference value, no alarm is performed.

And, when it becomes an alarm in this way, it has a format for classifying and processing the alarm by type of vehicle and license plate, management area type, management purpose type, and management time period, and the current alarm is performed according to the corresponding grade.

On the other hand, another embodiment provides a guide for developers because, when such a video monitoring system provides information about the surrounding environment to the central control center, a library responsible for communication functions is created for each video monitoring system. make it possible

To this end, in this embodiment, when environmental information around video surveillance is collected, a library for communication functions that is integrated with communication data characteristics unique to a plurality of different types of video surveillance system under an open common platform is registered in advance.

Then, when any one of a plurality of different types of video surveillance system is set according to the management setting information, an application and component of the corresponding video surveillance system type are generated by the library for the communication function.

So, environment information around the video surveillance is collected by these applications and components.

In this case, the library consists, for example, schematically as follows.

First, the library is configured to use one or more computer programs (eg, JavaScript, Java) for controlling various sensors and actuators related to an image monitoring system.

And, the library combines the classes that describe these control methods.

At this time, the method is configured to control according to the characteristics of communication data for each different video surveillance system.

And, also, these libraries consist of a standard library and a user-defined library.

Additionally, in such cases, a guide is provided for the facility developer as a whole.

Specifically, when the surrounding environment information of the video monitoring area is collected, the development guide library that integrates the unique data characteristics, applications, control operation environment, and services for each type of video monitoring system under an open common platform is registered in advance. do.

Then, when any one of the video surveillance system types is set according to the management setting information, applications and components of the corresponding video surveillance system type are generated by the development guide library.

Thus, the generated application and component are additionally used to collect the surrounding environment information of the video surveillance area.

In addition, this embodiment provides a protocol and architecture according to the oneM2M standard.

Specifically, when the surrounding environment information of the video monitoring area is collected, the protocol according to the oneM2M standard for managing the video monitoring device as a sensor node and the protocol library constituting the protocol management file are registered in advance.

In addition, these protocol libraries are integrally configured by communication data characteristics unique to each type of video surveillance system under an open common platform.

Then, when any one of the video surveillance system types is set according to the management setting information, an application and a component are generated by the protocol library.

Therefore, even with these applications and components, information about the surrounding environment of the video surveillance area is collected.

In addition, in this embodiment, it is possible to perform real-time monitoring and control, and sharing with other administrators in the video surveillance system.

To this end, when the surrounding environment information of the video monitoring area is collected, the unique real-time monitoring and control application production characteristics for each video monitoring system, an editor for sharing with other managers, and the video monitoring system to be controlled through the editor are connected. Register the tool in advance.

In addition, a component for expressing information collected from the video surveillance system connected in this way as a manager app service, and a function to create and use the component as an open API are also registered in advance.

Then, these registered results are created as an executable file by composing the corresponding service app under the open platform.

Therefore, since the video monitoring system is monitored and controlled in real time by such an executable file, information about the surrounding environment of the video monitoring area is collected.

On the other hand, this embodiment also provides a lightweight protocol for a low-performance video surveillance system in addition to these various functions.

Specifically, it is done as follows.

First, when the surrounding environment information of the video monitoring area is collected, the data transmission specification of the video monitoring system is compared with a preset reference data transmission specification.

As a result of the comparison, if the data transmission specification of the video surveillance system is greater than or equal to the reference data transmission specification, the current state is maintained. On the other hand, if it is less than the data transmission specification, an encryption buffer is allocated and initialized.

Next, an encryption key is generated through a preset master key.

Then, the surrounding environment information of the video monitoring area is read.

Then, the length of the surrounding environment information and the corresponding encryption and data length are compared among the predetermined encrypted data lengths corresponding to a plurality of different video surveillance systems.

As a result of the comparison, if the length of the surrounding environment information is smaller than the corresponding encrypted data length, it is transmitted without encryption, and if it is larger, the frame is detected.

And, the extension header reads the extension bit to check whether there is.

As a result of the check, if there is an extension bit, the payload start point offset is moved by the extension length.

On the other hand, the frame, encryption key, and payload offset information are transmitted as parameters, and the start part of the payload of the frame is partially encrypted in a preset encryption format to provide it as a lightweight protocol for a low-performance video surveillance system.

And, in this case, the following configuration is further provided for security management in such a system environment.

Specifically, in order to collect the surrounding environment information of the video monitoring area, a lower hash value is given in advance based on a hash tree for each different application, and an upper hash value of the hash tree is registered.

In addition, when a hash node is formed by connecting a hash value for each application used to collect the current surrounding environment information, a format is registered to confirm that it is secure.

In this state, when the surrounding environment information of the video monitoring area is collected, it is confirmed that security is achieved only when a hash node is formed by connecting a hash value for each related application according to this format.

In addition, when the surrounding environment information of the video monitoring area is collected, the application is additionally detected, and when the detection result, when the application is used, the signature information of the corresponding application is searched according to the user selection mode based on the white list and the black list. .

As a result of the search, if there is the corresponding application signature information, use of the own application is allowed, whereas if there is no corresponding application signature information, the use of the own application is blocked.

100 : camera
200: central control center
101: camera module 102: DSP unit
103: artificial intelligence unit 104: control unit
105: communication department
201: I/F unit 202: main control unit
203 : database

Claims (10)

When a stereo image of a vehicle passing through an image monitoring area is input, a first step of extracting the type of vehicle and license plate, image structure characteristics, and image depth information from the corresponding stereo image;
a second step of collecting environmental information from the image monitoring area when the vehicle and license plate types, image structure features, and image depth information are extracted;
A third step of making the vehicle and the license plate into a three-dimensional object according to the following three-dimensional learning model of the license plate by the type of the vehicle and the license plate, image structure features and image depth information, and environmental information of the image monitoring area; and

The license plate three-dimensional learning model,
a) In the case of recognizing a license plate, a three-dimensional object is made by classifying the vehicle and license plate into image structural features and image depth according to a number of different vehicle types and license plate types, according to the type of the surrounding environment,
Define a learning model for extracting vehicle and license plate information by generating an elliptical equation on the image plane in correspondence with the vehicle and each different three-dimensional object of the license plate,
b) extracting image structure features and image depth datasets of vehicles and license plates for a number of different vehicles and license plates and types of surrounding environments;
c) attribute the extracted data set by reflecting a number of different video surveillance locations, time zones, and types of surrounding environments;
d) determining the properties of the image structure features and image depth of the vehicle and the license plate for each of a plurality of different learning models based on the attribution result,
e) normalizing the determined result,
f) Based on the normalized result, by setting the image structure features and image depth of the vehicle and the license plate for each different learning model, independent and dependent for a number of different vehicles and 3D objectization corresponding to each 3D object of the license plate set as a variable,
g) by generating the set result as learning and training data,
h) generate a license plate 3D learning model based on deep learning from the generated result,

a fourth step of extracting and recognizing the vehicle number by the three-dimensional objectized vehicle and the license plate; including,

The first step is
a step 1-1 of generating a background part by accumulating stereo images when a plurality of vehicles (multi-targets) are found in the stereo image;
a 1-2 step of calculating pixels exceeding a preset difference value based on the background part;
Steps 1-3 of obtaining a pixel region having the difference value equal to or greater than a threshold as a foreground part;
Steps 1-4 of removing the noise of the foreground part through a morphology filter;
Steps 1-5 of extracting partial contours for each of a plurality of different objects from the noise-removed foreground part using an image distribution diagram;
1-6 steps of creating a sub-body of the object by combining an area smaller than a preset minimum size in the extracted partial contour of the object with an adjacent area;
Steps 1-7 of comparing the sub-body of the created object with a preset sub-body of the vehicle and collectively extracting only the sub-body of the current vehicle;
When the sub-body of the vehicle is extracted, the first to eighth steps for estimating the overall size and position of the current vehicle by a preset format for estimating the overall size and position of the vehicle for each sub-body of different vehicles from the sub-body of the vehicle step; and
A method of integrally extracting the type of the current vehicle and license plate, and the image structure feature of the vehicle and the license plate for each vehicle by using the vehicle by vehicle and license plate type preset in the entire estimated vehicle, and the license plate image structure feature Steps 1-9; including;

Steps 1-8 are,
When the overall size and location of the vehicle are estimated by the image distribution map, the image distribution map of the current vehicle is compared from the preset abnormal vehicle image distribution map, and if it is greater than the preset difference value, an alarm is issued, and when it is less than the image distribution map, no alarm,

Steps 1-8 are,
When the alarm becomes the type, the type of vehicle and license plate, the type of management area, the type of management purpose, and a format for grading and processing by management time zone, performing the current alarm according to the corresponding grade; A vehicle license plate recognition method using an artificial intelligence algorithm, characterized in that delete delete delete The method according to claim 1,
The second step is
a 2-1 step of registering, in advance, a library for communication functions that integrates communication data characteristics unique to a plurality of different types of video monitoring systems under an open common platform when environmental information around the video monitoring area is collected;
a second-2 step of generating an application and a component of the corresponding video surveillance system type by the library for communication function when any one of the plurality of different video surveillance system types is set according to management setting information; and
a step 2-3 of collecting environmental information around the video surveillance area by the generated application and component; A vehicle license plate recognition method using an artificial intelligence algorithm, characterized in that it comprises a. 6. The method of claim 5,
The second step is
When the surrounding environment information of the video monitoring area is collected, the development guide library that integrates the unique data characteristics for each type of video monitoring system, applications, control operation environment, and services under an open common platform is registered in advance. -Stage 1; and
a step 2-2' of generating an application and component of the corresponding video surveillance system type by the development guide library when any one of the video surveillance system types is set according to management setting information; further comprising,

Step 2-3 is
collecting information about the surrounding environment of the video surveillance area by additionally using the application and component generated in the step 2-2'; A vehicle license plate recognition method using an artificial intelligence algorithm, characterized in that 7. The method of claim 6,
The second step is
When the surrounding environment information of the video monitoring area is collected, a protocol according to the oneM2M standard for managing the video monitoring device as a sensor node, and a protocol library constituting the protocol management file are registered in advance;
A step 2-1'' of integrating the protocol library by communication data characteristics unique to each type of video surveillance system under an open common platform; and
a 2-2'' step of generating an application and a component by the protocol library when any one of the video surveillance system types is set according to management setting information; further comprising,

Step 2-3 is
collecting the surrounding environment information of the video surveillance area even with the applications and components generated in the step 2-2''; A vehicle license plate recognition method using an artificial intelligence algorithm, characterized in that 8. The method of claim 7,
The second step is
When the surrounding environment information of the video monitoring area is collected, a tool for connecting the unique real-time monitoring and control application production characteristics for each video monitoring system, an editor for sharing with other managers, and a video monitoring system to be controlled through the editor pre-register,
a 2-1''' step of pre-registering a component for expressing information collected from the connected video surveillance system as a manager app service, and a function for creating and using the component as an open API; and
a 2-2''' step of composing the registered result into a corresponding service app under an open platform and generating an executable file; further comprising,

Step 2-3 is
to collect the surrounding environment information of the video monitoring area by real-time monitoring and control of the video monitoring system by the generated executable file; A vehicle license plate recognition method using an artificial intelligence algorithm, characterized in that 9. The method according to any one of claims 5 to 8,
The second step is
A) When the surrounding environment information of the video monitoring area is collected, the data transmission specification of the video monitoring system is compared with a preset reference data transmission specification,
B) As a result of the comparison, if the data transmission specification of the video surveillance system is higher than the reference data transmission specification, the current state is maintained, whereas if it is less than the data transmission specification, a buffer for encryption is allocated and initialized,
C) Generates an encryption key through a preset master key,
D) read the surrounding environment information of the video monitoring area,
E) comparing the length of the surrounding environment information and the corresponding encryption and data length among the predetermined encrypted data lengths corresponding to a plurality of different video surveillance systems,
F) As a result of the comparison, if the length of the surrounding environment information is smaller than the corresponding encrypted data length, it is transmitted without encryption, and if it is large, the frame is detected,
G) The extension header reads the extension bit to check whether there is,
H) As a result of the check, if there is an extension bit, the payload start offset is moved by the extension length,
I) receiving the frame, encryption key, and payload offset information as parameters, partially encrypting the start part of the frame's payload in a preset encryption format, and providing it as a lightweight protocol for a low-performance video surveillance system; A vehicle license plate recognition method using an artificial intelligence algorithm, characterized in that 10. The method of claim 9,
The second step is
a) In order to collect the surrounding environment information of the video monitoring area, a lower hash value is given in advance based on the hash tree for each different application, and the upper hash value of the hash tree is registered,
Registers a format that confirms that it is secure when a hash node is formed by connecting a hash value for each application used to collect the current surrounding environment information,
b) When the surrounding environment information of the video monitoring area is collected, it is confirmed that security is secured only when a hash node is formed by connecting a hash value for each related application according to the format,
c) When the surrounding environment information of the video monitoring area is collected, the application is additionally detected, and, as a result of the detection, when the application is used, the signature information of the corresponding application is searched according to the user selection mode based on the white list and the black list. and,
d) as a result of the search, allowing the use of one's own application when there is corresponding application signature information, while blocking the use of one's own application when there is no corresponding application signature information; A vehicle license plate recognition method using an artificial intelligence algorithm, characterized in that

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