CN113095281A - Fake-licensed vehicle identification method and device, electronic equipment and storage medium - Google Patents

Fake-licensed vehicle identification method and device, electronic equipment and storage medium Download PDF

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Publication number
CN113095281A
CN113095281A CN202110476419.4A CN202110476419A CN113095281A CN 113095281 A CN113095281 A CN 113095281A CN 202110476419 A CN202110476419 A CN 202110476419A CN 113095281 A CN113095281 A CN 113095281A
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information
target
vehicle
longitude
latitude
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麦忠海
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Ping An International Smart City Technology Co Ltd
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Ping An International Smart City Technology Co Ltd
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    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
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    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/587—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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Abstract

The invention relates to the technical field of artificial intelligence, and provides a fake-licensed vehicle identification method, a fake-licensed vehicle identification device, electronic equipment and a storage medium, wherein the method comprises the following steps: receiving a target image of a target vehicle in real time, identifying the target image and determining first information of the target vehicle; extracting registration information according to the license plate number in the first information; when second information inconsistent with the registration information exists in the first information, acquiring a plurality of longitudes and latitudes of the target vehicle within a preset time period; and determining a plurality of longitude and latitude deviation values of the target vehicle according to the plurality of longitude and latitude deviation values, and determining whether the target vehicle is a fake-licensed vehicle or not according to the plurality of longitude and latitude deviation values. According to the method and the device, whether the target vehicle is the fake-licensed vehicle or not is automatically identified according to the longitude and latitude deviation values, the information of a user does not need to be acquired, the information safety of the vehicle owner is ensured, and the identification efficiency and accuracy of the fake-licensed vehicle are improved. In addition, the invention also relates to the technical field of block chains, and the target image is stored in the block chain node.

Description

Fake-licensed vehicle identification method and device, electronic equipment and storage medium
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a fake-licensed vehicle identification method and device, electronic equipment and a storage medium.
Background
At present, vehicles are used for traveling or goods transportation, but illegal personnel can transform the vehicles into the same vehicles with real license plates in an illegal way. In the prior art, a face image of a driver driving a vehicle is acquired, the face image of the driver is compared with a face image of a registered vehicle owner of the vehicle, and when the face image of the driver is determined to be inconsistent with the face image of the registered vehicle owner of the vehicle, the current vehicle can be determined to be a fake-licensed vehicle.
However, in the prior art, since there is a driver driving a current vehicle, the vehicle may be rented by a user, and if the face image of the driver is compared with the face image of a registered owner of the vehicle, the vehicle is misjudged as a fake-licensed vehicle, which results in a low recognition accuracy of the fake-licensed vehicle.
Disclosure of Invention
In view of the above, there is a need for a fake-licensed vehicle identification method, device, electronic device, and storage medium, which automatically identify whether a target vehicle is a fake-licensed vehicle according to a plurality of longitude and latitude deviation values, without acquiring user information, thereby ensuring the safety of vehicle owner information and improving the identification efficiency and accuracy of the fake-licensed vehicle.
A first aspect of the present invention provides a fake-licensed vehicle identification method, the method comprising:
receiving a target image of a target vehicle shot by a camera, and identifying the target image to determine first information of the target vehicle;
extracting corresponding registration information according to the license plate number in the first information;
identifying whether second information inconsistent with the registration information exists in the first information;
when second information inconsistent with the registration information exists in the first information, acquiring a plurality of longitudes and latitudes of the target vehicle within a preset time period;
determining a plurality of longitude and latitude deviation values of the target vehicle according to the plurality of longitude and latitude dimensions, and determining whether the target vehicle is a fake-licensed vehicle according to the plurality of longitude and latitude deviation values.
Optionally, the obtaining of the multiple longitudes and latitudes of the target vehicle within the preset time period includes:
acquiring a plurality of target images of the target vehicle shot by a camera within a preset time period;
extracting time information and coordinate information in the multiple target images;
mapping the coordinate information to a preset map, and updating the coordinate information according to the preset map to obtain new coordinate information;
drawing a running track of the target vehicle according to the time information and the new coordinate information;
and randomly extracting a plurality of target points from the driving track, and taking the longitude and latitude of the target points as a plurality of longitude and latitude of the target vehicle in a preset time period.
Optionally, the obtaining of the multiple longitudes and latitudes of the target vehicle within the preset time period includes:
acquiring a plurality of target images of the target vehicle shot by a camera within a preset time period;
extracting first time information and first coordinate information in each target image;
mapping the first coordinate information to a preset map to obtain second coordinate information corresponding to the target image;
adjusting the first time information according to the preset map, the first coordinate information and the second coordinate information to obtain second time information corresponding to the target image;
generating a running track of the target vehicle according to second time information and the second coordinate information of the plurality of target images;
and randomly extracting a plurality of target points from the driving track, and taking the longitude and latitude of the target points as a plurality of longitude and latitude of the target vehicle in a preset time period.
Optionally, the identifying the target image and determining the first information of the target vehicle includes:
scanning each pixel point of the target image to obtain RGB data of each pixel point, wherein the target image is stored in a block chain node;
converting the RGB data of each pixel point into HSV space image data;
identifying the color of the license plate background color in the target image, and performing color filtering according to a license plate background color threshold value corresponding to the color of the license plate background color to obtain a filtered first image;
carrying out noise reduction processing and edge detection on the first image to obtain a second image; traversing the second image by adopting depth first to obtain a plurality of connected areas; recording the length ratio of each connected region, determining at least one target connected region according to the length-width ratios of the plurality of connected regions and a preset length-width ratio threshold, and screening out a license plate image of the target vehicle from the at least one target connected region; identifying the license plate image to obtain the license plate number of the target vehicle;
recognizing the first image to obtain the body color of the target vehicle, and recognizing the type of the first image to obtain the type recognition information of the target vehicle;
and determining the color of the license plate ground color, the license plate number, the body color and the vehicle type identification information of the target vehicle as the first information of the target vehicle.
Optionally, the extracting corresponding registration information according to the license plate number in the first information includes:
identifying city information in the license plate number;
acquiring a corresponding calling interface according to the city information;
and calling the registration information corresponding to the license plate number according to the license plate number and the corresponding calling interface.
Optionally, the determining a plurality of latitude and longitude deviation values of the target vehicle according to the plurality of dimensions comprises:
calculating a deviation value between each longitude and latitude and the rest longitude and latitude to obtain a plurality of first deviation values;
determining the first deviation values as latitude and longitude deviation values of the target vehicle.
Optionally, the determining a plurality of latitude and longitude deviation values of the target vehicle according to the plurality of dimensions comprises:
calculating a deviation value between each longitude and latitude and the rest longitude and latitude to obtain a plurality of first deviation values;
determining a standard time difference between each longitude and latitude and the rest of the longitude and latitude according to a preset map;
calculating the quotient of each first deviation value and the corresponding standard time difference to obtain the running speed between each longitude and latitude and the rest longitude and latitude;
calculating a difference value between the driving speed between each longitude and latitude and the rest longitude and latitude and a speed threshold value corresponding to the longitude and latitude to obtain a plurality of second deviation values;
determining the second deviation values as longitude and latitude deviation values of the target vehicle.
A second aspect of the present invention provides a fake-licensed vehicle identifying device, the device comprising:
the receiving module is used for receiving a target image of a target vehicle shot by a camera, identifying the target image and determining first information of the target vehicle;
the extraction module is used for extracting corresponding registration information according to the license plate number in the first information;
the identification module is used for identifying whether second information inconsistent with the registration information exists in the first information or not;
the acquisition module is used for acquiring a plurality of longitudes and latitudes of the target vehicle within a preset time period when second information inconsistent with the registration information exists in the first information;
and the determining module is used for determining a plurality of longitude and latitude deviation values of the target vehicle according to the plurality of longitude and latitude deviation values and determining whether the target vehicle is a fake-licensed vehicle or not according to the plurality of longitude and latitude deviation values.
A third aspect of the invention provides an electronic device comprising a processor and a memory, the processor being configured to implement the fake-licensed vehicle identification method when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the fake-licensed vehicle identification method.
In summary, according to the fake-licensed vehicle identification method, the fake-licensed vehicle identification device, the electronic device and the storage medium, on one hand, a plurality of longitude and latitude deviation values of the target vehicle are determined according to the plurality of longitude and latitude deviation values, whether the target vehicle is the fake-licensed vehicle is automatically identified by comparing the determined longitude and latitude deviation values of the target vehicle with a preset deviation threshold value, the information of a user does not need to be acquired, the information safety of a vehicle owner is ensured, and the identification efficiency and the accuracy of the fake-licensed vehicle are improved; on the other hand, a plurality of longitudes and latitudes of the target vehicle within a preset time period are obtained, the coordinate information of the target vehicle is mapped into a corresponding preset map, actual time information and coordinate information corresponding to the target vehicle are determined, a running track of the target vehicle is drawn, and longitudes and latitudes of a plurality of target points are randomly extracted from the running track to serve as the plurality of longitudes and latitudes of the target vehicle within the preset time period, so that the accuracy and diversity of the plurality of longitudes and latitudes are improved; and finally, extracting corresponding registration information according to the license plate number in the first information, and calling the registration information from a corresponding calling interface through the city information in the license plate number, so that the registration information is called in a targeted manner, and the calling efficiency and accuracy of the registration information are improved.
Drawings
Fig. 1 is a flowchart of a fake-licensed vehicle identification method according to an embodiment of the present invention.
Fig. 2 is a structural diagram of a fake-licensed vehicle identification apparatus according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Example one
Fig. 1 is a flowchart of a fake-licensed vehicle identification method according to an embodiment of the present invention.
In this embodiment, the fake-licensed vehicle identification method may be applied to an electronic device, and for an electronic device that needs to perform fake-licensed vehicle identification, the fake-licensed vehicle identification function provided by the method of the present invention may be directly integrated on the electronic device, or may be run in the electronic device in the form of a Software Development Kit (SDK).
As shown in fig. 1, the fake-licensed vehicle identification method specifically includes the following steps, and the sequence of the steps in the flowchart can be changed and some steps can be omitted according to different requirements.
And S11, receiving the target image of the target vehicle shot by the camera, identifying the target image and determining the first information of the target vehicle.
In this embodiment, a target image of the target vehicle is received from a plurality of preset data sources, where the target image may be an image frame of the target vehicle obtained from a captured video or a target image of the target vehicle captured by the camera, and after the target image of the target vehicle is received, the target image is identified to obtain first information of the target vehicle.
In a preferred embodiment, the identifying the target image and determining the first information of the target vehicle includes:
scanning each pixel point of the target image to obtain RGB data of each pixel point, wherein the target image is stored in a block chain node;
converting the RGB data of each pixel point into HSV space image data;
identifying the color of the license plate background color in the target image, and performing color filtering according to a license plate background color threshold value corresponding to the color of the license plate background color to obtain a filtered first image;
carrying out noise reduction processing and edge detection on the first image to obtain a second image; traversing the second image by adopting depth first to obtain a plurality of connected areas; recording the length ratio of each connected region, determining at least one target connected region according to the length-width ratios of the plurality of connected regions and a preset length-width ratio threshold, and screening out a license plate image of the target vehicle from the at least one target connected region; identifying the license plate image to obtain the license plate number of the target vehicle;
recognizing the first image to obtain the body color of the target vehicle, and recognizing the type of the first image to obtain the type recognition information of the target vehicle;
and determining the color of the license plate ground color, the license plate number, the body color and the vehicle type identification information of the target vehicle as the first information of the target vehicle.
In a preferred embodiment, the performing noise reduction processing and edge detection on the first image to obtain a second image includes:
determining a sharpness of the first image;
determining a quantity threshold value matched with the definition according to the definition;
scanning each pixel point of the first image, and comparing the RGB value of a currently scanned target pixel point with the RGB values of surrounding pixel points surrounding the target pixel point;
if the RGB value of the target pixel point is equal to the RGB values of the surrounding pixel points, determining the number of the surrounding pixel points;
if the number is smaller than the number threshold, determining the target pixel point as a noise point, and performing noise reduction processing on the noise point;
and carrying out edge detection on the first image subjected to noise reduction processing to obtain a second image.
In the embodiment, in the process of identifying the first information of the target vehicle, HSV color conversion is performed on the target image, HSV colors are filtered, specifically, color filtering is performed according to a threshold range corresponding to the color of the license plate ground color of the target vehicle, pertinence is achieved, and the accuracy of license plate number identification is improved.
In this embodiment, the filtered first image is subjected to noise reduction and edge detection, noise points are removed through noise reduction processing, the edge of the noise-reduced first image is detected, interference of objects with the same color as the ground color of the license plate around the first image is prevented, particularly the color of the vehicle body, jump interference of the color of the vehicle body can be reduced after the edge detection, the color of the vehicle body is filtered, the interference is removed, a second image is obtained, the second image is traversed by adopting depth-first, a plurality of connected regions are obtained, specifically, the connected regions are pixel sets composed of adjacent pixels with the same pixel value, as the existing license plates have rectangular characteristics, a certain length-width ratio is met, the license plate image of the target vehicle is screened out by judging whether the traversed connected regions meet a preset length-width ratio threshold value, and the license plate number of the target vehicle is identified by the license plate image, specifically, the number plate number in the number plate image can be recognized by adopting OCR, the number plate number of the target vehicle is determined, the first image is recognized, the vehicle body color and the vehicle type recognition information are obtained, and the color of the license plate ground color of the target vehicle, the number plate number, the vehicle body color and the vehicle type recognition information are determined as the first information of the target vehicle.
In the embodiment, the color is filtered according to the threshold range corresponding to the color of the license plate ground color of the target vehicle, the color is filtered according to the color of different license plate ground colors by adopting different threshold ranges, the color filtering efficiency and accuracy are improved, meanwhile, the first image is subjected to edge detection, a plurality of connected regions are obtained by traversing the second image, and the license plate images are screened out, so that the phenomenon that the license plate images are wrongly positioned and extracted due to the fact that the positions of the license plate images are positioned by adopting a projection method in the prior art can be avoided, the license plate image extraction accuracy is improved, and the accuracy of the first information extraction is further improved.
In other alternative embodiments, the first information may include any one or more of the following: the color of the license plate ground color of the target vehicle; a license plate number; the color of the vehicle body; vehicle type identification information; vehicle configuration information.
It is emphasized that, in order to further ensure the privacy and security of the target image, the target image may also be stored in a node of a block chain.
And S12, extracting corresponding registration information according to the license plate number in the first information.
In this embodiment, the registration information refers to information registered when the user purchases the target vehicle, and specifically, the registration information includes vehicle information and user information, where the vehicle information includes: and other information related to the vehicle, such as license plate number, vehicle body color, vehicle structure information, vehicle type information, and the like, wherein the registration information includes the first information.
In a preferred embodiment, said extracting corresponding registration information according to the license plate number in the first information comprises:
identifying city information in the license plate number;
acquiring a corresponding calling interface according to the city information;
and calling the registration information corresponding to the license plate number according to the license plate number and the corresponding calling interface.
In the embodiment, the system comprises a plurality of cities, each city corresponds to one calling interface, the registration information is called from the corresponding calling interface according to the city information in the license plate number, the registration information is called from the corresponding calling interface, the registration information is called with pertinence, and the calling efficiency and the accuracy of the registration information are improved.
S13, it is identified whether or not there is second information inconsistent with the registration information in the first information.
In this embodiment, the first information and the registration information are matched, and whether the target vehicle is a fake-licensed vehicle is determined according to a matching result, where the first information includes a plurality of first sub-information, and the second information includes a plurality of second sub-information.
In a preferred embodiment, the identifying whether there is second information inconsistent with the registration information in the first information includes:
matching each first sub-information in the first information with a second sub-information in the registration information;
when each piece of the first sub information is matched with a piece of second sub information in the registered information, determining that the second information inconsistent with the registered information does not exist in the first information; or
When each piece of the first sub information is not matched with any second sub information in the registered information, determining that second information inconsistent with the registered information exists in the first information.
In this embodiment, if any one of the first sub-information in the first information does not match any one of the second sub-information in the second information, it is suspected that the target vehicle is a fake-licensed vehicle.
And S14, when second information inconsistent with the registered information exists in the first information, acquiring a plurality of longitudes and latitudes of the target vehicle within a preset time period.
In this embodiment, a time period may be preset, specifically, the preset time period may be determined according to the driving road condition and the time period of the target vehicle, and different time periods are preset according to different situations, for example, when the driving time period is a peak time period and the road condition is relatively congested, the preset time period may be set to 30 minutes; when the driving time period is not the peak time period, but the road condition is relatively complex, the preset time period can be set to be 1 hour. The longitude and latitude described in this embodiment are used to characterize the driving trajectory of the vehicle.
In a preferred embodiment, the obtaining the plurality of latitudes and longitudes of the target vehicle within the preset time period includes:
acquiring a plurality of target images of the target vehicle shot by a camera within a preset time period;
extracting time information and coordinate information in the multiple target images;
mapping the coordinate information to a preset map, and updating the coordinate information according to the preset map to obtain new coordinate information;
drawing a running track of the target vehicle according to the time information and the new coordinate information;
and randomly extracting a plurality of target points from the driving track, and taking the longitude and latitude of the target points as a plurality of longitude and latitude of the target vehicle in a preset time period.
In this embodiment, a corresponding preset map is extracted according to an acquired target image of a target vehicle, and a plurality of latitudes and longitudes of the target vehicle are determined according to the preset map. Specifically, the coordinate information of the target vehicle is mapped into the preset map, the coordinate information is moved to a road of the preset map, new coordinate information corresponding to each coordinate information is obtained, a driving track of the target vehicle is drawn on the preset map according to the new coordinate information, and a plurality of target points are randomly extracted from the driving track, specifically, the target points refer to points with large influence on the driving track such as intersections and turns.
In a preferred embodiment, the obtaining the plurality of latitudes and longitudes of the target vehicle within the preset time period includes:
acquiring a plurality of target images of the target vehicle shot by a camera within a preset time period;
extracting first time information and first coordinate information in each target image;
mapping the first coordinate information to a preset map to obtain second coordinate information corresponding to the target image;
adjusting the first time information according to the preset map, the first coordinate information and the second coordinate information to obtain second time information corresponding to the target image;
generating a running track of the target vehicle according to second time information and the second coordinate information of the plurality of target images;
and randomly extracting a plurality of target points from the driving track, and taking the longitude and latitude of the target points as a plurality of longitude and latitude of the target vehicle in a preset time period.
In this embodiment, the time information and the coordinate information in the target image captured by the camera are capturing time, the coordinate information is position information of the camera, and the time information and the coordinate information are not actual time information and coordinate information of the vehicle, so that the actual time information and the coordinate information corresponding to the target vehicle are determined by mapping the coordinate information of the target vehicle to a corresponding preset map, a driving track of the target vehicle is drawn, and longitudes and latitudes of a plurality of target points are randomly extracted from the driving track as the longitudes and latitudes of the target vehicle within a preset time period, thereby improving accuracy and diversity of the longitudes and latitudes.
S15, determining a plurality of longitude and latitude deviation values of the target vehicle according to the plurality of longitude and latitude deviation values, and determining whether the target vehicle is a fake-licensed vehicle according to the plurality of longitude and latitude deviation values.
In this embodiment, the fake-licensed vehicle refers to a vehicle which is forged by lawless persons and illegally fake the number plate, the model and the color of the real fake-licensed vehicle, so that the surface of the vehicle which is smuggled, assembled, scrapped and stolen is covered with a legal coat, and whether the target vehicle is the fake-licensed vehicle is identified according to a plurality of longitudes and latitudes of the target vehicle within a preset time period.
In a preferred embodiment, said determining a plurality of latitude and longitude deviation values for said target vehicle based on said plurality of dimensions comprises:
calculating a deviation value between each longitude and latitude and the rest longitude and latitude to obtain a plurality of first deviation values;
determining the first deviation values as latitude and longitude deviation values of the target vehicle.
In this embodiment, a plurality of first deviation values are obtained by calculating a difference between each longitude and latitude and the remaining longitude and latitude, and whether the longitude and latitude with a large span appears may be determined according to the plurality of first deviation values, for example, whether the longitude and latitude appears in different provinces within a preset time period, and it is determined that the target vehicle is a fake-licensed vehicle.
In an alternative embodiment, the determining a plurality of latitude and longitude deviation values for the target vehicle based on the plurality of dimensions comprises:
calculating a deviation value between each longitude and latitude and the rest longitude and latitude to obtain a plurality of first deviation values;
determining a standard time difference between each longitude and latitude and the rest of the longitude and latitude according to a preset map;
calculating the quotient of each first deviation value and the corresponding standard time difference to obtain the running speed between each longitude and latitude and the rest longitude and latitude;
calculating a difference value between the driving speed between each longitude and latitude and the rest longitude and latitude and a speed threshold value corresponding to the longitude and latitude to obtain a plurality of second deviation values;
determining the second deviation values as longitude and latitude deviation values of the target vehicle.
In this embodiment, whether the speed of the target vehicle meets the requirement may be determined according to a preset speed threshold of each road segment corresponding to the map.
In a preferred embodiment, the determining whether the target vehicle is a fake-licensed vehicle according to the latitude and longitude deviation value includes:
comparing each longitude and latitude deviation value with a preset deviation threshold value;
when any one longitude and latitude deviation value is larger than or equal to the preset deviation threshold value, determining that the target vehicle is a fake-licensed vehicle; or
And when the longitude and latitude deviation values are smaller than the preset deviation threshold value, determining that the target vehicle is not the fake-licensed vehicle.
In this embodiment, each longitude and latitude deviation value is compared with a preset deviation threshold, whether the target vehicle is a fake-licensed vehicle is determined according to the comparison result, and if any longitude and latitude deviation value is greater than or equal to the preset deviation threshold, the target vehicle may have a longitude and latitude with a large span or a traveling speed that does not meet a requirement, and the target vehicle is determined to be a fake-licensed vehicle.
In the embodiment, whether the target vehicle is the fake-licensed vehicle or not is automatically identified by comparing the determined longitude and latitude deviation values of the target vehicle with the preset deviation threshold value, the information of a user does not need to be acquired, the information safety of a vehicle owner is ensured, and the identification efficiency and accuracy of the fake-licensed vehicle are improved.
Further, the method further comprises:
and when the target vehicle is determined to be the fake-licensed vehicle, triggering an alarm, and sending the first information to a corresponding client.
In the embodiment, when the target vehicle is determined to be the fake-licensed vehicle, automatic alarm is realized, and the related vehicle information is sent to the corresponding client side, so that the processing efficiency of the fake-licensed vehicle is improved.
In summary, in the method for identifying a fake-licensed vehicle according to the embodiment, on one hand, a plurality of longitude and latitude deviation values of the target vehicle are determined according to the plurality of longitude and latitude deviation values, whether the target vehicle is a fake-licensed vehicle is determined according to the plurality of longitude and latitude deviation values, whether the target vehicle is the fake-licensed vehicle is automatically identified by comparing the plurality of longitude and latitude deviation values of the determined target vehicle with a preset deviation threshold value, information of a user does not need to be acquired, safety of information of a vehicle owner is ensured, and identification efficiency and accuracy of the fake-licensed vehicle are improved; on the other hand, a plurality of longitudes and latitudes of the target vehicle within a preset time period are obtained, the coordinate information of the target vehicle is mapped into a corresponding preset map, actual time information and coordinate information corresponding to the target vehicle are determined, a running track of the target vehicle is drawn, and longitudes and latitudes of a plurality of target points are randomly extracted from the running track to serve as the plurality of longitudes and latitudes of the target vehicle within the preset time period, so that the accuracy and diversity of the plurality of longitudes and latitudes are improved; and finally, extracting corresponding registration information according to the license plate number in the first information, and calling the registration information from a corresponding calling interface through the city information in the license plate number, so that the registration information is called in a targeted manner, and the calling efficiency and accuracy of the registration information are improved.
Example two
Fig. 2 is a structural diagram of a fake-licensed vehicle identification apparatus according to a second embodiment of the present invention.
In some embodiments, the fake-licensed vehicle identification device 20 may include a plurality of functional modules comprised of program code segments. Program code for various program segments in the fake-licensed vehicle identification apparatus 20 may be stored in a memory of the electronic device and executed by the at least one processor to perform (see detailed description of fig. 1) the functions of fake-licensed vehicle identification.
In this embodiment, the fake-licensed vehicle identification device 20 may be divided into a plurality of functional modules according to the functions performed by the fake-licensed vehicle identification device. The functional module may include: the device comprises a receiving module 201, an extracting module 202, a recognizing module 203, an obtaining module 204, a determining module 205 and a sending module 206. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
The receiving module 201 is configured to receive a target image of a target vehicle captured by a camera, identify the target image, and determine first information of the target vehicle.
In this embodiment, a target image of the target vehicle is received from a plurality of preset data sources, where the target image may be an image frame of the target vehicle obtained from a captured video or a target image of the target vehicle captured by the camera, and after the target image of the target vehicle is received, the target image is identified to obtain first information of the target vehicle.
In a preferred embodiment, the receiving module 201 recognizing the target image and determining the first information of the target vehicle includes:
scanning each pixel point of the target image to obtain RGB data of each pixel point, wherein the target image is stored in a block chain node;
converting the RGB data of each pixel point into HSV space image data;
identifying the color of the license plate background color in the target image, and performing color filtering according to a license plate background color threshold value corresponding to the color of the license plate background color to obtain a filtered first image;
carrying out noise reduction processing and edge detection on the first image to obtain a second image; traversing the second image by adopting depth first to obtain a plurality of connected areas; recording the length ratio of each connected region, determining at least one target connected region according to the length-width ratios of the plurality of connected regions and a preset length-width ratio threshold, and screening out a license plate image of the target vehicle from the at least one target connected region; identifying the license plate image to obtain the license plate number of the target vehicle;
recognizing the first image to obtain the body color of the target vehicle, and recognizing the type of the first image to obtain the type recognition information of the target vehicle;
and determining the color of the license plate ground color, the license plate number, the body color and the vehicle type identification information of the target vehicle as the first information of the target vehicle.
In a preferred embodiment, the performing noise reduction processing and edge detection on the first image to obtain a second image includes:
determining a sharpness of the first image;
determining a quantity threshold value matched with the definition according to the definition;
scanning each pixel point of the first image, and comparing the RGB value of a currently scanned target pixel point with the RGB values of surrounding pixel points surrounding the target pixel point;
if the RGB value of the target pixel point is equal to the RGB values of the surrounding pixel points, determining the number of the surrounding pixel points;
if the number is smaller than the number threshold, determining the target pixel point as a noise point, and performing noise reduction processing on the noise point;
and carrying out edge detection on the first image subjected to noise reduction processing to obtain a second image.
In the embodiment, in the process of identifying the first information of the target vehicle, HSV color conversion is performed on the target image, HSV colors are filtered, specifically, color filtering is performed according to a threshold range corresponding to the color of the license plate ground color of the target vehicle, pertinence is achieved, and the accuracy of license plate number identification is improved.
In this embodiment, the filtered first image is subjected to noise reduction and edge detection, noise points are removed through noise reduction processing, the edge of the noise-reduced first image is detected, interference of objects with the same color as the ground color of the license plate around the first image is prevented, particularly the color of the vehicle body, jump interference of the color of the vehicle body can be reduced after the edge detection, the color of the vehicle body is filtered, the interference is removed, a second image is obtained, the second image is traversed by adopting depth-first, a plurality of connected regions are obtained, specifically, the connected regions are pixel sets composed of adjacent pixels with the same pixel value, as the existing license plates have rectangular characteristics, a certain length-width ratio is met, the license plate image of the target vehicle is screened out by judging whether the traversed connected regions meet a preset length-width ratio threshold value, and the license plate number of the target vehicle is identified by the license plate image, specifically, the number plate number in the number plate image can be recognized by adopting OCR, the number plate number of the target vehicle is determined, the first image is recognized, the vehicle body color and the vehicle type recognition information are obtained, and the color of the license plate ground color of the target vehicle, the number plate number, the vehicle body color and the vehicle type recognition information are determined as the first information of the target vehicle.
In the embodiment, the color is filtered according to the threshold range corresponding to the color of the license plate ground color of the target vehicle, the color is filtered according to the color of different license plate ground colors by adopting different threshold ranges, the color filtering efficiency and accuracy are improved, meanwhile, the first image is subjected to edge detection, a plurality of connected regions are obtained by traversing the second image, and the license plate images are screened out, so that the phenomenon that the license plate images are wrongly positioned and extracted due to the fact that the positions of the license plate images are positioned by adopting a projection method in the prior art can be avoided, the license plate image extraction accuracy is improved, and the accuracy of the first information extraction is further improved.
In other alternative embodiments, the first information may include any one or more of the following: the color of the license plate ground color of the target vehicle; a license plate number; the color of the vehicle body; vehicle type identification information; vehicle configuration information.
It is emphasized that, in order to further ensure the privacy and security of the target image, the target image may also be stored in a node of a block chain.
And the extracting module 202 is configured to extract corresponding registration information according to the license plate number in the first information.
In this embodiment, the registration information refers to information registered when the user purchases the target vehicle, and specifically, the registration information includes vehicle information and user information, where the vehicle information includes: and other information related to the vehicle, such as license plate number, vehicle body color, vehicle structure information, vehicle type information, and the like, wherein the registration information includes the first information.
In a preferred embodiment, the extracting module 202 extracts the corresponding registration information according to the license plate number in the first information includes:
identifying city information in the license plate number;
acquiring a corresponding calling interface according to the city information;
and calling the registration information corresponding to the license plate number according to the license plate number and the corresponding calling interface.
In the embodiment, the system comprises a plurality of cities, each city corresponds to one calling interface, the registration information is called from the corresponding calling interface according to the city information in the license plate number, the registration information is called from the corresponding calling interface, the registration information is called with pertinence, and the calling efficiency and the accuracy of the registration information are improved.
An identifying module 203, configured to identify whether there is second information inconsistent with the registration information in the first information.
In this embodiment, the first information and the registration information are matched, and whether the target vehicle is a fake-licensed vehicle is determined according to a matching result, where the first information includes a plurality of first sub-information, and the second information includes a plurality of second sub-information.
In a preferred embodiment, the identifying module 203 identifies whether there is second information inconsistent with the registration information in the first information includes:
matching each first sub-information in the first information with a second sub-information in the registration information;
when each piece of the first sub information is matched with a piece of second sub information in the registered information, determining that the second information inconsistent with the registered information does not exist in the first information; or
When each piece of the first sub information is not matched with any second sub information in the registered information, determining that second information inconsistent with the registered information exists in the first information.
In this embodiment, if any one of the first sub-information in the first information does not match any one of the second sub-information in the second information, it is suspected that the target vehicle is a fake-licensed vehicle.
An obtaining module 204, configured to obtain multiple longitudes and latitudes of the target vehicle within a preset time period when second information inconsistent with the registration information exists in the first information.
In this embodiment, a time period may be preset, specifically, the preset time period may be determined according to the driving road condition and the time period of the target vehicle, and different time periods are preset according to different situations, for example, when the driving time period is a peak time period and the road condition is relatively congested, the preset time period may be set to 30 minutes; when the driving time period is not the peak time period, but the road condition is relatively complex, the preset time period can be set to be 1 hour. The longitude and latitude described in this embodiment are used to characterize the driving trajectory of the vehicle.
In a preferred embodiment, the obtaining module 204 obtains the plurality of latitudes and longitudes of the target vehicle within the preset time period by:
acquiring a plurality of target images of the target vehicle shot by a camera within a preset time period;
extracting time information and coordinate information in the multiple target images;
mapping the coordinate information to a preset map, and updating the coordinate information according to the preset map to obtain new coordinate information;
drawing a running track of the target vehicle according to the time information and the new coordinate information;
and randomly extracting a plurality of target points from the driving track, and taking the longitude and latitude of the target points as a plurality of longitude and latitude of the target vehicle in a preset time period.
In this embodiment, a corresponding preset map is extracted according to an acquired target image of a target vehicle, and a plurality of latitudes and longitudes of the target vehicle are determined according to the preset map. Specifically, the coordinate information of the target vehicle is mapped into the preset map, the coordinate information is moved to a road of the preset map, new coordinate information corresponding to each coordinate information is obtained, a driving track of the target vehicle is drawn on the preset map according to the new coordinate information, and a plurality of target points are randomly extracted from the driving track, specifically, the target points refer to points with large influence on the driving track such as intersections and turns.
In a preferred embodiment, the obtaining module 204 obtains the plurality of latitudes and longitudes of the target vehicle within the preset time period by:
acquiring a plurality of target images of the target vehicle shot by a camera within a preset time period;
extracting first time information and first coordinate information in each target image;
mapping the first coordinate information to a preset map to obtain second coordinate information corresponding to the target image;
adjusting the first time information according to the preset map, the first coordinate information and the second coordinate information to obtain second time information corresponding to the target image;
generating a running track of the target vehicle according to second time information and the second coordinate information of the plurality of target images;
and randomly extracting a plurality of target points from the driving track, and taking the longitude and latitude of the target points as a plurality of longitude and latitude of the target vehicle in a preset time period.
In this embodiment, the time information and the coordinate information in the target image captured by the camera are capturing time, the coordinate information is position information of the camera, and the time information and the coordinate information are not actual time information and coordinate information of the vehicle, so that the actual time information and the coordinate information corresponding to the target vehicle are determined by mapping the coordinate information of the target vehicle to a corresponding preset map, a driving track of the target vehicle is drawn, and longitudes and latitudes of a plurality of target points are randomly extracted from the driving track as the longitudes and latitudes of the target vehicle within a preset time period, thereby improving accuracy and diversity of the longitudes and latitudes.
A determining module 205, configured to determine a plurality of longitude and latitude deviation values of the target vehicle according to the plurality of longitude and latitude dimensions, and determine whether the target vehicle is a fake-licensed vehicle according to the plurality of longitude and latitude deviation values.
In this embodiment, the fake-licensed vehicle refers to a vehicle which is forged by lawless persons and illegally fake the number plate, the model and the color of the real fake-licensed vehicle, so that the surface of the vehicle which is smuggled, assembled, scrapped and stolen is covered with a legal coat, and whether the target vehicle is the fake-licensed vehicle is identified according to a plurality of longitudes and latitudes of the target vehicle within a preset time period.
In a preferred embodiment, the determining module 205 determines the plurality of latitude and longitude deviation values for the target vehicle based on the plurality of dimensions comprises:
calculating a deviation value between each longitude and latitude and the rest longitude and latitude to obtain a plurality of first deviation values;
determining the first deviation values as latitude and longitude deviation values of the target vehicle.
In this embodiment, a plurality of first deviation values are obtained by calculating a difference between each longitude and latitude and the remaining longitude and latitude, and whether the longitude and latitude with a large span appears may be determined according to the plurality of first deviation values, for example, whether the longitude and latitude appears in different provinces within a preset time period, and it is determined that the target vehicle is a fake-licensed vehicle.
In an alternative embodiment, the determining module 205 determines the plurality of latitude and longitude deviation values for the target vehicle based on the plurality of dimensions includes:
calculating a deviation value between each longitude and latitude and the rest longitude and latitude to obtain a plurality of first deviation values;
determining a standard time difference between each longitude and latitude and the rest of the longitude and latitude according to a preset map;
calculating the quotient of each first deviation value and the corresponding standard time difference to obtain the running speed between each longitude and latitude and the rest longitude and latitude;
calculating a difference value between the driving speed between each longitude and latitude and the rest longitude and latitude and a speed threshold value corresponding to the longitude and latitude to obtain a plurality of second deviation values;
determining the second deviation values as longitude and latitude deviation values of the target vehicle.
In this embodiment, whether the speed of the target vehicle meets the requirement may be determined according to a preset speed threshold of each road segment corresponding to the map.
In a preferred embodiment, the determining module 205 determines whether the target vehicle is a fake-licensed vehicle according to the latitude and longitude deviation value comprises:
comparing each longitude and latitude deviation value with a preset deviation threshold value;
when any one longitude and latitude deviation value is larger than or equal to the preset deviation threshold value, determining that the target vehicle is a fake-licensed vehicle; or
And when the longitude and latitude deviation values are smaller than the preset deviation threshold value, determining that the target vehicle is not the fake-licensed vehicle.
In this embodiment, each longitude and latitude deviation value is compared with a preset deviation threshold, whether the target vehicle is a fake-licensed vehicle is determined according to the comparison result, and if any longitude and latitude deviation value is greater than or equal to the preset deviation threshold, the target vehicle may have a longitude and latitude with a large span or a traveling speed that does not meet a requirement, and the target vehicle is determined to be a fake-licensed vehicle.
In the embodiment, whether the target vehicle is the fake-licensed vehicle or not is automatically identified by comparing the determined longitude and latitude deviation values of the target vehicle with the preset deviation threshold value, the information of a user does not need to be acquired, the information safety of a vehicle owner is ensured, and the identification efficiency and accuracy of the fake-licensed vehicle are improved.
Further, the sending module 206 is configured to trigger an alarm when it is determined that the target vehicle is a fake-licensed vehicle, and send the first information to a corresponding client.
In the embodiment, when the target vehicle is determined to be the fake-licensed vehicle, automatic alarm is realized, and the related vehicle information is sent to the corresponding client side, so that the processing efficiency of the fake-licensed vehicle is improved.
In summary, in the fake-licensed vehicle identification apparatus of this embodiment, on one hand, a plurality of longitude and latitude deviation values of the target vehicle are determined according to the plurality of longitude and latitude deviation values, whether the target vehicle is a fake-licensed vehicle is automatically identified by comparing the plurality of longitude and latitude deviation values of the determined target vehicle with a preset deviation threshold value, information of a user does not need to be acquired, information security of a vehicle owner is ensured, and identification efficiency and accuracy of the fake-licensed vehicle are improved; on the other hand, a plurality of longitudes and latitudes of the target vehicle within a preset time period are obtained, the coordinate information of the target vehicle is mapped into a corresponding preset map, actual time information and coordinate information corresponding to the target vehicle are determined, a running track of the target vehicle is drawn, and longitudes and latitudes of a plurality of target points are randomly extracted from the running track to serve as the plurality of longitudes and latitudes of the target vehicle within the preset time period, so that the accuracy and diversity of the plurality of longitudes and latitudes are improved; and finally, extracting corresponding registration information according to the license plate number in the first information, and calling the registration information from a corresponding calling interface through the city information in the license plate number, so that the registration information is called in a targeted manner, and the calling efficiency and accuracy of the registration information are improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the electronic device 3 comprises a memory 31, at least one processor 32, at least one communication bus 33 and a transceiver 34.
It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 does not constitute a limitation of the embodiment of the present invention, and may be a bus-type configuration or a star-type configuration, and the electronic device 3 may include more or less other hardware or software than those shown, or a different arrangement of components.
In some embodiments, the electronic device 3 is an electronic device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.
It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
In some embodiments, the memory 31 is used for storing program codes and various data, such as the fake-licensed vehicle identification device 20 installed in the electronic device 3, and realizes high-speed and automatic access of programs or data during the operation of the electronic device 3. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.
In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the electronic device 3 by using various interfaces and lines, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.
In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and optionally, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.
In a further embodiment, in conjunction with fig. 2, the at least one processor 32 may execute operating means of the electronic device 3 and installed various types of applications (such as the fake-licensed vehicle identification device 20), program code, and the like, such as the various modules described above.
The memory 31 has program code stored therein, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions. For example, the various modules illustrated in fig. 2 are program code stored in the memory 31 and executed by the at least one processor 32 to perform the functions of the various modules for the purpose of fake-licensed vehicle identification.
In one embodiment of the present invention, the memory 31 stores a plurality of instructions that are executed by the at least one processor 32 to perform the functions of fake-licensed vehicle identification.
Specifically, the at least one processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, and details are not repeated here.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
Further, the computer-readable storage medium may be non-volatile or volatile.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (10)

1. A fake-licensed vehicle identification method, the method comprising:
receiving a target image of a target vehicle shot by a camera, and identifying the target image to determine first information of the target vehicle;
extracting corresponding registration information according to the license plate number in the first information;
identifying whether second information inconsistent with the registration information exists in the first information;
when second information inconsistent with the registration information exists in the first information, acquiring a plurality of longitudes and latitudes of the target vehicle within a preset time period;
determining a plurality of longitude and latitude deviation values of the target vehicle according to the plurality of longitude and latitude dimensions, and determining whether the target vehicle is a fake-licensed vehicle according to the plurality of longitude and latitude deviation values.
2. The fake-licensed vehicle identification method of claim 1, wherein said obtaining a plurality of latitudes and longitudes of the target vehicle within a preset time period comprises:
acquiring a plurality of target images of the target vehicle shot by a camera within a preset time period;
extracting time information and coordinate information in the multiple target images;
mapping the coordinate information to a preset map, and updating the coordinate information according to the preset map to obtain new coordinate information;
drawing a running track of the target vehicle according to the time information and the new coordinate information;
and randomly extracting a plurality of target points from the driving track, and taking the longitude and latitude of the target points as a plurality of longitude and latitude of the target vehicle in a preset time period.
3. The fake-licensed vehicle identification method of claim 1, wherein said obtaining a plurality of latitudes and longitudes of the target vehicle within a preset time period comprises:
acquiring a plurality of target images of the target vehicle shot by a camera within a preset time period;
extracting first time information and first coordinate information in each target image;
mapping the first coordinate information to a preset map to obtain second coordinate information corresponding to the target image;
adjusting the first time information according to the preset map, the first coordinate information and the second coordinate information to obtain second time information corresponding to the target image;
generating a running track of the target vehicle according to second time information and the second coordinate information of the plurality of target images;
and randomly extracting a plurality of target points from the driving track, and taking the longitude and latitude of the target points as a plurality of longitude and latitude of the target vehicle in a preset time period.
4. The fake-licensed vehicle identification method of claim 1, wherein the identifying the target image to determine the first information of the target vehicle comprises:
scanning each pixel point of the target image to obtain RGB data of each pixel point, wherein the target image is stored in a block chain node;
converting the RGB data of each pixel point into HSV space image data;
identifying the color of the license plate background color in the target image, and performing color filtering according to a license plate background color threshold value corresponding to the color of the license plate background color to obtain a filtered first image;
carrying out noise reduction processing and edge detection on the first image to obtain a second image; traversing the second image by adopting depth first to obtain a plurality of connected areas; recording the length ratio of each connected region, determining at least one target connected region according to the length-width ratios of the plurality of connected regions and a preset length-width ratio threshold, and screening out a license plate image of the target vehicle from the at least one target connected region; identifying the license plate image to obtain the license plate number of the target vehicle;
recognizing the first image to obtain the body color of the target vehicle, and recognizing the type of the first image to obtain the type recognition information of the target vehicle;
and determining the color of the license plate ground color, the license plate number, the body color and the vehicle type identification information of the target vehicle as the first information of the target vehicle.
5. The fake-licensed vehicle identification method of claim 1, wherein the extracting corresponding registration information according to the license plate number in the first information comprises:
identifying city information in the license plate number;
acquiring a corresponding calling interface according to the city information;
and calling the registration information corresponding to the license plate number according to the license plate number and the corresponding calling interface.
6. The fake-licensed vehicle identification method of claim 1, wherein said determining a plurality of latitude and longitude deviation values for the target vehicle based on the plurality of dimensions comprises:
calculating a deviation value between each longitude and latitude and the rest longitude and latitude to obtain a plurality of first deviation values;
determining the first deviation values as latitude and longitude deviation values of the target vehicle.
7. The fake-licensed vehicle identification method of claim 1, wherein said determining a plurality of latitude and longitude deviation values for the target vehicle based on the plurality of dimensions comprises:
calculating a deviation value between each longitude and latitude and the rest longitude and latitude to obtain a plurality of first deviation values;
determining a standard time difference between each longitude and latitude and the rest of the longitude and latitude according to a preset map;
calculating the quotient of each first deviation value and the corresponding standard time difference to obtain the running speed between each longitude and latitude and the rest longitude and latitude;
calculating a difference value between the driving speed between each longitude and latitude and the rest longitude and latitude and a speed threshold value corresponding to the longitude and latitude to obtain a plurality of second deviation values;
determining the second deviation values as longitude and latitude deviation values of the target vehicle.
8. A fake-licensed vehicle identification device, the device comprising:
the receiving module is used for receiving a target image of a target vehicle shot by a camera, identifying the target image and determining first information of the target vehicle;
the extraction module is used for extracting corresponding registration information according to the license plate number in the first information;
the identification module is used for identifying whether second information inconsistent with the registration information exists in the first information or not;
the acquisition module is used for acquiring a plurality of longitudes and latitudes of the target vehicle within a preset time period when second information inconsistent with the registration information exists in the first information;
and the determining module is used for determining a plurality of longitude and latitude deviation values of the target vehicle according to the plurality of longitude and latitude deviation values and determining whether the target vehicle is a fake-licensed vehicle or not according to the plurality of longitude and latitude deviation values.
9. An electronic device, comprising a processor and a memory, wherein the processor is configured to implement the fake-licensed vehicle identification method according to any one of claims 1 to 7 when executing a computer program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of identifying a fake-licensed vehicle according to any one of claims 1 to 7.
CN202110476419.4A 2021-04-29 2021-04-29 Fake-licensed vehicle identification method and device, electronic equipment and storage medium Pending CN113095281A (en)

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