CN110991255A - Method for detecting fake-licensed vehicle based on deep learning algorithm - Google Patents
Method for detecting fake-licensed vehicle based on deep learning algorithm Download PDFInfo
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- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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Abstract
The invention discloses a method for detecting a fake-licensed vehicle based on a deep learning algorithm, which comprises the steps of selecting a close-up image of any vehicle, and detecting and identifying all license plate information in the close-up image; detecting coordinate information of all the train heads and the train tails in the close-up image, and associating and binding the train head and train tail information with license plate number information; detecting the vehicle type name of the corresponding vehicle head and tail area of each license plate; storing the license plate information and the bound vehicle type information into a database, judging whether the vehicle types are consistent or not if the vehicle information exists in the database, and judging that the vehicle types are inconsistent to be a suspected fake-licensed vehicle; and continuously selecting the vehicle sketch to repeat the above operations. The method selects the vehicle close-up image from the high-order video for service processing, captures the fake-licensed vehicle by combining a deep learning algorithm, and has specific practical significance for guaranteeing citizen rights and fighting against illegal criminal activities.
Description
Technical Field
The invention relates to the field of computer vision and machine learning, in particular to a method for detecting a fake-licensed vehicle based on a deep learning algorithm.
Background
Along with the continuous development of social economy, the living standard of people is gradually improved, the quantity of motor vehicles in China is increased year by year, the illegal behaviors of vehicle registration are frequently generated, individual vehicle owners intentionally apply other license plate numbers to engage in the illegal behaviors, the good road traffic order is seriously interfered, the management and control of public security authorities on public safety are disturbed, the normal market economic order is disturbed, and the legal rights and interests of real vehicle owners are damaged. The flow of the fake-licensed vehicles is not allowed according to the traffic laws of China, but the problem of the fake-licensed vehicles still exists under the drive of benefits, and the situation of remedying the problem of the fake-licensed vehicles is severe.
Although some fake-licensed vehicles can be found by manual inspection, report by the masses and the like, the traditional method is time-consuming, labor-consuming and low in efficiency, the problem of the fake-licensed vehicles is solved by applying scientific technology, and with the development of deep learning in the field of image target detection and identification, mass data are processed by using an algorithm, so that a new idea and a new solution are provided for the fake-licensed capturing behavior.
Disclosure of Invention
The invention aims to provide a method for detecting a fake-licensed vehicle, which can improve the accuracy of fake-licensed vehicle detection through various deep learning algorithms.
In order to achieve the above object, the present invention provides a method for detecting a fake-licensed vehicle based on a deep learning algorithm, the method comprising:
selecting and recognizing a vehicle close-up image, and acquiring all license plate information in the vehicle close-up image;
traversing all license plate information, and acquiring the information of the vehicle head and the vehicle tail corresponding to all license plates;
binding license plate information and information of the vehicle head and the vehicle tail, and identifying the vehicle types of all vehicles;
and judging whether all the vehicle information exists in the database, if so, comparing the vehicle types, and if not, judging that the vehicle is a suspected fake-licensed vehicle.
Further, if the vehicle information does not exist in the database, the license plate information and the information of the vehicle head and the vehicle tail bound with the license plate are recorded into the database.
Further, selecting and recognizing the vehicle close-up image, and acquiring all license plate information in the vehicle close-up image comprises the following steps:
and selecting a vehicle sketch map, and calling a license plate detection and recognition algorithm to obtain vertex coordinates of all license plate positions, license plate colors, license plate numbers and license plate confidence coefficients in the vehicle sketch map.
Further, if the confidence of the license plate is higher than a set threshold, corresponding license plate information is reserved;
and if the confidence of the license plate is lower than a set threshold value, discarding the corresponding license plate information.
Further, if the confidence of the license plate is higher than a set threshold value, the close-up image of the vehicle is corrected, and the corrected information of the head and the tail of the vehicle is obtained.
Further, the step of correcting the vehicle close-up image specifically comprises the following steps: rotating the close-up image of the vehicle according to the coordinates of the four vertexes of each license plate;
calling a vehicle head and tail detection algorithm to obtain rectangular frames of regions of the vehicle head and tail and confidence degrees of information of the vehicle head and tail in the vehicle sketch map;
if the confidence of the information of the vehicle head and the vehicle tail is higher than a set threshold, the information of the vehicle head and the vehicle tail is reserved;
and if the confidence coefficient of the information of the vehicle head and the vehicle tail is lower than a set threshold value, discarding the information of the vehicle head and the vehicle tail.
Further, the method further comprises: comparing the information of the vehicle head and the vehicle tail with the license plate information, determining whether the information of the vehicle head and the vehicle tail and the license plate information are the same vehicle or not,
if the vehicle is the same vehicle, the license plate information and the information of the vehicle head and the vehicle tail are bound to form a group of vehicle information, wherein the vehicle information comprises a license plate number, a license plate color and the coordinates of the rectangular frame of the vehicle head and the vehicle tail corresponding to the license plate.
Further, comparing the information of the vehicle head and the vehicle tail with the license plate information, determining whether the information of the vehicle head and the vehicle tail and the license plate information are specifically included in the same vehicle,
comparing the position relation between the rectangular frame of each vehicle head and the vehicle tail and the four vertexes of the license plate, if the four vertexes of the license plate are all located in the rectangular frame of the vehicle head and the vehicle tail, selecting the center point of the license plate and the center point of the rectangular frame of the vehicle head and the vehicle tail, and determining that the license plate and the vehicle head and the vehicle tail belong to the same vehicle;
if the license plate vertexes are all located in the rectangular frames of the multiple vehicle heads or the vehicle tails, determining that one vehicle with the nearest distance between the license plate center point and the vehicle head and tail center points is the same vehicle;
and if the four vertexes of the license plate are not in the rectangular frame of any vehicle head or tail, discarding the license plate information.
Furthermore, if the same vehicle is confirmed, the step of confirming the group of vehicle information also comprises the step of confirming the group of vehicle information,
traversing the vehicle information, calling a vehicle type recognition algorithm, and recognizing the name of the vehicle type in the head and the tail of each license plate;
judging whether the vehicle exists in the database or not according to the license plate number and the license plate color,
if yes, comparing the vehicle types to judge the fake-licensed vehicle,
if the vehicle information does not exist, the vehicle information is input into a database, and the vehicle information comprises license plate numbers, vehicle colors and vehicle type names.
Further, the step of comparing the vehicle types and judging the fake-licensed vehicle specifically comprises the steps of confirming whether the vehicle types are consistent with the historical vehicle types in the database if historical vehicle records exist in the database, and determining the vehicle as the fake-licensed vehicle if the vehicle types are not consistent with the historical vehicle types in the database.
The method for detecting the fake-licensed vehicles based on the deep learning algorithm selects the vehicle close-up images from the high-order videos to perform business processing, identifies the information of all vehicles in the vehicle close-up images, including the positions of the license plates, the numbers of the license plates, the colors of the license plates, the positions of the front and the rear of the vehicles and the names of the vehicles, based on the deep learning algorithms such as the license plate detection and identification algorithm, the detection of the front and the rear of the vehicles, the identification of the vehicle types and the like, further judges whether the vehicles belong to the fake-licensed vehicles, provides guarantee for urban traffic management, and also provides guarantee for citizen rights and specific practical significance for fighting against illegal crimina.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting a fake-licensed vehicle based on a deep learning algorithm according to the present invention;
FIG. 2 is a schematic flow chart of a method for detecting a fake-licensed vehicle based on a deep learning algorithm according to the present invention;
FIG. 3 is a head and tail calibration chart of the method for detecting the fake-licensed vehicle based on the deep learning algorithm;
FIG. 4 is a schematic diagram of the area of the head and the tail of the vehicle obtained according to the correction map in the method for detecting the fake-licensed vehicle based on the deep learning algorithm.
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
The invention discloses a fake-licensed vehicle detecting method based on a deep learning algorithm,
specifically, as shown in fig. 1 and fig. 2, the invention discloses a method for detecting a fake-licensed vehicle based on a deep learning algorithm, which specifically comprises the following steps:
101. selecting a vehicle close-up image, detecting and identifying the close-up image, and identifying all license plate information in the vehicle close-up image;
specifically, any one vehicle close-up image Pa is selected, license plate information of a plurality of vehicles possibly exists in the vehicle close-up image, vertex coordinates of all license plate positions in the vehicle close-up image, colors of the license plates, license plate numbers and confidence degrees of the license plates are obtained by calling a license plate detection and recognition algorithm, a plurality of license plate records are formed,
if the confidence of the license plate is lower than a set threshold value, the license plate information is abandoned;
and if the confidence of the license plate is higher than a set threshold value, the license plate information is reserved.
102. Traversing all the identified license plate information, and identifying information of the vehicle head and the vehicle tail corresponding to all the license plates;
after the step 101, the reserved license plate information with high confidence coefficient is used for correcting the vehicle close-up image to obtain the corrected information of the vehicle head and the vehicle tail, and the specific correction method is as follows:
as shown in fig. 3 and 4, according to the four acquired vertex coordinates of each license plate, rotating the vehicle sketch map to enable the license plate to be in a horizontal angle, so as to be convenient for identifying the head and tail regions, and then calling a head and tail detection algorithm to acquire rectangular frames of the head and tail regions of all vehicles in the vehicle sketch map and confidence coefficients of head and tail information;
if the confidence of the information of the vehicle head and the vehicle tail is higher than a set threshold, the information of the vehicle head and the vehicle tail is reserved;
and if the confidence coefficient of the information of the vehicle head and the vehicle tail is lower than a set threshold value, discarding the information of the vehicle head and the vehicle tail.
103. Binding all license plates and information of vehicle heads and parking spaces corresponding to the license plates, and identifying vehicle types of all vehicles;
specifically, the judgment is carried out according to the coordinates of four vertexes of the license plate and the coordinates of the rectangular frames of the head and the tail of the vehicle, when the coordinates of the four vertexes of the license plate are completely positioned inside the rectangular frame of the head and the tail of the vehicle and the distance between the center point of the license plate and the center point of the head and the tail of the vehicle is the nearest, the license plate and the tail of the vehicle are determined to belong to the same vehicle, the license plate number, the license plate color and the coordinates of the rectangular frames of the head and.
And sequentially detecting the vehicle type name and the vehicle type confidence coefficient corresponding to each vehicle by using a vehicle type calling detection algorithm, and if the vehicle type confidence coefficient is lower, giving up the vehicle record and keeping the vehicle record with high vehicle type confidence coefficient.
104. And judging whether the vehicle information exists in a database, if so, comparing the vehicle types, and if not, judging that the vehicle is a suspected fake-licensed vehicle.
Specifically, the vehicle information is traversed, whether a vehicle record correspondingly exists in a database is inquired according to the license plate number and the license plate color, if the license plate number and the vehicle color record all exist in the database, the vehicle type corresponding to the license plate is compared with the existing vehicle type in the database, and if the vehicle type comparison is inconsistent, the vehicle is considered as a suspected fake-licensed vehicle.
If the license plate number vehicle color record is not in the database, the vehicle record is added into the database for the next vehicle type comparison of the vehicle, and meanwhile, the original database is enriched.
In the present invention, the pre-established database may include: a plurality of vehicle type databases, each vehicle type database comprising: a set of a plurality of sample images of the vehicle model; the sample image of the vehicle model includes: the vehicle images under different lighting conditions, the vehicle images at different shooting angles and the vehicle images at different scenes are used for ensuring the integrity of the vehicle type information.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A method for detecting fake-licensed vehicles based on a deep learning algorithm is characterized by comprising the following steps:
selecting and recognizing a vehicle close-up image, and acquiring all license plate information in the vehicle close-up image;
traversing all license plate information, and acquiring the information of the vehicle head and the vehicle tail corresponding to all license plates;
binding license plate information and information of a vehicle head and a vehicle tail, and identifying the vehicle type of the vehicle;
and judging whether the vehicle information exists in the database, if so, comparing the vehicle types, and if not, judging that the vehicle is a suspected fake-licensed vehicle.
2. The method for detecting the fake-licensed vehicle based on the deep learning algorithm of claim 1, wherein if the vehicle information does not exist in the database, the license plate information, and the information of the vehicle head, the vehicle tail and the vehicle type bound to the license plate are recorded into the database.
3. The method for detecting a fake-licensed vehicle based on the deep learning algorithm of claim 2,
selecting and recognizing the vehicle close-up image, and acquiring all license plate information in the vehicle close-up image, wherein the license plate information comprises the following steps:
by calling a license plate detection and recognition algorithm, the vertex coordinates of all license plate positions in the vehicle sketch map, the license plate color, the license plate number and the license plate confidence are obtained,
if the confidence of the license plate is higher than a set threshold, corresponding license plate information is reserved;
and if the confidence of the license plate is lower than a set threshold value, discarding the corresponding license plate information.
4. The method for detecting a fake-licensed vehicle based on the deep learning algorithm of claim 3,
and if the confidence coefficient of the license plate is higher than the set threshold value, correcting the close-up image of the vehicle, and acquiring the corrected information of the head and the tail of the vehicle.
5. The method for detecting the fake-licensed vehicle based on the deep learning algorithm of claim 4, wherein the step of correcting the vehicle close-up image specifically comprises the following steps: rotating the vehicle sketch map according to the coordinates of the four vertexes of each license plate to enable the license plate to be in a horizontal position;
calling a vehicle head and tail detection algorithm to obtain rectangular frames of regions of the vehicle head and tail and confidence degrees of information of the vehicle head and tail in the vehicle sketch map;
if the confidence of the information of the vehicle head and the vehicle tail is higher than a set threshold, the information of the vehicle head and the vehicle tail is reserved;
and if the confidence coefficient of the information of the vehicle head and the vehicle tail is lower than a set threshold value, discarding the information of the vehicle head and the vehicle tail.
6. The deep learning algorithm-based method for detecting a fake-licensed vehicle of claim 5, wherein the method further comprises: comparing the information of the vehicle head and the vehicle tail with the license plate information, determining whether the information of the vehicle head and the vehicle tail and the license plate information are the same vehicle or not,
if the vehicle is the same vehicle, the license plate information and the information of the vehicle head and the vehicle tail are bound to form a group of vehicle information, wherein the vehicle information comprises a license plate number, a license plate color and the coordinates of the rectangular frame of the vehicle head and the vehicle tail corresponding to the license plate.
7. The method of claim 6, wherein comparing the head and tail information with the license plate information to determine whether the head and tail information and the license plate information are the same vehicle comprises,
comparing the position relation between the rectangular frame of each vehicle head and the vehicle tail and the four vertexes of the license plate, and if the four vertexes of the license plate are all located in the rectangular frame of the vehicle head and the vehicle tail, determining that the license plate and the vehicle head and the vehicle tail belong to the same vehicle;
if the vertexes of the license plate are all positioned in the rectangular frames of the vehicle heads or the vehicle tails,
selecting a central point of a license plate and a central point of a rectangular frame at the head and tail of the vehicle, and identifying a vehicle with the nearest distance between the central point of the license plate and the central point of the head and tail of the vehicle as the same vehicle;
and if the four vertexes of the license plate are not in the rectangular frame of any vehicle head or tail, discarding the license plate information.
8. The method for detecting a fake-licensed vehicle based on the deep learning algorithm of claim 7,
if the vehicle is confirmed to be the same vehicle, the license plate information and the information of the vehicle head and the vehicle tail are bound to form a group of vehicle information,
traversing the vehicle information, calling a vehicle type recognition algorithm, and recognizing the name of the vehicle type in the head and the tail of each license plate;
judging whether the vehicle exists in the database or not according to the license plate number and the license plate color,
if yes, comparing the vehicle types to judge the fake-licensed vehicle,
if the vehicle information does not exist, the vehicle information is input into a database, and the vehicle information comprises license plate numbers, vehicle colors and vehicle type names.
9. The method for detecting a fake-licensed vehicle based on the deep learning algorithm of claim 8,
and comparing the vehicle types to judge the fake-licensed vehicle, specifically comprising the steps of confirming whether the vehicle types are consistent with the historical vehicle types in the database if historical vehicle records exist in the database, and determining the vehicle as the fake-licensed vehicle if the vehicle types are not consistent with the historical vehicle types in the database.
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| CN111932903A (en) * | 2020-09-02 | 2020-11-13 | 智慧互通科技有限公司 | Parking lot entrance and exit management method and system based on multiple cameras |
| CN112885108A (en) * | 2020-12-23 | 2021-06-01 | 爱泊车美好科技有限公司 | Vehicle change detection method and system on parking space based on deep learning algorithm |
| CN112885108B (en) * | 2020-12-23 | 2022-02-01 | 爱泊车美好科技有限公司 | Vehicle change detection method and system on parking space based on deep learning algorithm |
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