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CN110310488B - Large truck violation message generation method and system - Google Patents

Large truck violation message generation method and system
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CN110310488B
CN110310488BCN201910624438.XACN201910624438ACN110310488BCN 110310488 BCN110310488 BCN 110310488BCN 201910624438 ACN201910624438 ACN 201910624438ACN 110310488 BCN110310488 BCN 110310488B
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large truck
lane
license plate
plate number
expressway
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CN110310488A (en
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戚湧
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Nanjing University of Science and Technology
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Nanjing University of Science and Technology
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Abstract

Translated fromChinese

本发明属于软件领域,提供了一种大型货车违章消息生成方法及系统,方法包括:检测当前的位置坐标是否处于高速公路内;如果当前的位置坐标处于高速公路内,判断当前车道是否为高速公路的左车道;如果当前车道为高速公路的左车道,就获取摄像组朝挡风玻璃正前方区域拍摄的车辆图像以及车辆图像的拍摄时间,识别车辆图像的车牌号;将车牌号与本省的大型货车车牌号进行匹配;如果匹配成功,就识别车牌号为本省的大型货车车牌号;将预设的大型货车违章标签、大型货车车牌号、拍摄时间以及位置坐标封包,生成大型货车违章消息;向预设的管理服务器上传大型货车违章消息、大型货车违章视频以及对应关系。本发明有利于提高大型货车的监管效果。

Figure 201910624438

The invention belongs to the software field, and provides a method and system for generating a violation message of a large truck. If the current lane is the left lane of the expressway, obtain the vehicle image taken by the camera group towards the area directly in front of the windshield and the shooting time of the vehicle image, and identify the license plate number of the vehicle image; The license plate number of the truck is matched; if the match is successful, the license plate number is identified as the license plate number of the large truck in the province; the preset illegal label of the large truck, the license plate number of the large truck, the shooting time and the location coordinates are packaged to generate a large truck violation message; The preset management server uploads large truck violation messages, large truck illegal videos, and corresponding relationships. The invention is beneficial to improve the supervision effect of large trucks.

Figure 201910624438

Description

Large truck violation message generation method and system
Technical Field
The invention belongs to the field of software, and particularly relates to a method and a system for generating violation messages of large trucks.
Background
At present, when a large truck runs on a highway, the phenomenon that a lane on the left side of the highway is occupied generally exists, and more traffic accidents are caused. Because most of large trucks running on the expressway belong to heavy trucks, the total weight of the trucks is about 49 tons generally, and if the large trucks occupy lanes on the left side, once the trucks cannot be braked, destructive collision and extrusion are likely to be caused to front small-sized trucks. Furthermore, if a large truck occupies the left lane, the following small cars can only overtake from the right side, which is known to be dangerous.
However, the monitoring system installed on the highway aims at monitoring violation of all vehicle bodies on the highway, the monitoring range is too large, the monitoring system is not targeted, and the violation of regulation information of the large truck is difficult to generate.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method and a system for generating a violation message of a large truck, so as to solve the problem that the violation message of the large truck is difficult to generate in time in the prior art, so that the supervision effect of the large truck is not ideal.
In a first aspect, an embodiment of the present invention provides a method for generating a violation message of a large truck, including:
the method comprises the steps that a vehicle-mounted terminal obtains a current position coordinate and detects whether the current position coordinate is in a highway or not;
if the current position coordinate is in the highway, judging whether the current lane is the left lane of the highway or not;
if the current lane is the left lane of the expressway, triggering a shooting instruction, acquiring vehicle images shot by a camera group towards an area right in front of a windshield and shooting time of the vehicle images by utilizing the shooting instruction, and identifying license plate numbers of the vehicle images;
in a preset storage area, acquiring a pre-stored large truck license plate number of the province, and matching the license plate number with the large truck license plate number of the province;
if the license plate number is successfully matched with the license plate number of the large truck in the province, identifying the license plate number of the large truck in the province as the license plate number of the large truck in the province;
packaging a preset large truck violation label, the large truck license plate number, the shooting time and the position coordinate to generate a large truck violation message;
and establishing a corresponding relation between the large truck violation message and the large truck violation video, and uploading the large truck violation message, the large truck violation video and the corresponding relation to a preset management server.
Preferably, if the current position coordinate is located in an expressway, determining whether a current lane is a left lane of the expressway, specifically:
and if the current position coordinate is in the expressway, acquiring a road image of the expressway, carrying out lane recognition on the road image, judging that the current lane is the left lane of the expressway if the lane is recognized as the left lane, and judging that the current lane is not the left lane of the expressway if the lane is recognized as the middle lane or the right lane.
Preferably, if the current position coordinate is located in an expressway, determining whether a current lane is a left lane of the expressway, specifically:
if the current position coordinate is in the highway, acquiring an image of the left side of the vehicle body through a side-viewing camera arranged on a left rearview mirror, identifying whether a moving object exists in the image of the left side of the vehicle body by utilizing a background difference method, and meanwhile, acquiring the distance between the vehicle body and a left side rail through a distance sensor arranged on an outer handle of a left vehicle door, if the image of the left side of the vehicle body does not exist the moving object and the mean value of the distance between the vehicle body and the left side rail is smaller than a preset distance within a preset time, judging that the current lane is the left lane of the highway, and if the image of the left side of the vehicle body exists the moving object or the mean value of the distance between the vehicle body and the left side rail is not smaller than the preset distance within the preset time, judging that the current lane is not the left lane of the highway.
Preferably, if the current lane is the left lane of the expressway, a shooting instruction is triggered, and by using the shooting instruction, vehicle images shot by a camera group towards an area right in front of a windshield and shooting time of the vehicle images are acquired, and license plate numbers of the vehicle images are identified, specifically:
if the current lane is the left lane of the expressway, the current speed is obtained, whether the current speed is lower than the minimum driving speed required by the left lane of the expressway is judged, if the current speed is lower than the minimum driving speed required by the left lane of the expressway, the shooting instruction is utilized to obtain the vehicle images shot by the shooting group towards the area right in front of the windshield and the shooting time of the vehicle images, and the license plate number of the vehicle images is identified.
The method comprises the following steps of packaging a preset large truck violation label, a large truck license plate number, shooting time and the position coordinate to generate a large truck violation message, and specifically comprises the following steps:
acquiring an image frame rate, multiplying the image frame rate by preset time to obtain the number of frames of the vehicle image acquired within the preset time, acquiring the number of times of identifying the license plate number of the large truck of the province within the preset time, generating a model according to a preset truck identification reliability coefficient, the number of the acquired vehicle image and the number of times of identifying the license plate number of the large truck of the province, generating a truck identification reliability coefficient for identifying the license plate number of the large truck of the province, and packaging a preset large truck violation label, the license plate number of the large truck, shooting time and the position coordinate to generate a large truck violation message when the truck identification reliability coefficient is greater than the preset value;
the truck identification reliability coefficient generation model specifically comprises the following steps:
Figure GDA0003260225670000031
wherein V is a truck identification reliability coefficient and consists of an identification rate and an identification coefficient,
Figure GDA0003260225670000032
for the identification rate, Ni represents the number of times of identifying the license plate number of the large truck of the province within the preset time, and Frames represents the number of Frames of the acquired vehicle image within the preset time;
Figure GDA0003260225670000033
for the identification coefficient, No represents a preset number of identifications; 0<a<1、0<b<The size of 1, a + b is 1, and the weight ratio of the identification rate and the identification coefficient in the truck identification reliability coefficient is determined by the size of a and b.
Preferably, the position coordinate is a GPS coordinate or a beidou coordinate, the large Truck violation label is composed of a first character string and a second character string, the first character string is 0x1906, and the second character string is Truck.
In a second aspect, an embodiment of the present invention provides a system for generating a large truck violation message, including:
the system comprises a position coordinate acquisition module, a position coordinate acquisition module and a position coordinate detection module, wherein the position coordinate acquisition module is used for acquiring a current position coordinate and detecting whether the current position coordinate is in a highway or not;
the left lane judging module is used for judging whether the current lane is the left lane of the expressway or not if the current position coordinate is in the expressway;
the license plate number identification module is used for triggering a shooting instruction if the current lane is the left lane of the expressway, acquiring a vehicle image shot by a camera group towards an area right in front of a windshield and the shooting time of the vehicle image by utilizing the shooting instruction, and identifying the license plate number of the vehicle image;
the large truck license plate matching module is used for acquiring a pre-stored large truck license plate number of the province in a preset storage area and matching the license plate number with the large truck license plate number of the province;
the large truck license plate number identification module is used for identifying that the license plate number is the license plate number of the large truck in the province if the license plate number is successfully matched with the license plate number of the large truck in the province;
the large truck violation message generating module is used for packaging a preset large truck violation label, the large truck license plate number, the shooting time and the position coordinate to generate a large truck violation message;
and the large truck violation message uploading module is used for establishing a corresponding relation between the large truck violation message and the large truck violation video and uploading the large truck violation message, the large truck violation video and the corresponding relation to a preset management server.
Preferably, the left lane determining module is specifically configured to:
and if the current position coordinate is in the expressway, acquiring a road image of the expressway, carrying out lane recognition on the road image, judging that the current lane is the left lane of the expressway if the lane is recognized as the left lane, and judging that the current lane is not the left lane of the expressway if the lane is recognized as the middle lane or the right lane.
Preferably, the left lane determining module is specifically configured to:
if the current position coordinate is in the highway, acquiring an image of the left side of the vehicle body through a side-viewing camera arranged on a left rearview mirror, identifying whether a moving object exists in the image of the left side of the vehicle body by utilizing a background difference method, and meanwhile, acquiring the distance between the vehicle body and a left side rail through a distance sensor arranged on an outer handle of a left vehicle door, if the image of the left side of the vehicle body does not exist the moving object and the mean value of the distance between the vehicle body and the left side rail is smaller than a preset distance within a preset time, judging that the current lane is the left lane of the highway, and if the image of the left side of the vehicle body exists the moving object or the mean value of the distance between the vehicle body and the left side rail is not smaller than the preset distance within the preset time, judging that the current lane is not the left lane of the highway.
Preferably, the large truck license plate number identification module is specifically configured to: if the current lane is the left lane of the expressway, the current speed is obtained, whether the current speed is lower than the minimum driving speed required by the left lane of the expressway is judged, if the current speed is lower than the minimum driving speed required by the left lane of the expressway, the shooting instruction is utilized to obtain the vehicle images shot by the shooting group towards the area right in front of the windshield and the shooting time of the vehicle images, and the license plate number of the vehicle images is identified.
Preferably, the large truck violation message generating module is specifically configured to: acquiring an image frame rate, multiplying the image frame rate by preset time to obtain the number of frames of the vehicle image acquired within the preset time, acquiring the number of times of identifying the license plate number of the large truck of the province within the preset time, generating a model according to a preset truck identification reliability coefficient, the number of the acquired vehicle image and the number of times of identifying the license plate number of the large truck of the province, generating a truck identification reliability coefficient for identifying the license plate number of the large truck of the province, and packaging a preset large truck violation label, the license plate number of the large truck, shooting time and the position coordinate to generate a large truck violation message when the truck identification reliability coefficient is greater than the preset value;
the truck identification reliability coefficient generation model specifically comprises the following steps:
Figure GDA0003260225670000051
wherein V is a truck identification reliability coefficient and consists of an identification rate and an identification coefficient,
Figure GDA0003260225670000061
for the identification rate, Ni represents the number of times of identifying the license plate number of the large truck of the province within the preset time, and Frames represents the number of Frames of the acquired vehicle image within the preset time;
Figure GDA0003260225670000062
for the identification coefficient, No represents a preset number of identifications; 0<a<1、0<b<The size of 1, a + b is 1, and the weight ratio of the identification rate and the identification coefficient in the truck identification reliability coefficient is determined by the size of a and b.
Compared with the prior art, the method and the system for generating the violation messages of the large trucks solve the problem that the violation messages of the large trucks are difficult to generate in time and the supervision effect of the large trucks is not ideal in the prior art.
Drawings
FIG. 1 is a flow chart of an implementation of a method for generating a violation message of a large truck according to an embodiment of the present invention;
fig. 2 is a block diagram of a large truck violation message generating system according to an embodiment of the present invention.
Detailed Description
Referring to fig. 1, fig. 1 is a flowchart of an implementation of a method for generating a violation message of a large truck according to an embodiment of the present invention, where the method is applied to a terminal, and the method for generating a violation message of a large truck shown in fig. 1 may include the following steps:
s101, a vehicle-mounted terminal acquires a current position coordinate and detects whether the current position coordinate is in a highway or not;
the vehicle-mounted terminal is front-end equipment of the vehicle body monitoring and management system, is connected with the camera group, is arranged in a vehicle and is a vehicle event data recorder and a mobile phone.
S102, if the current position coordinate is in the expressway, judging whether a current lane is a left lane of the expressway;
wherein, S102 has two implementation manners, which are detailed as follows:
in a first implementation manner, if the current position coordinate is in a highway, a road image of the highway is acquired, lane recognition is performed on the road image, if the lane is recognized as a left lane, the current lane is judged to be the left lane of the highway, and if the lane is recognized as a middle lane or a right lane, the current lane is judged not to be the left lane of the highway.
The lane recognition algorithm is the prior art, and is not described herein.
In a second implementation manner, if the current position coordinate is in the highway, acquiring an image of the left side of the vehicle body through a side view camera installed on a left rearview mirror, identifying whether a moving object exists in the image of the left side of the vehicle body by using a background difference method, and meanwhile acquiring the distance between the vehicle body and a left side rail through a distance sensor installed on an outer handle of a left vehicle door, if the image of the left side of the vehicle body does not exist a moving object and the mean value of the distance between the vehicle body and the left side rail is smaller than a preset distance within a preset time, determining that the current lane is the left lane of the highway, and if the image of the left side of the vehicle body exists a moving object or the mean value of the distance between the vehicle body and the left side rail is not smaller than a preset distance within a preset time, determining that the current lane is not the left lane of the highway.
The preset time and the preset distance are set by the user or default of the system, and are not limited herein.
If no moving object exists in the image of the left side of the vehicle body within the preset time and the average value of the distances between the vehicle body and the left side rail is smaller than the preset distance, the fact that the vehicle body of the vehicle body is close to the left side rail and is located in a left lane of the expressway is shown.
Wherein, preferably, the preset distance is 1.95 meters, and the preset time is 10 seconds. The preset focal length is 2.6 mm. Because the lane of highway is generally 3.75 meters, the dolly width is about 1.80 meters, the big car width is about 2.50 meters, the focal length of mobile unit automatically regulated looks at the camera and is 2.6 millimeters, can control the shooting range that looks at the camera and cover the lane of automobile body left side 1.95 meters, in this way, just can acquire and acquire the image on the left of the automobile body.
S103, if the current lane is the left lane of the expressway, triggering a shooting instruction, acquiring vehicle images shot by a camera group towards an area right in front of a windshield and shooting time of the vehicle images by using the shooting instruction, and identifying license plate numbers of the vehicle images;
if the current lane is the left lane of the expressway, the current speed is obtained, whether the current speed is lower than the minimum driving speed required by the left lane of the expressway is judged, if the current speed is lower than the minimum driving speed required by the left lane of the expressway, the shooting instruction is utilized to obtain vehicle images shot by a shooting group towards an area right in front of a windshield and the shooting time of the vehicle images, whether the color of the license plate of the vehicle image is yellow is identified, and if the license plate of the vehicle image is yellow, the license plate number of the vehicle image is identified.
The large truck and the small truck can be directly distinguished by using license plate recognition software to detect the color of the license plate, and license plate number recognition is carried out after the large truck is distinguished, so that the beneficial effects are that: the license plate number of the non-large truck does not need to be identified, and the data for identifying the license plate number of the non-large truck is reduced, so that the data volume processed by the vehicle-mounted terminal can be greatly reduced.
If the current vehicle speed is lower than the minimum driving speed required by the left lane of the expressway, the fact that an obstacle exists in front is indicated, therefore, the current driving state is determined by judging the current vehicle speed of the vehicle body, and preparation is made for identifying license plates.
S104, in a preset storage area, obtaining a pre-stored large truck license plate number of the province, and matching the license plate number with the large truck license plate number of the province;
compared with the large truck license plate number stored in the country, the pre-stored large truck license plate number in the province can greatly reduce the time required for matching and effectively improve the matching efficiency of the large truck license plate number.
S105, if the license plate number is successfully matched with the license plate number of the large truck in the province, identifying the license plate number of the large truck in the province as the license plate number of the large truck in the province;
s106, packaging a preset large truck violation label, the license plate number of the large truck, the shooting time and the position coordinate to generate a large truck violation message;
wherein, S106 specifically is:
acquiring an image frame rate, multiplying the image frame rate by preset time to obtain the number of frames of the vehicle image acquired within the preset time, acquiring the number of times of identifying the license plate number of the large truck of the province within the preset time, generating a model according to a preset truck identification reliability coefficient, the number of the acquired vehicle image and the number of times of identifying the license plate number of the large truck of the province, generating a truck identification reliability coefficient for identifying the license plate number of the large truck of the province, and packaging a preset large truck violation label, the license plate number of the large truck, shooting time and the position coordinate to generate a large truck violation message when the truck identification reliability coefficient is greater than the preset value;
the truck identification reliability coefficient generation model specifically comprises the following steps:
Figure GDA0003260225670000081
wherein V is a truck identification reliability coefficient and consists of an identification rate and an identification coefficient,
Figure GDA0003260225670000091
for the identification rate, Ni represents the number of times of identifying the license plate number of the large truck of the province within the preset time, and Frames represents the number of Frames of the acquired vehicle image within the preset time;
Figure GDA0003260225670000092
for the identification coefficient, No represents a preset number of identifications; 0<a<1、0<b<The size of 1, a + b is 1, and the weight ratio of the identification rate and the identification coefficient in the truck identification reliability coefficient is determined by the size of a and b.
The truck identification reliability coefficient is used for describing the reliability of identifying the license plate number of the large truck of the province, the truck identification reliability coefficient is higher and more reliable, and otherwise, the truck identification reliability coefficient is lower and more unreliable.
The identification rate and the identification coefficient are larger, the truck identification reliability coefficient is higher, and therefore error identification can be effectively avoided, the license plate number of the large truck of the province is correct, and the generated violation message of the large truck is more reliable.
The preset value and the preset time are set by the user or default by the system, and are not limited herein.
The position coordinate is a GPS coordinate or a Beidou coordinate, the large Truck violation label is composed of a first character string and a second character string, the first character string is 0x1906, and the second character string is Truck.
The large truck violation label is stored in a fixed position of the large truck violation message and used for informing the management server that the license plate number, the shooting time and the position coordinate of the large truck are behind the large truck violation label.
S107, establishing a corresponding relation between the large truck violation message and the large truck violation video, and uploading the large truck violation message, the large truck violation video and the corresponding relation to a preset management server.
It should be noted that the management server obtains factory-set automatic scanning parameters, the automatic scanning parameters comprise a time interval and an information interface, the time interval is extracted from the automatic scanning parameters, a timer generates a scanning instruction of the information interface according to the time interval, whether data uploaded by a vehicle-mounted terminal exists in the information interface is scanned according to the scanning instruction, when the data uploaded by the vehicle-mounted terminal exists in the information interface, the data uploaded by the vehicle-mounted terminal is processed through Deep Packet Inspection (DPI) technology, so that the large truck violation message, the large truck violation video and the corresponding relation are obtained, then the large truck violation message is transmitted to a decision interface by the information interface for processing, then the decision interface transmits the large truck violation message to an LED display screen on a highway with the position coordinate according to the position coordinate in the large truck violation message, to alert the driver of the vehicle driving the large truck license plate number. Therefore, the violation information of the large truck can be obtained in time, and the driver driving the large truck license plate can be reminded in time.
And establishing a corresponding relation between the large truck violation message and the large truck violation video, and uploading the large truck violation message, the large truck violation video and the corresponding relation to a preset management server by adopting a 4G network mode or a 5G network mode.
In the embodiment of the invention, the preset violation label of the large truck, the license plate number of the large truck, the shooting time and the position coordinate are packaged to generate the violation message of the large truck, so that the violation message of the large truck can be generated in time, evidence is provided for the violation judgment of the large truck, and the monitoring effect of the large truck is improved.
Referring to fig. 2, fig. 2 is a block diagram of a large truck violation message generating system according to an embodiment of the present invention, where the large truck violation message generating system includes:
a position coordinate obtainingmodule 21, configured to obtain a current position coordinate, and detect whether the current position coordinate is located in a highway;
a leftlane determining module 22, configured to determine whether the current lane is a left lane of the highway if the current position coordinate is located in the highway;
the license platenumber identification module 23 is configured to trigger a shooting instruction if the current lane is the left lane of the highway, acquire, by using the shooting instruction, a vehicle image shot by a camera group toward an area right in front of a windshield and shooting time of the vehicle image, and identify a license plate number of the vehicle image;
the large truck license platenumber matching module 24 is used for acquiring a pre-stored large truck license plate number of the province in a preset storage area and matching the license plate number with the license plate number of the large truck of the province;
a large truck license platenumber identification module 25, configured to identify that the license plate number is a large truck license plate number of the province if the license plate number is successfully matched with the license plate number of the local province;
the large truck violationmessage generating module 26 is configured to encapsulate a preset large truck violation label, the large truck license plate number, the shooting time and the position coordinate to generate a large truck violation message;
and the large truck violationmessage uploading module 27 is used for establishing a corresponding relationship between the large truck violation message and the large truck violation video, and uploading the large truck violation message, the large truck violation video and the corresponding relationship to a preset management server.
In the embodiment of the invention, the preset violation label of the large truck, the license plate number of the large truck, the shooting time and the position coordinate are packaged to generate the violation message of the large truck, so that the violation message of the large truck can be generated in time, evidence is provided for the violation judgment of the large truck, and the monitoring effect of the large truck is improved.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.

Claims (8)

Translated fromChinese
1.一种大型货车违章消息生成方法,其特征在于,包括:1. a large-scale truck violation message generation method, is characterized in that, comprises:车载终端获取当前的位置坐标,检测所述当前的位置坐标是否处于高速公路内;The in-vehicle terminal obtains the current position coordinates, and detects whether the current position coordinates are within the expressway;如果所述当前的位置坐标处于高速公路内,判断当前车道是否为所述高速公路的左车道;If the current position coordinates are within the expressway, determine whether the current lane is the left lane of the expressway;如果所述当前车道为所述高速公路的左车道,就触发拍摄指令,利用所述拍摄指令,获取摄像组朝挡风玻璃正前方区域拍摄的车辆图像以及所述车辆图像的拍摄时间,识别所述车辆图像的车牌号;If the current lane is the left lane of the expressway, a shooting instruction is triggered, and the shooting instruction is used to obtain the vehicle image taken by the camera group towards the area directly in front of the windshield and the shooting time of the vehicle image, and identify all the vehicle images. the license plate number of the vehicle image;在预设的存储区域中,获取预存的本省的大型货车车牌号,将所述车牌号与所述本省的大型货车车牌号进行匹配;In the preset storage area, obtain the pre-stored license plate number of the large truck in the province, and match the license plate number with the license plate number of the large truck in the province;如果所述车牌号与所述本省的大型货车车牌号匹配成功,就识别所述车牌号为本省的大型货车车牌号;If the license plate number is successfully matched with the license plate number of the large truck in the province, the license plate number is identified as the license plate number of the large truck in the province;将预设的大型货车违章标签、所述大型货车车牌号、拍摄时间以及所述位置坐标封包,生成大型货车违章消息;Packing the preset large truck violation label, the large truck license plate number, the shooting time and the location coordinates to generate a large truck violation message;建立所述大型货车违章消息以及所述大型货车违章视频之间的对应关系,向预设的管理服务器上传所述大型货车违章消息、所述大型货车违章视频以及所述对应关系;establishing a correspondence between the large truck violation message and the large truck illegal video, and uploading the large truck illegal message, the large truck illegal video, and the corresponding relationship to a preset management server;所述将预设的大型货车违章标签、所述大型货车车牌号、拍摄时间以及所述位置坐标封包,生成大型货车违章消息,具体为:The large truck violation message is generated by encapsulating the preset large truck violation label, the large truck license plate number, the shooting time, and the location coordinates, specifically:获取图像帧率,将图像帧率和预设时间相乘,得到预设时间内获取到的所述车辆图像的帧数,获取预设时间内识别所述车牌号为本省的大型货车车牌号的次数,根据预设的货车识别可靠系数生成模型、获取到的所述车辆图像的帧数以及识别所述车牌号为本省的大型货车车牌号的次数,生成识别所述车牌号为本省的大型货车车牌号的货车识别可靠系数,当货车识别可靠系数大于预设值时,将预设的大型货车违章标签、所述大型货车车牌号、拍摄时间以及所述位置坐标封包,生成大型货车违章消息;Obtain the image frame rate, multiply the image frame rate and the preset time, obtain the frame number of the vehicle image obtained within the preset time, and obtain the license plate number of the large truck that identifies the province within the preset time. The number of times, according to the preset truck identification reliability coefficient generation model, the obtained frame number of the vehicle image, and the number of times of recognizing the license plate number of the large truck in the province, generate the large truck with the license plate number in the province. The truck identification reliability coefficient of the license plate number. When the truck identification reliability coefficient is greater than the preset value, the preset large truck violation label, the large truck license plate number, the shooting time and the location coordinates are packaged to generate a large truck violation message;其中,货车识别可靠系数生成模型具体为:Among them, the generation model of the reliability coefficient of truck identification is specifically:
Figure FDA0003260225660000021
Figure FDA0003260225660000021
其中,V为货车识别可靠系数,V由识别率和识别系数两部分组成,
Figure FDA0003260225660000022
为识别率,Ni表示预设时间内,识别所述车牌号为本省的大型货车车牌号的次数,Frames表示预设时间内,获取到的所述车辆图像的帧数;
Figure FDA0003260225660000023
为识别系数,No表示预设的识别次数;0<a<1、0<b<1,a+b=1,a和b的大小分别决定着识别率和识别系数在货车识别可靠系数中的权重比。
Among them, V is the recognition reliability coefficient of the truck, and V is composed of two parts: the recognition rate and the recognition coefficient.
Figure FDA0003260225660000022
is the recognition rate, Ni represents the number of times of identifying the license plate number of the large truck in the province within a preset time, and Frames represents the number of frames of the vehicle image obtained within a preset time;
Figure FDA0003260225660000023
For the recognition coefficient, No represents the preset recognition times; 0<a<1, 0<b<1, a+b=1, the sizes of a and b respectively determine the recognition rate and the recognition coefficient in the truck recognition reliability coefficient. weight ratio.
2.如权利要求1所述的方法,其特征在于,所述如果所述当前的位置坐标处于高速公路内,判断当前车道是否为所述高速公路的左车道,具体为:2. The method according to claim 1, wherein, if the current position coordinates are in the expressway, judging whether the current lane is the left lane of the expressway, specifically:如果所述当前的位置坐标处于高速公路内,获取所述高速公路的道路图像,对所述道路图像进行车道识别,如果车道识别为左车道,就判断当前车道为所述高速公路的左车道,如果车道识别为中间车道或右车道,就判断当前车道不为所述高速公路的左车道。If the current position coordinates are in the expressway, obtain a road image of the expressway, and perform lane recognition on the road image, and if the lane is identified as the left lane, determine that the current lane is the left lane of the expressway, If the lane is identified as the middle lane or the right lane, it is determined that the current lane is not the left lane of the expressway.3.如权利要求1所述的方法,其特征在于,所述如果所述当前的位置坐标处于高速公路内,判断当前车道是否为所述高速公路的左车道,具体为:3. The method according to claim 1, wherein, if the current position coordinates are in the expressway, judging whether the current lane is the left lane of the expressway, specifically:如果所述当前的位置坐标处于高速公路内,通过安装在左后视镜的侧视摄像头,获取车身左边的图像,利用背景差分法,识别所述车身左边的图像是否存在移动物体,同时通过安装在左车门外把手的距离传感器,获取车身离左侧栏杆的距离,如果在预设时间内,所述车身左边的图像不存在移动物体且所述车身离左侧栏杆的距离的均值小于预设距离,就判断当前车道为所述高速公路的左车道,如果在预设时间内,所述车身左边的图像存在移动物体或所述车身离左侧栏杆的距离的均值不小于预设距离,就判断当前车道不为所述高速公路的左车道。If the current position coordinates are in the expressway, the image on the left side of the vehicle body is obtained through the side-view camera installed on the left rearview mirror, and the background difference method is used to identify whether there is a moving object in the image on the left side of the vehicle body. The distance sensor on the outside handle of the left door obtains the distance between the vehicle body and the left railing. If there is no moving object in the image on the left side of the vehicle body within the preset time, and the average value of the distance between the vehicle body and the left railing is less than the preset value If the current lane is the left lane of the expressway, if there is a moving object in the image on the left side of the vehicle body or the average distance between the vehicle body and the left rail is not less than the preset distance, then It is determined that the current lane is not the left lane of the expressway.4.如权利要求1所述的方法,其特征在于,所述如果所述当前车道为所述高速公路的左车道,就触发拍摄指令,利用所述拍摄指令,获取摄像组朝挡风玻璃正前方区域拍摄的车辆图像以及所述车辆图像的拍摄时间,识别所述车辆图像的车牌号,具体为:4 . The method according to claim 1 , wherein if the current lane is the left lane of the expressway, a shooting instruction is triggered, and the shooting instruction is used to obtain the direction of the camera group facing the windshield. 5 . The vehicle image taken in the front area and the shooting time of the vehicle image, and the license plate number of the vehicle image is identified, specifically:如果所述当前车道为所述高速公路的左车道,获取当前车速,判断当前车速是否低于所述高速公路的左车道要求的最小行驶速度,如果所述当前车速低于所述高速公路的左车道要求的最小行驶速度,就利用所述拍摄指令,获取摄像组朝挡风玻璃正前方区域拍摄的车辆图像以及所述车辆图像的拍摄时间,识别所述车辆图像的车牌号。If the current lane is the left lane of the expressway, obtain the current vehicle speed and judge whether the current vehicle speed is lower than the minimum driving speed required by the left lane of the expressway, if the current vehicle speed is lower than the left lane of the expressway The minimum driving speed required by the lane is used to obtain the vehicle image taken by the camera group towards the area directly in front of the windshield and the shooting time of the vehicle image by using the shooting instruction, and identify the license plate number of the vehicle image.5.一种大型货车违章消息生成的系统,其特征在于,包括:5. A system for generating large truck violation messages, characterized in that it comprises:位置坐标获取模块,用于获取当前的位置坐标,检测所述当前的位置坐标是否处于高速公路内;a location coordinate acquisition module, used to acquire the current location coordinates and detect whether the current location coordinates are in the expressway;左车道判断模块,用于如果所述当前的位置坐标处于高速公路内,判断当前车道是否为所述高速公路的左车道;a left lane judgment module, configured to judge whether the current lane is the left lane of the expressway if the current position coordinates are in the expressway;车牌号识别模块,用于如果所述当前车道为所述高速公路的左车道,就触发拍摄指令,利用所述拍摄指令,获取摄像组朝挡风玻璃正前方区域拍摄的车辆图像以及所述车辆图像的拍摄时间,识别所述车辆图像的车牌号;The license plate number recognition module is used to trigger a shooting instruction if the current lane is the left lane of the expressway, and use the shooting instruction to obtain the vehicle image and the vehicle image shot by the camera group towards the area directly in front of the windshield. The shooting time of the image, and the license plate number of the vehicle image;大型货车车牌号匹配模块,用于在预设的存储区域中,获取预存的本省的大型货车车牌号,将所述车牌号与所述本省的大型货车车牌号进行匹配;The large truck license plate number matching module is used to obtain the pre-stored large truck license plate number of the province in the preset storage area, and match the license plate number with the large truck license plate number of the province;大型货车车牌号识别模块,用于如果所述车牌号与所述本省的大型货车车牌号匹配成功,就识别所述车牌号为本省的大型货车车牌号;The large truck license plate number identification module is used to identify the large truck license plate number of the province if the license plate number is successfully matched with the large truck license plate number of the province;大型货车违章消息生成模块,用于将预设的大型货车违章标签、所述大型货车车牌号、拍摄时间以及所述位置坐标封包,生成大型货车违章消息;The large truck violation message generation module is used to package the preset large truck illegal label, the large truck license plate number, the shooting time and the position coordinates to generate the large truck violation message;大型货车违章消息上传模块,用于建立所述大型货车违章消息以及所述大型货车违章视频之间的对应关系,向预设的管理服务器上传所述大型货车违章消息、所述大型货车违章视频以及所述对应关系;The large truck violation message uploading module is used to establish the correspondence between the large truck violation message and the large truck violation video, and upload the large truck violation message, the large truck violation video, and the large truck violation video to a preset management server. the corresponding relationship;所述大型货车违章消息生成模块,具体用于:获取图像帧率,将图像帧率和预设时间相乘,得到预设时间内获取到的所述车辆图像的帧数,获取预设时间内识别所述车牌号为本省的大型货车车牌号的次数,根据预设的货车识别可靠系数生成模型、获取到的所述车辆图像的帧数以及识别所述车牌号为本省的大型货车车牌号的次数,生成识别所述车牌号为本省的大型货车车牌号的货车识别可靠系数,当货车识别可靠系数大于预设值时,将预设的大型货车违章标签、所述大型货车车牌号、拍摄时间以及所述位置坐标封包,生成大型货车违章消息;The large truck violation message generation module is specifically used for: acquiring an image frame rate, multiplying the image frame rate and a preset time to obtain the number of frames of the vehicle image acquired within the preset time, and obtaining the frame number of the vehicle image within the preset time. The number of times that the license plate number is recognized as the province's large truck license plate number, the model is generated according to the preset truck identification reliability coefficient, the frame number of the obtained vehicle image, and the number of the large truck license plate number identified as the province's large truck number. The number of times to generate a truck identification reliability coefficient that identifies the license plate number of the large truck in the province. When the truck identification reliability coefficient is greater than the preset value, the preset large truck violation label, the large truck license plate number, and the shooting time and the location coordinate package to generate a large truck violation message;其中,货车识别可靠系数生成模型具体为:Among them, the generation model of the reliability coefficient of truck identification is specifically:
Figure FDA0003260225660000041
Figure FDA0003260225660000041
其中,V为货车识别可靠系数,V由识别率和识别系数两部分组成,
Figure FDA0003260225660000042
为识别率,Ni表示预设时间内,识别所述车牌号为本省的大型货车车牌号的次数,Frames表示预设时间内,获取到的所述车辆图像的帧数;
Figure FDA0003260225660000043
为识别系数,No表示预设的识别次数;0<a<1、0<b<1,a+b=1,a和b的大小分别决定着识别率和识别系数在货车识别可靠系数中的权重比。
Among them, V is the recognition reliability coefficient of the truck, and V is composed of two parts: the recognition rate and the recognition coefficient.
Figure FDA0003260225660000042
is the recognition rate, Ni represents the number of times of identifying the license plate number of the large truck in the province within a preset time, and Frames represents the number of frames of the vehicle image obtained within a preset time;
Figure FDA0003260225660000043
For the recognition coefficient, No represents the preset recognition times; 0<a<1, 0<b<1, a+b=1, the sizes of a and b respectively determine the recognition rate and the recognition coefficient in the truck recognition reliability coefficient. weight ratio.
6.如权利要求5所述的系统,其特征在于,所述左车道判断模块,具体用于:6. The system of claim 5, wherein the left lane judgment module is specifically used for:如果所述当前的位置坐标处于高速公路内,获取所述高速公路的道路图像,对所述道路图像进行车道识别,如果车道识别为左车道,就判断当前车道为所述高速公路的左车道,如果车道识别为中间车道或右车道,就判断当前车道不为所述高速公路的左车道。If the current position coordinates are in the expressway, obtain a road image of the expressway, and perform lane recognition on the road image, and if the lane is identified as the left lane, determine that the current lane is the left lane of the expressway, If the lane is identified as the middle lane or the right lane, it is determined that the current lane is not the left lane of the expressway.7.如权利要求5所述的系统,其特征在于,所述左车道判断模块,具体用于:7. The system according to claim 5, wherein the left lane judgment module is specifically used for:如果所述当前的位置坐标处于高速公路内,通过安装在左后视镜的侧视摄像头,获取车身左边的图像,利用背景差分法,识别所述车身左边的图像是否存在移动物体,同时通过安装在左车门外把手的距离传感器,获取车身离左侧栏杆的距离,如果在预设时间内,所述车身左边的图像不存在移动物体且所述车身离左侧栏杆的距离的均值小于预设距离,就判断当前车道为所述高速公路的左车道,如果在预设时间内,所述车身左边的图像存在移动物体或所述车身离左侧栏杆的距离的均值不小于预设距离,就判断当前车道不为所述高速公路的左车道。If the current position coordinates are in the expressway, the image on the left side of the vehicle body is obtained through the side-view camera installed on the left rearview mirror, and the background difference method is used to identify whether there is a moving object in the image on the left side of the vehicle body. The distance sensor on the outer handle of the left door obtains the distance between the vehicle body and the left railing. If there is no moving object in the image on the left side of the vehicle body within a preset time, and the average value of the distance between the vehicle body and the left railing is less than the preset value If the current lane is the left lane of the expressway, if there is a moving object in the image on the left side of the vehicle body or the average distance between the vehicle body and the left rail is not less than the preset distance, then It is determined that the current lane is not the left lane of the expressway.8.如权利要求5所述的系统,其特征在于,所述大型货车车牌号识别模块,具体用于:如果所述当前车道为所述高速公路的左车道,获取当前车速,判断当前车速是否低于所述高速公路的左车道要求的最小行驶速度,如果所述当前车速低于所述高速公路的左车道要求的最小行驶速度,就利用所述拍摄指令,获取摄像组朝挡风玻璃正前方区域拍摄的车辆图像以及所述车辆图像的拍摄时间,识别所述车辆图像的车牌号。8. The system according to claim 5, wherein the large truck license plate number recognition module is specifically used for: if the current lane is the left lane of the expressway, obtain the current vehicle speed, and determine whether the current vehicle speed is not It is lower than the minimum driving speed required by the left lane of the expressway. If the current vehicle speed is lower than the minimum driving speed required by the left lane of the expressway, the shooting instruction is used to obtain the direction of the camera group towards the windshield. The vehicle image photographed in the front area and the photographing time of the vehicle image are used to identify the license plate number of the vehicle image.
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