CN114463991B - Axle measuring method, system, terminal device and storage medium - Google Patents
Axle measuring method, system, terminal device and storage medium Download PDFInfo
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- CN114463991B CN114463991B CN202210108740.1A CN202210108740A CN114463991B CN 114463991 B CN114463991 B CN 114463991B CN 202210108740 A CN202210108740 A CN 202210108740A CN 114463991 B CN114463991 B CN 114463991B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
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Abstract
The invention relates to the technical field of vehicle measurement, in particular to a wheel axle measuring method, a system, terminal equipment and a storage medium, wherein the wheel axle measuring method adopts the identification results of a laser radar measuring device and an AI image identification wheel axle measuring device, combines the background illumination and rainfall conditions in the measuring process to select and discard two sets of identification results, and improves the identification rate; a wheel axle measuring system adopts a distributed architecture and consists of a field wheel axle identification device (client) and an analysis server (server), wherein the field wheel axle identification device adopts a multi-mode combination device for independent detection, the devices are mutually verified, a light intensity sensor, a rainfall sensor, a vehicle separator and the like are used for distinguishing environmental conditions, and identification results are selected through rigorous logic analysis of the analysis server, so that the identification rate is improved.
Description
Technical Field
The invention relates to the technical field of vehicle detection, in particular to a wheel axle measuring method, a wheel axle measuring system, a terminal device and a storage medium.
Background
As a vehicle which is necessary in modern society, the detection technology and the standard of the automobile are improved along with the improvement of awareness of people on driving safety.
In the field of intelligent traffic overload control, in order to improve the use efficiency of a highway, a normally running vehicle needs to be detected without stopping the vehicle, and data such as the number of axles, the axle type, load limiting quality and the like of the vehicle are obtained; laser radar scanning is limited by weather conditions, and particularly when the rain is heavy and reaches the level of medium rain or above, the recognition rate is reduced; the AI image recognition method has certain requirements on environmental illumination, and if the illumination intensity is too high or too low, the recognition effect is affected.
Disclosure of Invention
The invention aims to provide a wheel axle measuring method, which adopts the identification results of a laser radar measuring device and an AI image identification wheel axle measuring device, and selects and discards two sets of identification results according to the background light illuminance and rainfall conditions in the measuring process, thereby improving the identification rate.
The invention also aims to provide a wheel axle measuring system which adopts a distributed architecture and consists of a field wheel axle identification device (client) and an analysis server (server), wherein the field wheel axle identification device adopts a multi-mode combination device for independent detection, mutual verification is realized, devices such as a light intensity sensor, a rainfall sensor, a vehicle separator and the like are used for distinguishing environmental conditions, and identification results are selected through rigorous logic analysis of the analysis server, so that the identification rate is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of axle measurement comprising the steps of:
step S1: receiving in-place sensing signals at a retracting point in real time, and sending starting instructions to the first wheel shaft measuring device, the second wheel shaft measuring device and the vehicle license plate identification device when the in-place sensing signals are received; after the first wheel axle measuring device receives a starting instruction, acquiring a first identification image in real time; after the second wheel axle measuring device receives the starting instruction, a second identification image is obtained in real time; after receiving a starting instruction, the vehicle license plate recognition device acquires a license plate recognition image in real time;
step S2: receiving in-place sensing signals at the end points in real time, sending closing instructions to the first wheel axle measuring device, the second wheel axle measuring device and the vehicle number plate identification device when the in-place sensing signals are received, closing the execution operation of the first wheel axle measuring device, the second wheel axle measuring device and the vehicle number plate identification device after the closing instructions are received, and acquiring actual rainfall measurement data and actual illuminance measurement data from the starting point to the end points;
and step S3: analyzing the first identification image, the second identification image and the license plate identification image to respectively obtain a first wheel axle identification result, a second wheel axle identification result and license plate information;
and step S4: analyzing the rainfall measurement data and the illuminance measurement data, and comparing the rainfall measurement data with a rainfall setting condition to obtain a rainfall comparison result; comparing the illuminance measurement data with the illuminance setting condition to obtain an illuminance comparison result;
step S5: comparing and judging the first wheel axle identification result and the second wheel axle identification result to obtain a judgment result;
step S6: analyzing the judgment result, if the judgment result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is not in trailer, binding the first wheel axle identification result and the license plate information, storing the binding result in a database, and feeding back the import success information; if the judgment result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is the trailer, adopting the first wheel axle identification result and feeding back the adopted information; and if the judgment result is that the first wheel axle identification result is inconsistent with the second wheel axle identification result, combining the rainfall comparison result and the illuminance comparison result according to the judgment result to enter a selecting and abandoning step to obtain a selecting and abandoning result, and feeding back selecting and abandoning information.
Preferably, before step S1, the following monitoring step is further included:
step A1: monitoring environmental data from a starting point to an end point in real time, and acquiring rainfall measurement data and illuminance measurement data;
step A2: comparing the rainfall measurement data with rainfall setting conditions according to the rainfall measurement data and the illuminance measurement data to obtain a rainfall comparison result; comparing the illuminance measurement data with illuminance setting conditions to obtain an illuminance comparison result;
step A3: and if the rainfall comparison result and the illuminance comparison result do not meet the set conditions, feeding back the measurement suspension information and continuously monitoring the environmental data until the rainfall measurement data or the illuminance measurement data meet the set conditions, and starting the step S1.
Preferably, in step S1, the first wheel axle measuring device is a laser radar wheel axle measuring device, and the second wheel axle measuring device is an AI image recognition wheel axle measuring device.
Preferably, in step S3, the first wheel axle recognition result and the second wheel axle recognition result both include the number of axles and the axle type of the target vehicle, and the license plate information includes a license plate number, a license plate color, and a vehicle type.
Preferably, in step S4, the rainfall setting condition is that the rainfall measurement data is below a heavy rain level; the illumination setting condition is that the change of the illumination measurement data in unit time is not more than 500Lux.
Preferably, in step S5, the determination result is divided into the number of axles and the axle type being the same, the number of axles and the axle type being the same but the axle type being different, the number of axles and the axle type being different, the towed vehicle and the non-towed vehicle.
Preferably, the selecting and discarding steps include the steps of:
step C1: when the judging result is that the number of the shafts is consistent but the shaft types are not consistent, analyzing a rainfall comparison result; if the rainfall comparison result meets the rainfall setting condition, selecting a first wheel axle identification result; if the rainfall comparison result does not accord with the rainfall setting condition, selecting an axle number identification result in the first axle identification result, and abandoning an axle type identification result and a second axle identification result in the first axle identification result;
and C2: when the judgment result is that the number of axes is inconsistent with the axis type, analyzing the illuminance comparison result; if the illuminance comparison result meets the illuminance setting condition, selecting a second wheel axle identification result; if the illuminance comparison result does not accord with the illuminance setting condition, discarding the first wheel axle identification result and the second wheel axle identification result;
step C3: when the judgment result is that the number of axes is consistent but the shapes of the axes are inconsistent or the number of axes and the shapes of the axes are inconsistent, accumulating the difference times for 1 time; when the difference times reach the set times, feeding back measurement suspension information after executing the step C1 or the step C2;
and C4: when the judgment result is that the number of axes is consistent but the shapes of the axes are inconsistent or the number of axes and the shapes of the axes are inconsistent, and the rainfall comparison result and the illuminance comparison result both accord with the set conditions, accumulating the abnormal times for 1 time; and when the abnormal times reach the set times, feeding back abnormal measurement information after executing the step C1 or the step C2.
A wheel axle measuring system comprises a field wheel axle identification device and an analysis server, wherein the field wheel axle identification device is in communication connection with the analysis server;
the on-site wheel axle identification equipment comprises a rainfall sensor, a light illumination sensor, a vehicle number plate identification device, a laser radar wheel axle measuring device, an AI image identification wheel axle measuring device and a vehicle separation device;
the rainfall sensor is used for collecting rainfall conditions in real time;
the illuminance sensor is used for collecting the illuminance of background light in real time;
the vehicle license plate recognition device is used for recognizing the license plate number, the license plate color and the vehicle type of the detected vehicle;
the laser radar wheel axle measuring device is used for dynamically identifying the number and the type of the vehicle axles;
the AI image recognition wheel axle measuring device is used for dynamically recognizing the number and the type of the vehicle axles;
the vehicle separation device is used for providing starting and ending signals for the laser radar wheel axle measuring device and the AI image recognition wheel axle measuring device to separate the vehicles;
the analysis server comprises a receiving module, an analysis module, a comparison module, a judgment module, a feedback module and a synchronization module;
the receiving module is used for receiving the in-place sensing signal, the first identification image, the second identification image and the license plate identification image;
the analysis module is used for analyzing the first identification image, the second identification image and the license plate identification image to respectively obtain a first wheel axle identification result, a second wheel axle identification result and license plate information;
the comparison module is used for comparing the rainfall measurement data with rainfall setting conditions to obtain a rainfall comparison result; comparing the illuminance measurement data with illuminance setting conditions to obtain an illuminance comparison result; comparing and judging the first wheel axle identification result and the second wheel axle identification result to obtain a judgment result;
the judging module is used for analyzing the judging result, if the judging result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is not the trailer, binding the first wheel axle identification result and the license plate information and storing the binding result in a database, and feeding back the importing success information; if the judgment result is that the first wheel axle identification result is inconsistent with the second wheel axle identification result, the selection and abandonment steps are carried out according to the judgment result and the rainfall comparison result and the illuminance comparison result to obtain selection and abandonment results, and selection and abandonment information is fed back;
the feedback module is used for feeding back import success information, selection and abandonment information, suspension measurement information and abnormal measurement information;
the synchronization module is used for synchronizing the judgment result meeting the expectation to the database.
A terminal device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the axle measuring method described above when executing said computer program.
A storage medium, which stores a computer program that, when being executed by a processor, carries out the steps of the above-mentioned axle measuring method.
Compared with the prior art, the technical scheme has the following beneficial effects:
(1) The invention adopts a method for dynamically measuring the number and the type of the axle of the automobile, improves the measuring efficiency, simultaneously verifies the two identification results of independent detection, and discriminates the environment by combining the rainfall measurement data and the illuminance measurement data, thereby solving the problem that the number and the type of the axle of riding the bicycle can not be conveniently and accurately identified in the prior art.
(2) The system adopts a distributed architecture and consists of a field wheel shaft identification device (client) and an analysis server (server), wherein the field wheel shaft identification device can be put differently according to the situation of a target road section on site, so that the practicability of the field wheel shaft identification device is improved, and the measurement requirements of unattended operation and all-day weather can be met; and uploading the field identification information to a service software end of an analysis server, and selecting the identification results of two sets of devices through rigorous logic analysis by combining the background light illumination test data and the rainfall test data in the measurement process according to the identification results of the laser radar measuring device and the AI image identification wheel shaft measuring device by the analysis server so as to obtain an effective identification result supported by data and improve the identification rate.
Drawings
FIG. 1 is a flow chart of an embodiment of the axle measurement method of the present invention;
FIG. 2 is a schematic view of the axle measurement system of the present invention;
FIG. 3 is a schematic diagram of the construction of the axle measurement terminal apparatus of the present invention;
in the drawings: the system comprises a field wheel shaft identification device 1, an analysis server 2, a rainfall sensor 11, a light intensity sensor 12, a vehicle license plate identification device 13, a laser radar wheel shaft measuring device 14, an AI image identification wheel shaft measuring device 15, a vehicle separation device 16, a receiving module 21, an analysis module 22, a comparison module 23, a judgment module 24, a feedback module 25, a synchronization module 26, a terminal device 3, a memory 4, a processor 5 and a computer program 6.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
As shown in fig. 1, a wheel axle measuring method includes the steps of:
step S1: receiving in-place sensing signals at a retracting point in real time, and sending starting instructions to the first wheel axle measuring device, the second wheel axle measuring device and the vehicle license plate identification device when receiving the in-place sensing signals; after the first wheel axle measuring device receives a starting instruction, acquiring a first identification image in real time; after the second wheel axle measuring device receives the starting instruction, a second identification image is obtained in real time; after receiving a starting instruction, the vehicle license plate recognition device acquires a license plate recognition image in real time;
step S2: receiving in-place sensing signals at the end points in real time, sending closing instructions to the first wheel shaft measuring device, the second wheel shaft measuring device and the vehicle number plate recognition device when the in-place sensing signals are received, closing the execution operation of the first wheel shaft measuring device, the second wheel shaft measuring device and the vehicle number plate recognition device after the closing instructions are received, and acquiring actual measurement data of rainfall and actual measurement data of illuminance from the starting points to the end points;
and step S3: analyzing the first recognition image, the second recognition image and the license plate recognition image to respectively obtain a first wheel axle recognition result, a second wheel axle recognition result and license plate information;
and step S4: analyzing the rainfall measurement data and the illuminance measurement data, and comparing the rainfall measurement data with a rainfall setting condition to obtain a rainfall comparison result; comparing the illuminance measurement data with the illuminance setting condition to obtain an illuminance comparison result;
step S5: comparing and judging the first wheel axle identification result and the second wheel axle identification result to obtain a judgment result;
step S6: analyzing the judgment result, if the judgment result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is not the trailer, binding the first wheel axle identification result and the license plate information, storing the bound first wheel axle identification result and the license plate information in a database, and feeding back import success information; if the judgment result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is that the trailer is towed, adopting the first wheel axle identification result and feeding back the adopted information; and if the judgment result is that the first wheel axle identification result is inconsistent with the second wheel axle identification result, combining the rainfall comparison result and the illuminance comparison result according to the judgment result to enter a selecting and abandoning step to obtain a selecting and abandoning result, and feeding back selecting and abandoning information.
The invention adopts a method for dynamically measuring the number and the type of the axle of the automobile, improves the measurement efficiency, simultaneously verifies the two independent detection identification results mutually, and discriminates the environment by combining the rainfall measurement data and the illuminance measurement data, thereby solving the problem that the number and the type of the axle of the bicycle can not be conveniently and accurately identified in the prior art.
Specifically, when the identification results of the first wheel axle measuring device and the second wheel axle measuring device are consistent and the identification result is not the trailer, the identification result and the license plate information are bound by combining the license plate information identified by the vehicle license plate identification device and stored in a database in the form of a vehicle file, and the vehicle license plate identification device can be used as a history record and can be used for directly quoting the next measurement of the vehicle or checking the identification result.
It should be noted that the combination of the semitrailer and the tractor for towing the semitrailer is called a trailer for short, and the semitrailer can tow semitrailers with different numbers of axles and numbers of license plates, so that the numbers of the axles and the axle types of the trailer are not unique, and the trailer cannot be stored in a vehicle file form.
Furthermore, the method can automatically judge whether the target vehicle is in an overload state or not by combining weighing data according to the requirements of the national standard GB1589, can realize unattended operation, and is widely applied to factory weighing of freight source enterprises, overload return of expressway entrances and overload unloading fields of freight vehicles.
To be further explained, before the step S1, the following monitoring step is further included:
step A1: monitoring environmental data from a starting point to an end point in real time, and acquiring rainfall measurement data and illuminance measurement data;
step A2: comparing the rainfall measurement data with rainfall setting conditions according to the rainfall measurement data and the illuminance measurement data to obtain a rainfall comparison result; comparing the illuminance measurement data with the illuminance setting condition to obtain an illuminance comparison result;
step A3: and if the rainfall comparison result and the illuminance comparison result do not meet the set conditions, feeding back the pause measurement information and continuously monitoring the environmental data until the rainfall measurement data or the illuminance measurement data meet the set conditions, and starting the step S1.
When the rainfall comparison result and the illuminance comparison result do not meet the set conditions, the analysis server automatically and temporarily detects the rainfall and displays related contents on the LED display screen to inform a worker; meanwhile, environmental data such as rainfall, illuminance and the like on site are continuously monitored, and measurement is automatically recovered after environmental conditions meet measurement requirements, so that unattended operation is realized.
To be further described, in step S1, the first wheel axle measuring device is a laser radar wheel axle measuring device, and the second wheel axle measuring device is an AI image recognition wheel axle measuring device.
To be further explained, in step S3, the first wheel axle recognition result and the second wheel axle recognition result both include the number of axles and the axle type of the target vehicle, and the license plate information includes a license plate number, a license plate color, and a vehicle type.
To be more specific, in step S4, the rainfall setting condition is that the rainfall measurement data is lower than a heavy rain level; the illuminance setting condition is that the change of illuminance measurement data in unit time is not more than 500Lux.
The laser radar is convenient to install, measures in a non-contact mode, is simple to install and is convenient to maintain, but outdoor sites are limited by weather conditions, particularly when rain is heavy and reaches the level of medium rain or above, the recognition rate is reduced, and the shaft number or the shaft type is easy to make mistakes; therefore, the validity of the wheel axle data measured by the laser radar wheel is verified through the rainfall condition.
The AI image recognition method has good overall recognition effect, is convenient to install and maintain, is not influenced under the conditions of small rain and medium rain, but has certain requirements on environmental illumination, when the background of the tire tread has strong light or when a vehicle runs through a recognition area, the background light is suddenly bright and dim (partial trucks can be additionally provided with spot lamps on two sides of the vehicle for night running safety) or the background light is too dim, the recognition effect can be influenced, and in addition, the partial vehicles with too low running speed of long axle distance are easy to generate shunting errors, so that the axle number and axle type recognition errors are caused; therefore, the validity of the AI image identification wheel axle data is verified by collecting the variation of the background light illumination.
The heavy rain level means a rainfall of 25 to 50mm at 1d (or 24 h).
To be more specific, in step S5, the determination results are divided into the number of axles and the axle type being the same, the number of axles and the axle type being the same but the axle type being different, the number of axles and the axle type being different, and the towed vehicle and the non-towed vehicle.
In a further aspect, the selecting and discarding step includes the steps of:
step C1: when the judgment result is that the number of the shafts is consistent but the shaft types are not consistent, analyzing the rainfall comparison result; if the rainfall comparison result meets the rainfall setting condition, selecting a first wheel axle identification result; if the rainfall comparison result does not meet the rainfall setting condition, selecting an axle number identification result in the first axle identification result, and abandoning an axle type identification result and a second axle identification result in the first axle identification result;
and step C2: when the judgment result is that the number of axes is inconsistent with the axis type, analyzing the illuminance comparison result; if the illuminance comparison result meets the illuminance setting condition, selecting a second wheel axle identification result; if the illuminance comparison result does not accord with the illuminance setting condition, discarding the first wheel axle identification result and the second wheel axle identification result;
step C3: when the judgment result is that the number of axes is consistent but the shapes of the axes are inconsistent or the number of axes and the shapes of the axes are inconsistent, accumulating the difference times for 1 time; when the difference times reach the set times, feeding back measurement suspension information after executing the step C1 or the step C2;
and C4: when the judgment result is that the number of axes is consistent but the shapes of the axes are inconsistent or the number of axes and the shapes of the axes are inconsistent, and the rainfall comparison result and the illuminance comparison result both accord with the set conditions, accumulating the abnormal times for 1 time; and when the abnormal times reach the set times, feeding back abnormal measurement information after executing the step C1 or the step C2.
In step C1, when the judgment result of the first identification result and the second identification result is that the axle number is consistent and the axle type is inconsistent, the measurement data of the rainfall sensor is judged first, and when the rainfall measurement data is lower than a set value and meets the requirement of the laser radar axle identification device, the analysis server preferentially adopts the identification result of the laser radar axle identification device. Compared through experiments, under the condition that the measurement conditions of the two identification devices are met, the identification rate of the laser radar wheel axle identification device on the axle type is higher than that of the AI image identification wheel axle measurement device by about 3%.
In step C2, when the judgment result of the first identification result and the second identification result is that the number of axes and the shape of axes are not consistent, the measurement data of the illuminance sensor is judged first, and when the illuminance of the measurement environment is satisfied and the illuminance does not change in display during measurement (the illuminance value does not change by more than 500 Lux), the identification result of the wheel axle measurement device is identified by the AI image with priority. And if necessary, the video of the measurement process can be downloaded through NVR equipment, and secondary identification is automatically carried out.
In step C3, when the environmental data such as rainfall, illuminance and the like satisfy the measurement conditions of the two sets of devices for laser radar and AI image recognition, but the judgment result shows inconsistency many times in unit time, the system informs the equipment maintenance personnel through short messages or mails and the like.
In step C4, when the judgment result is that the number of shafts is consistent but the shaft type is inconsistent or the number of times of the inconsistency between the number of shafts and the shaft type reaches the set condition, the detection is automatically suspended to wait for the maintenance personnel to perform the overhaul.
As shown in fig. 2, an axle measuring system includes a field axle identification device 1 and an analysis server 2, wherein the field axle identification device 1 is connected to the analysis server 2 in a communication manner;
the on-site wheel axle identification device 1 comprises a rainfall sensor 11, a light intensity sensor 12, a vehicle number plate identification device 13, a laser radar wheel axle measuring device 14, an AI image identification wheel axle measuring device 15 and a vehicle separation device 16;
the rainfall sensor 11 is used for collecting rainfall conditions in real time;
the illuminance sensor 12 is used for collecting background illuminance in real time;
the vehicle license plate recognition device 13 is used for recognizing the license plate number, the license plate color and the vehicle type of the detected vehicle;
the laser radar wheel axle measuring device 14 is used for dynamically identifying the number and the type of the vehicle axles;
the AI image recognition wheel axle measuring device 15 is used for dynamically recognizing the number and the type of the vehicle axles;
the vehicle separation device 16 is used for providing starting and ending signals for the laser radar wheel axle measuring device 14 and the AI image recognition wheel axle measuring device 15 to separate the vehicles;
the analysis server 2 comprises a receiving module 21, an analysis module 22, a comparison module 23, a judgment module 24, a feedback module 25 and a synchronization module 26;
the receiving module 21 is configured to receive the in-place sensing signal, the first identification image, the second identification image, and the license plate identification image;
the analysis module 22 is configured to analyze the first recognition image, the second recognition image and the license plate recognition image to obtain a first axle recognition result, a second axle recognition result and license plate information, respectively;
the comparison module 23 is configured to compare the rainfall measurement data with a rainfall setting condition to obtain a rainfall comparison result; comparing the illuminance measurement data with the illuminance setting condition to obtain an illuminance comparison result; comparing and judging the first wheel axle identification result and the second wheel axle identification result to obtain a judgment result;
the judgment module 24 is configured to analyze the judgment result, and if the judgment result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is not the trailer, bind the first wheel axle identification result and the license plate information and store the bound first wheel axle identification result and the license plate information in a database, and feed back the import success information; if the judgment result is that the first wheel axle identification result is inconsistent with the second wheel axle identification result, the selection and abandonment steps are carried out according to the judgment result and the rainfall comparison result and the illuminance comparison result to obtain selection and abandonment results, and selection and abandonment information is fed back;
the feedback module 25 is configured to feed back import success information, selection and abandonment information, suspension measurement information, and abnormal measurement information;
the synchronization module 26 is used for synchronizing the judgment result meeting the expectation into the database.
The system adopts a distributed architecture and consists of a field wheel shaft identification device (client) and an analysis server (server), wherein the field wheel shaft identification device can be put differently according to the situation of a target road section on site, so that the practicability of the field wheel shaft identification device is improved, and the measurement requirements of unattended operation and all-day weather can be met; and uploading the field identification information to a service software end of an analysis server, wherein the analysis server identifies the identification results of the wheel axle measuring device according to the laser radar measuring device and the AI image, combines the background light illumination test data and the rainfall test data in the measuring process, and selects the identification results of two sets of devices through rigorous logic analysis to obtain an effective identification result supported by data and improve the identification rate.
As shown in fig. 3, a terminal device 3 comprises a memory 4, a processor 5 and a computer program 6 stored in the memory 4 and operable on the processor 5, wherein the processor 5 implements the steps of the axle measurement method when executing the computer program 6.
A storage medium storing a computer program 6, the computer program 6 realizing the steps of the axle measuring method described above when being executed by a processor 5.
The technical principle of the present invention is described above in connection with specific embodiments. The description is made for the purpose of illustrating the principles of the invention and should not be taken in any way as limiting the scope of the invention. Other embodiments of the invention will occur to those skilled in the art without the exercise of inventive faculty based on the explanations herein, and such equivalent modifications or substitutions are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (10)
1. A method of axle measurement, comprising the steps of:
step S1: receiving in-place sensing signals at a retracting point in real time, and sending starting instructions to the first wheel axle measuring device, the second wheel axle measuring device and the vehicle license plate identification device when receiving the in-place sensing signals; after the first wheel axle measuring device receives a starting instruction, acquiring a first identification image in real time; after the second wheel axle measuring device receives the starting instruction, a second identification image is obtained in real time; after receiving a starting instruction, the vehicle license plate recognition device acquires a license plate recognition image in real time;
step S2: receiving in-place sensing signals at the end points in real time, sending closing instructions to the first wheel axle measuring device, the second wheel axle measuring device and the vehicle number plate identification device when the in-place sensing signals are received, closing the execution operation of the first wheel axle measuring device, the second wheel axle measuring device and the vehicle number plate identification device after the closing instructions are received, and acquiring actual rainfall measurement data and actual illuminance measurement data from the starting point to the end points;
and step S3: analyzing the first identification image, the second identification image and the license plate identification image to respectively obtain a first wheel axle identification result, a second wheel axle identification result and license plate information;
and step S4: analyzing the rainfall measurement data and the illuminance measurement data, and comparing the rainfall measurement data with a rainfall setting condition to obtain a rainfall comparison result; comparing the illuminance measurement data with the illuminance setting condition to obtain an illuminance comparison result;
step S5: comparing and judging the first wheel axle identification result and the second wheel axle identification result to obtain a judgment result;
step S6: analyzing the judgment result, if the judgment result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is not in trailer, binding the first wheel axle identification result and the license plate information, storing the binding result in a database, and feeding back the import success information; if the judgment result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is that the trailer is towed, adopting the first wheel axle identification result and feeding back the adopted information; if the judgment result is that the first wheel axle identification result is inconsistent with the second wheel axle identification result,
and entering a selecting and abandoning step according to the judgment result by combining the rainfall comparison result and the illuminance comparison result to obtain a selecting and abandoning result and feeding back selecting and abandoning information.
2. A wheel axle measuring method according to claim 1, further comprising, before step S1, the following monitoring steps:
step A1: monitoring environmental data from a starting point to an ending point in real time, and acquiring rainfall measurement data and illuminance measurement data;
step A2: comparing the rainfall measurement data with rainfall setting conditions according to the rainfall measurement data and the illuminance measurement data to obtain a rainfall comparison result; comparing the illuminance measurement data with the illuminance setting condition to obtain an illuminance comparison result;
step A3: and if the rainfall comparison result and the illuminance comparison result do not meet the set conditions, feeding back the pause measurement information and continuously monitoring the environmental data until the rainfall measurement data or the illuminance measurement data meet the set conditions, and starting the step S1.
3. A wheel axle measuring method according to claim 1, characterized in that in step S1, the first wheel axle measuring device is a laser radar wheel axle measuring device and the second wheel axle measuring device is an AI image recognition wheel axle measuring device.
4. A method for measuring a wheel axle according to claim 1, wherein in step S3, the first wheel axle recognition result and the second wheel axle recognition result each include the number of axles and the axle type of the target vehicle, and the license plate information includes a license plate number, a license plate color and a vehicle type.
5. A wheel axle measuring method according to claim 1, wherein in step S4, the rainfall setting condition is that the rainfall measurement data is below a heavy rain level; the illuminance setting condition is that the change of illuminance measurement data in unit time is not more than 500Lux.
6. A wheel axle measuring method according to claim 1, wherein, in step S5,
and the judgment result is divided into a towed vehicle and a non-towed vehicle, wherein the number of the shafts is consistent with the type of the shafts, the number of the shafts is consistent but the type of the shafts is inconsistent, the number of the shafts is inconsistent with the type of the shafts.
7. A method of axle measurement as claimed in claim 6, wherein the selecting and discarding steps include the steps of:
step C1: when the judgment result is that the number of the shafts is consistent but the shaft types are not consistent, analyzing the rainfall comparison result; if the rainfall comparison result meets the rainfall setting condition, selecting a first wheel axle identification result; if the rainfall comparison result does not accord with the rainfall setting condition, selecting an axle number identification result in the first axle identification result, and abandoning an axle type identification result and a second axle identification result in the first axle identification result;
and C2: when the judgment result is that the number of axes is inconsistent with the type of the axes, analyzing the illuminance comparison result; if the illuminance comparison result meets the illuminance setting condition, selecting a second wheel axle identification result; if the illuminance comparison result does not accord with the illuminance setting condition, discarding the first wheel axle identification result and the second wheel axle identification result;
and C3: when the judgment result is that the number of axes is consistent but the shapes of the axes are inconsistent or the number of axes and the shapes of the axes are inconsistent, accumulating the difference times for 1 time; when the difference times reach the set times, feeding back measurement suspension information after executing the step C1 or the step C2;
and C4: when the judgment result is that the number of axes is consistent but the shapes of the axes are inconsistent or the number of axes and the shapes of the axes are inconsistent, and the rainfall comparison result and the illuminance comparison result both accord with the set conditions, accumulating the abnormal times for 1 time; and when the abnormal times reach the set times, feeding back abnormal measurement information after executing the step C1 or the step C2.
8. The wheel axle measuring system is characterized by comprising a field wheel axle identification device and an analysis server, wherein the field wheel axle identification device is in communication connection with the analysis server;
the on-site wheel axle identification equipment comprises a rainfall sensor, a light illumination sensor, a vehicle number plate identification device, a laser radar wheel axle measuring device, an AI image identification wheel axle measuring device and a vehicle separation device;
the rainfall sensor is used for collecting rainfall conditions in real time;
the illuminance sensor is used for collecting the illuminance of background light in real time;
the vehicle license plate recognition device is used for recognizing the license plate number, the license plate color and the vehicle type of the detected vehicle;
the laser radar wheel axle measuring device is used for dynamically identifying the number and the type of the vehicle axles;
the AI image recognition wheel axle measuring device is used for dynamically recognizing the number and the type of the vehicle axles;
the vehicle separation device is used for providing starting and ending signals for the laser radar axle measuring device and the AI image recognition axle measuring device to separate vehicles;
the analysis server comprises a receiving module, an analysis module, a comparison module, a judgment module, a feedback module and a synchronization module;
the receiving module is used for receiving the in-place sensing signal, the first recognition image, the second recognition image and the license plate recognition image;
the analysis module is used for analyzing the first identification image, the second identification image and the license plate identification image to respectively obtain a first wheel axle identification result, a second wheel axle identification result and license plate information;
the comparison module is used for comparing the rainfall measurement data with rainfall setting conditions to obtain a rainfall comparison result; comparing the illuminance measurement data with the illuminance setting condition to obtain an illuminance comparison result; comparing and judging the first wheel axle identification result and the second wheel axle identification result to obtain a judgment result;
the judging module is used for analyzing the judging result, if the judging result is that the first wheel axle identification result is consistent with the second wheel axle identification result and the identification result is not the trailer, binding the first wheel axle identification result and the license plate information and storing the binding result in a database, and feeding back the importing success information; if the judgment result is that the first wheel axle identification result is inconsistent with the second wheel axle identification result, the selection and abandonment steps are carried out according to the judgment result and the rainfall comparison result and the illuminance comparison result to obtain selection and abandonment results, and selection and abandonment information is fed back;
the feedback module is used for feeding back import success information, selection and abandonment information, pause measurement information and abnormal measurement information;
the synchronization module is used for synchronizing the judgment result meeting the expectation to the database.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor when executing the computer program carries out the steps of the axle measuring method according to any one of claims 1 to 7.
10. A storage medium, which stores a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the axle measuring method according to any one of claims 1 to 7.
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| CA2238127A1 (en) * | 1998-05-15 | 1999-11-15 | Terry Bergan | Truck traffic monitoring and warning systems and vehicle ramp advisory system |
| KR101140025B1 (en) * | 2010-12-14 | 2012-05-02 | 김기태 | Method and system for detecting overload and unlawful measurement of vehicle |
| US9110196B2 (en) * | 2012-09-20 | 2015-08-18 | Google, Inc. | Detecting road weather conditions |
| CN103279996B (en) * | 2013-05-06 | 2016-05-04 | 华南理工大学 | Information of vehicles in a kind of multilane situation detects and recognition system |
| CN203260072U (en) * | 2013-05-06 | 2013-10-30 | 华南理工大学 | Vehicle information detection and identification system under multiple-lane condition |
| CN104064030B (en) * | 2014-07-01 | 2016-04-13 | 武汉万集信息技术有限公司 | A kind of model recognizing method and system |
| GB201503855D0 (en) * | 2015-03-06 | 2015-04-22 | Q Free Asa | Vehicle detection |
| JP2016184316A (en) * | 2015-03-26 | 2016-10-20 | 株式会社東芝 | Vehicle type determination device and vehicle type determination method |
| CN107909820A (en) * | 2017-12-27 | 2018-04-13 | 天津杰泰高科传感技术有限公司 | The laser scanning vehicle separating device and implementation method of a kind of integrating automotive axle identification function |
| CN207650997U (en) * | 2017-12-27 | 2018-07-24 | 天津杰泰高科传感技术有限公司 | The laser scanning vehicle separating device of integrating automotive axle identification function |
| JP7234538B2 (en) * | 2018-08-31 | 2023-03-08 | コニカミノルタ株式会社 | Image processing device, axle number detection system, fee setting device, fee setting system and program |
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