CN111582288B - Non-contact vehicle overload recognition system based on vehicle body vibration model - Google Patents
Non-contact vehicle overload recognition system based on vehicle body vibration model Download PDFInfo
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Abstract
The invention discloses a system for recognizing the load of a double-axle vehicle and judging overload in a non-contact manner through a vehicle body vibration model and combining video image processing, and belongs to the field of overload recognition. The overhead camera captures a front photo of the vehicle, the characteristic information recognition module recognizes characteristic information of the vehicle, and the data such as the wheelbase of the vehicle is extracted by matching with the database. The side camera shoots a section of video of the vehicle after excitation, and transmits the video to the characteristic distance recognition module to obtain characteristic distance representing the change of the suspension distance; identifying a centroid position of the vehicle by a centroid position identification module; fitting the dynamic characteristic distance in a vehicle weight recognition module to obtain the natural frequency of the suspension, and calculating the sprung mass of the vehicle by combining the rigidity and other data of the suspension; and comparing rated loads, and judging whether the vehicle is overloaded or not.
Description
Technical Field
The invention belongs to the field of overload recognition, and particularly relates to a non-contact overload recognition system based on a vehicle body vibration model.
Background
According to statistics of traffic management departments, the overload rate of commercial vehicles in China is more than 30%. The overload of the vehicles can damage road facilities, the overload of the vehicles can cause the increase of the road damage degree by 41%, and according to the measurement and calculation of traffic departments, the overload of the vehicles in national roads and provincial roads shortens the service life of the cement road surface by 40%, and the service life of the asphalt road surface by 20% -30%.
The overload of the vehicle can increase emission pollution, and the actual load of the overload vehicle far exceeds the load standard, so that the load of a fuel system is overlarge, the fuel combustion is incomplete, and the tail gas emission is seriously exceeded.
The current traffic management department mainly comprises the following means for checking overload illegal behaviors: temporary setting check-out and fixed detection station check-out. The method needs a visual inspection mode for traffic police to estimate the overload of the vehicle, and has the defects of high police strength requirement, error in estimating the overload, influence on road passing efficiency and the like. The fixed detection station is usually used in a highway entrance toll station, the vehicle is required to stop weighing on the wagon balance, and the defects of lower test efficiency and influence on road passing efficiency exist.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a non-contact vehicle overload recognition system based on a vehicle body vibration model, which realizes load extraction of a vehicle to be detected in a non-stop state by combining vibration mechanics of the vehicle through shooting and video recording of two cameras, and improves the detection efficiency of a traffic management department on vehicle overload recognition.
In order to achieve the above object, the present invention provides a non-contact vehicle overload recognition system based on a vehicle body vibration model, comprising: the system comprises a visual roadbed module, a characteristic information identification module, a characteristic distance identification module, a centroid position identification module, a vehicle weight identification module and an overload judgment module;
The visual roadbed module comprises a top camera, a side camera and an excitation source; the overhead camera is used for taking a front photo of the tested vehicle; the side camera is used for recording a video, wherein the content of the video comprises: the tested vehicle drives into the visual field of the camera, the tested vehicle runs through the excitation source, the suspension generates free vibration after passing through the excitation source, and the tested vehicle drives away from the visual field of the camera; the excitation source is used for enabling the tested vehicle to generate free vibration in the visual field range of the side camera;
The characteristic information identification module is used for identifying characteristic information of the detected vehicle from a front photo of the detected vehicle so as to call the wheelbase, the front suspension rigidity, the rear suspension rigidity, the sprung mass and the rated load of the detected vehicle in no-load state from a preset database according to the characteristic information;
The characteristic distance recognition module is used for obtaining a front suspension characteristic distance and a rear suspension characteristic distance of the tested vehicle in each frame of image of the video according to the wheelbase, wherein the extracted characteristic distance is a static characteristic distance from an image in an unoibrated state before the tested vehicle passes through the excitation source, and the extracted characteristic distance is a dynamic characteristic distance from an image in an vibrated state after the tested vehicle passes through the excitation source;
The mass center position identification module is used for obtaining the distance between the mass center and the front and rear axles according to the wheelbase, the front suspension rigidity, the rear suspension rigidity, the front suspension static characteristic distance and the rear suspension static characteristic distance;
The vehicle weight identification module is used for obtaining the sprung mass of the tested vehicle according to the wheelbase, the front suspension rigidity, the rear suspension rigidity, the front suspension dynamic characteristic distance, the rear suspension dynamic characteristic distance and the distance between the mass center and the front and rear axles;
The overload judging module is used for making a difference between the sprung mass of the tested vehicle and the sprung mass in the idle state to obtain an actual load, and comparing the actual load with the rated load of the tested vehicle to determine the load condition of the tested vehicle.
Preferably, the front suspension characteristic distance is a vertical distance between a front suspension characteristic point and a front wheel center, and the rear suspension characteristic distance is a distance between a rear suspension characteristic point and a rear wheel center, wherein the front suspension characteristic point is an intersection point of a vehicle underbody line and a line passing through the front wheel center and perpendicular to the ground, and the rear suspension characteristic point is an intersection point of the vehicle underbody line and a line passing through the rear wheel center and perpendicular to the ground.
Preferably, the mass center position identification module is used for identifying the mass center of the object byAnd obtaining a horizontal distance a between the mass center of the vehicle and the front axle of the vehicle and a horizontal distance b between the mass center of the vehicle and the rear axle of the vehicle, wherein alpha is the gravity center coefficient of the vehicle, and L is the wheelbase of the vehicle.
Preferably, the weight recognition module is used for the vehicle to be driven byAnd obtaining the sprung mass of the tested vehicle, wherein K 1 is the front suspension stiffness, K 2 is the rear suspension stiffness, omega 1 is the natural frequency of the front suspension, omega 2 is the natural frequency of the rear suspension, L is the vehicle wheelbase, a is the horizontal distance from the front axle of the vehicle to the center of mass of the vehicle, b is the horizontal distance from the rear axle of the vehicle to the center of mass of the vehicle, m is the sprung mass of the vehicle, and rho y is the turning radius of the transverse axis y.
Preferably, the dynamic characteristic distance sequence of the front suspension in the free vibration process of the vehicle after passing through the excitation source is differenced with the static characteristic distance of the front suspension to obtain the vertical displacement sequence of the front suspension for vehicle body vibration, and the vertical displacement sequence of the front suspension is calculated by an equation Fitting to obtain a parameter omega 1 of an equation, namely the natural frequency of the front suspension;
The dynamic characteristic distance sequence of the rear suspension in the free vibration process of the vehicle after passing through the excitation source is differenced with the static characteristic distance of the rear suspension to obtain the vertical displacement sequence of the rear suspension for vibration of the vehicle body, and the vertical displacement sequence of the rear suspension is calculated by an equation Fitting to obtain a parameter omega 2 of an equation, namely the natural frequency of the rear suspension, wherein A represents the amplitude, ζ represents the damping ratio and θ represents the phase difference.
Preferably, the overload judging module is configured to determine that the detected vehicle is not overloaded when the actual load is less than or equal to the rated load, and determine that the detected vehicle is overloaded when the actual load is greater than the rated load.
Preferably, the excitation source comprises at least a trapezoidal deceleration strip.
Preferably, the characteristic information of the detected vehicle at least comprises a license plate number.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
1. the vehicle body vibration model is combined with the machine vision technology, so that non-stop non-contact overload identification is realized, and the overload detection efficiency of the detected vehicle is improved.
2. The hardware equipment required by overload identification only has two cameras and excitation sources, so that compared with a wagon balance, the cost is greatly reduced, the maintenance is convenient, and the service life is long.
Drawings
FIG. 1 is a schematic diagram of the overall structure of a system according to an embodiment of the present invention;
FIG. 2 is a schematic view showing the layout of main components on a roadbed of a road according to an embodiment of the present invention;
FIG. 3 is a method for selecting an excitation source according to an embodiment of the present invention;
FIG. 4 is a block diagram of an arrangement of visual roadbed modules according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a characteristic feature distance provided by an embodiment of the present invention;
FIG. 6 is a schematic illustration of a frame shape after suspension deformation according to an embodiment of the present invention;
FIG. 7 is a view of the center of gravity of a vehicle at a distance from the front and rear axles according to an embodiment of the present invention;
fig. 8 is a schematic view of a position of a transverse axis y of a vehicle according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Fig. 1 is a schematic diagram of an overall structure of a non-contact vehicle overload recognition system based on a vehicle body vibration model according to an embodiment of the present invention, where an application object is a dual-axis vehicle, and the system includes: the system comprises a visual roadbed module, a characteristic information identification module, a characteristic distance identification module, a mass center position identification module, a vehicle weight identification module, an overload judgment module and a database; the front suspension rigidity, the rear suspension rigidity, the track width, the wheelbase, the sprung mass and the rated load of the vehicle when in no-load are stored in the database, and the data are matched through characteristic information; wherein:
Fig. 2 is a schematic layout diagram of a visual roadbed module on a roadbed according to an embodiment of the present invention, where the visual roadbed module includes: the device comprises a top camera, a side camera and an excitation source; the overhead camera is used for taking a front photo of the tested vehicle, the side camera is used for recording a video, and the content of the video comprises: the tested vehicle is driven into the visual field of the camera, the tested vehicle runs through the excitation source, free vibration is generated in the suspension in the running process of the tested vehicle after the tested vehicle passes through the excitation source, and the tested vehicle is driven away from the visual field of the camera; the excitation source is used for enabling the tested vehicle to generate free vibration in the visual field range of the side camera.
In the embodiment of the invention, the excitation source comprises, but is not limited to, a trapezoid deceleration strip, meets the standard of pavement rubber deceleration strip, in JT/T713-2008, page 8, has the height range of 30 mm-60 mm and the bottom surface width range of 300 mm-400 mm. As shown in fig. 3.
As a preferred implementation mode, the overhead camera is arranged on a general monitoring vertical rod, the height of a bracket cross arm is required to ensure that the clearance height of the camera and an auxiliary light source from the ground after the installation is 6.0 m-8.0 m, the installation specification accords with the technical specification of safety precaution engineering (additional written description), and in GB 50348-2018, page 146 is connected with an image acquisition card; the image acquisition card is connected with the characteristic information identification module and transmits the shot front photo of the tested vehicle to the characteristic information identification module.
In an embodiment of the present invention, the feature information identifying module is configured to perform image processing on a front photograph of a measured vehicle, identify feature information of the measured vehicle from the front photograph, and transmit the feature information to the database, where the feature information includes at least a license plate number.
The database is used for searching and retrieving the wheelbase of the tested vehicle according to the characteristic information and transmitting the wheelbase information to the characteristic distance identification module;
As a preferred embodiment, the side camera is installed on a roadbed at any side 9m to 15m from a lane, and is used for recording a video, wherein the video comprises: the vehicle is driven into the visual field of the camera, the vehicle runs through the excitation source, the suspension generates free vibration after the vehicle passes through the excitation source, and the vehicle runs away from the visual field of the camera. The arrangement of the overhead camera and the side camera is shown in fig. 4.
In the embodiment of the invention, the characteristic distance identification module is used for processing the video acquired by the side camera to obtain the front suspension characteristic distance and the rear suspension characteristic distance of the tested vehicle in each frame of image of the video; the method comprises the steps that an image of a non-vibration state of a tested vehicle before passing through an excitation source is extracted and is called a static characteristic distance, and the static characteristic distance is transmitted to a mass center position identification module; the method comprises the steps that an image of a vibration state of a tested vehicle after passing through an excitation source is extracted and is called dynamic feature distance, and the dynamic feature distance is transmitted to a vehicle weight identification module;
the image for identifying the characteristic distance is frame data, the front suspension characteristic distance is the vertical distance between a front suspension characteristic point and a front wheel center, the rear suspension characteristic distance is the distance between a rear suspension characteristic point and a rear wheel center, and the front suspension characteristic point is the intersection point of a vehicle body bottom line and a line which passes through the front wheel center and is vertical to the ground; the characteristic point of the rear suspension is the intersection point of the line of the bottom of the vehicle body and the line which passes through the center of the rear wheel and is vertical to the ground; the characteristic distance is represented in fig. 5.
Searching and retrieving the wheelbase, the wheel track, the front suspension rigidity and the rear suspension rigidity of the tested vehicle from the database, and transmitting the wheelbase, the wheel track, the front suspension rigidity and the rear suspension rigidity to the mass center identification module;
The mass center identification module initially calculates the sprung mass of the tested vehicle through M 'g=k 1Δx1+k2Δx2, wherein M' is the sprung mass of the vehicle, g is the gravity coefficient, k 1 is the rigidity of the front suspension, deltax 1 is the difference between the static characteristic distance of the front suspension and the characteristic distance when the front suspension is in idle state, namely the compression amount of the front suspension, k 2 is the rigidity of the rear suspension, deltax 2 is the difference between the static characteristic distance of the rear suspension and the characteristic distance when the rear suspension is in idle state, namely the compression amount of the rear suspension. And then determining the initial value of the gravity center position by utilizing the deformation coordination relation of the frame. An overloaded vehicle is generally a medium-heavy vehicle, and the frame is basically a middle-mounted frame, which can be simplified into a rigid body, namely, after the suspension of the vehicle is deformed, the frames are coplanar, as shown in fig. 6. In fig. 6, a 0B0C0D0 is a projection plane of the vehicle body on the chassis plane under no-load working condition, ABCD is a position plane of the vehicle body after the 4 suspensions are deformed, L 1 is a vehicle wheelbase, L 2 is a vehicle wheelbase, and Mg is a position of a centroid of the vehicle. Let the load centroid x-axis be αl 1 and the y-axis be βl 2. The static deflection value at each suspension and the coordinates of each point are as follows:
ΔxA＝A0A ΔxB＝B0B ΔxC＝C0C ΔxD＝D0D
A0(0,0,0) B0(0,L2,0) C0(L1,L2,0) D0(L1,0,0)
A(0,0,ΔxA) B(0,L2,ΔxB) C(L1,L2,ΔxC) D(L1,0,ΔxD)
Mg(0，βL2，Δx)
From the above analysis, the frame is a rigid body and ABCD is coplanar, so that the co-planar deformation coordination relationship can be given by the equation set:
Wherein Deltax A corresponds to Deltax 1,ΔxD corresponds to Deltax 2, and the gravity center coefficients alpha and beta can be obtained by solving the equation set. Substituting the wheelbase L 1, the distance between the centroid and the front and rear axes can be calculated as shown in fig. 7:
The front suspension rigidity and the rear suspension rigidity of the tested vehicle are searched and called from the database and transmitted to the vehicle weight recognition module, and the vehicle weight recognition module is used for processing the front suspension rigidity, the rear suspension rigidity, the front suspension characteristic distance, the rear suspension characteristic distance and the no-load weight to obtain the sprung mass of the tested vehicle and transmitting to the overload judgment module.
In the embodiment of the invention, the vehicle weight identification module contains two contents, and identifies the front suspension natural frequency omega 1 and the rear suspension natural frequency omega 2 of the tested vehicle and calculates the total weight of the vehicle.
The calculation method of omega 1 and omega 2 is as follows:
The method comprises the steps of (1) making a difference between a dynamic characteristic distance sequence of a front suspension and a static characteristic distance of the front suspension in a free vibration process after a vehicle passes through an excitation source, and obtaining a vertical displacement sequence of the front suspension for vibration of the vehicle body; passing the vertical displacement sequence of the front suspension through equation Fitting to obtain a parameter omega 1 of an equation, namely the natural frequency of the front suspension, wherein A represents the amplitude, ζ represents the damping ratio and θ represents the phase difference.
The feature distance of the non-vibrating state of the tested vehicle is constant, so that one of the images of the non-vibrating state can be processed, and the front suspension feature distance of the first frame after the tested vehicle completely enters the field of view of the camera in the non-vibrating state is selected as the front suspension static feature distance for the convenience of processing.
Taking a dynamic characteristic distance sequence of the rear suspension in the free vibration process of the vehicle after passing through the excitation source, and making a difference with a static characteristic distance of the rear suspension to obtain a vertical displacement sequence of the rear suspension for vibration of the vehicle body; passing the vertical displacement sequence of the rear suspension through equationFitting to obtain the parameter omega 2 of the equation, namely the natural frequency of the rear suspension. A represents amplitude, ζ represents damping ratio, and θ represents phase difference.
The characteristic distance of the non-vibrating state of the tested vehicle is constant, so that one image of the non-vibrating state can be processed, and the rear suspension characteristic distance of the first frame after the tested vehicle completely enters the field of view of the camera in the non-vibrating state is selected as the rear suspension static characteristic distance for the convenience of processing.
Therefore, the calculation method of the sprung mass of the vehicle under test is as follows:
Wherein the set of equations is known: k 1 is the front suspension stiffness and K 2 is the rear suspension stiffness; omega 1 is the natural frequency of the front suspension, omega 2 is the natural frequency of the rear suspension; l is the wheelbase of the vehicle; a is the horizontal distance from the front axle of the vehicle to the center of mass of the vehicle and b is the horizontal distance from the rear axle of the vehicle to the center of mass of the vehicle. Unknown quantity: m is the sprung mass of the vehicle; ρ y is the radius of gyration on the lateral axis y, which is shown in fig. 8, and CG is the centroid of the vehicle. Substituting the equation set into the known quantity, and solving to obtain the sprung mass m of the vehicle.
Searching and retrieving the rated load of the tested vehicle from the database, transmitting the rated load to the overload judging module, and making a difference between the sprung mass of the tested vehicle and the sprung mass when the tested vehicle is in no-load state by the overload judging module to obtain the actual load, comparing the actual load with the rated load of the tested vehicle, and making the following selections:
The actual load is smaller than or equal to the rated load, and the process is finished;
Or;
and when the actual load is larger than the rated load, recording overload information and ending.
It should be noted that each step/component described in the present application may be split into more steps/components, or two or more steps/components or part of operations of the steps/components may be combined into new steps/components, according to the implementation needs, to achieve the object of the present application.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (4)
1. A non-contact vehicle overload recognition system based on a vehicle body vibration model, comprising: the system comprises a visual roadbed module, a characteristic information identification module, a characteristic distance identification module, a centroid position identification module, a vehicle weight identification module and an overload judgment module;
The visual roadbed module comprises a top camera, a side camera and an excitation source; the overhead camera is used for taking a front photo of the tested vehicle; the side camera is used for recording a video, wherein the content of the video comprises: the tested vehicle drives into the visual field of the camera, the tested vehicle runs through the excitation source, the suspension generates free vibration after passing through the excitation source, and the tested vehicle drives away from the visual field of the camera; the excitation source is used for enabling the tested vehicle to generate free vibration in the visual field range of the side camera;
The characteristic information identification module is used for identifying characteristic information of the detected vehicle from a front photo of the detected vehicle so as to call the wheelbase, the front suspension rigidity, the rear suspension rigidity, the sprung mass and the rated load of the detected vehicle in no-load state from a preset database according to the characteristic information;
The characteristic distance recognition module is used for obtaining a front suspension characteristic distance and a rear suspension characteristic distance of the tested vehicle in each frame of image of the video according to the wheelbase, wherein the extracted characteristic distance is a static characteristic distance from an image in an unoibrated state before the tested vehicle passes through the excitation source, and the extracted characteristic distance is a dynamic characteristic distance from an image in an vibrated state after the tested vehicle passes through the excitation source;
The mass center position identification module is used for obtaining the distance between the mass center and the front and rear axles according to the wheelbase, the front suspension rigidity, the rear suspension rigidity, the front suspension static characteristic distance and the rear suspension static characteristic distance;
The vehicle weight identification module is used for obtaining the sprung mass of the tested vehicle according to the wheelbase, the front suspension rigidity, the rear suspension rigidity, the front suspension dynamic characteristic distance, the rear suspension dynamic characteristic distance and the distance between the mass center and the front and rear axles;
The overload judging module is used for making a difference between the sprung mass of the tested vehicle and the sprung mass in the idle state to obtain an actual load, and comparing the actual load with the rated load of the tested vehicle to determine the load condition of the tested vehicle;
The front suspension characteristic distance is the vertical distance between a front suspension characteristic point and a front wheel center, the rear suspension characteristic distance is the distance between a rear suspension characteristic point and a rear wheel center, wherein the front suspension characteristic point is the intersection point of a vehicle body bottom line and a line passing through the front wheel center and vertical to the ground, and the rear suspension characteristic point is the intersection point of the vehicle body bottom line and a line passing through the rear wheel center and vertical to the ground;
the dynamic characteristic distance sequence of the front suspension in the free vibration process of the vehicle after passing through the excitation source is differenced with the static characteristic distance of the front suspension to obtain the vertical displacement sequence of the front suspension for vehicle body vibration, and the vertical displacement sequence of the front suspension is calculated by an equation Fitting to obtain a parameter omega 1 of an equation, namely the natural frequency of the front suspension;
The dynamic characteristic distance sequence of the rear suspension in the free vibration process of the vehicle after passing through the excitation source is differenced with the static characteristic distance of the rear suspension to obtain the vertical displacement sequence of the rear suspension for vibration of the vehicle body, and the vertical displacement sequence of the rear suspension is calculated by an equation Fitting to obtain a parameter omega 2 of an equation, namely the natural frequency of the rear suspension, wherein A represents amplitude, ζ represents damping ratio and θ represents phase difference;
the mass center position identification module is used for being composed of Obtaining a horizontal distance a between the mass center of the vehicle and the front axle of the vehicle and a horizontal distance b between the mass center of the vehicle and the rear axle of the vehicle, wherein alpha is the gravity center coefficient of the vehicle, and L is the wheelbase of the vehicle;
The vehicle weight identification module is used for being composed of And obtaining the sprung mass of the tested vehicle, wherein K 1 is the front suspension stiffness, K 2 is the rear suspension stiffness, omega 1 is the natural frequency of the front suspension, omega 2 is the natural frequency of the rear suspension, L is the vehicle wheelbase, a is the horizontal distance from the front axle of the vehicle to the center of mass of the vehicle, b is the horizontal distance from the rear axle of the vehicle to the center of mass of the vehicle, m is the sprung mass of the vehicle, and rho y is the turning radius of the transverse axis y.
2. The system of claim 1, wherein the overload determination module is configured to determine that the vehicle under test is not overloaded when the actual load is less than or equal to the rated load, and determine that the vehicle under test is overloaded when the actual load is greater than the rated load.
3. The system of claim 2, wherein the excitation source comprises at least a trapezoidal deceleration strip.
4. A system according to claim 3, wherein the characteristic information of the vehicle under test includes at least a license plate number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010374396.1A CN111582288B (en) | 2020-05-06 | 2020-05-06 | Non-contact vehicle overload recognition system based on vehicle body vibration model |
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| CN111735523B (en) * | 2020-08-27 | 2020-12-15 | 湖南大学 | Vehicle weight detection method and device based on video identification and storage medium |
| CN112161685B (en) * | 2020-09-28 | 2022-03-01 | 重庆交通大学 | Vehicle load measuring method based on surface characteristics |
| CN112766306B (en) * | 2020-12-26 | 2023-06-27 | 浙江天行健智能科技有限公司 | Deceleration strip area identification method based on SVM algorithm |
| CN113218483B (en) * | 2021-04-25 | 2022-04-01 | 武汉理工大学 | Overload visual detection system based on double-shaft vibration model |
| CN113240729B (en) * | 2021-04-25 | 2022-10-11 | 武汉理工大学 | Vehicle mass center position estimation method based on image recognition |
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