CN115096203B - Method for measuring tire tread depth by laser - Google Patents

Abstract

The invention relates to the technical field of tire tread depth detection, in particular to a method for measuring tire tread depth by laser, which comprises the following steps of S1, acquiring vehicle information; s2, detecting whether the front wheel enters a detection area, if so, acquiring a front wheel image through laser imaging, uploading the front wheel image to a server, and if not, continuously detecting; s3, detecting whether the rear wheel enters a detection area, if so, acquiring a rear wheel image through laser imaging, uploading the rear wheel image to a server, and if not, continuously detecting; s4, the server marks the front wheel image and the rear wheel image, and respectively acquires the optimal front wheel image and the optimal rear wheel image; s5, analyzing the optimal front wheel image and the optimal rear wheel image, and calculating the tire tread depth of the front wheel and the tire tread depth of the rear wheel after identifying the tire layout. The invention avoids testing errors caused by photographing different tire sizes, different tire layouts and different positions by combining an imaging mode and an image processing algorithm, and solves the problem of low accuracy of the existing laser tire pattern detection device.

Description

Method for measuring tire tread depth by laser

Technical Field

The invention relates to the technical field of tire tread depth detection, in particular to a method for measuring tire tread depth by laser.

Background

Tires are ground-engaging rolling, annular elastomeric rubber articles assembled on a variety of vehicles or machines. The automobile body is usually arranged on a metal rim, can support the automobile body, buffer external impact, realize contact with a road surface and ensure the running performance of the automobile. Since the primary function of the tire pattern is to increase the friction between the tread and the road surface to prevent wheel slip, the tire pattern is also one of the important factors for identifying the quality of the tire.

Two important factors of tire tread are tread pattern and tread depth. The too deep pattern, the larger the ground elastic deformation of the pattern block, the rolling resistance formed by the elastic hysteresis loss of the tire will be increased, meanwhile, the heat dissipation of the tire is not facilitated, the rise of the tire temperature is accelerated, the root of the pattern is easy to tear and fall off due to severe punishment of stress, and the like. The patterns are too shallow, so that not only the water storage and drainage capacity of the tire is affected, but also the harmful water slipping phenomenon is easily generated, and the defect that the tire with the tread is easy to slip is highlighted, thereby deteriorating the automobile performance. Therefore, the patterns are not good when being too deep or too shallow, and the objective rule is that the patterns become smaller when in use.

In the current automobile overhaul process, the tire tread depth is generally deeply detected and obtained through an instrument, but the detection result of the detection mode is easily influenced by a detection tool, a detection angle or a detection environment, so that the detection result is inaccurate, the safe use process of the tire is difficult to ensure, and meanwhile, the detection efficiency is low, and the requirement of rapid and accurate detection is difficult to meet.

Thus, a method of laser measuring the tire tread depth has been developed.

Disclosure of Invention

The invention aims to provide a method for measuring the tire tread depth by laser, which mainly solves the problems that the existing method can only be used for deeply detecting and acquiring the tire tread depth by an instrument, but the detection result is easily influenced by a detection tool, a detection angle or a detection environment, so that the detection result is inaccurate, the safe use process of the tire is difficult to ensure, and meanwhile, the detection efficiency is low, and the requirement of rapid and accurate detection is difficult to meet.

The invention provides a method for measuring the tire tread depth by laser, which comprises the following steps:

s1, acquiring vehicle information;

s2, detecting whether a front wheel enters a detection area, if so, acquiring a front wheel image through laser imaging, uploading the front wheel image to a server, then executing the next step, and if not, continuously detecting;

S3, detecting whether the rear wheel enters a detection area, if so, acquiring a rear wheel image through laser imaging, uploading the rear wheel image to a server, and then executing the next step, and if not, continuously detecting;

S4, the server marks the front wheel image and the rear wheel image, and respectively acquires an optimal front wheel image and an optimal rear wheel image;

S5, analyzing the optimal front wheel image and the optimal rear wheel image, identifying the tire layout, and then calculating the tire tread depth of the front wheel and the tire tread depth of the rear wheel.

Preferably, said step S2 comprises,

S21, detecting whether a vehicle entering sensor is triggered for the first time, if yes, starting a high-speed camera to enter a standby state, starting a laser generator, and if not, continuously detecting;

S22, detecting whether the photographing sensor is triggered for the first time, if so, continuously acquiring the front-wheel image through laser imaging until the photographing sensor is triggered, executing the next step, and if not, continuously detecting;

S23, detecting whether the vehicle sensor is triggered for the first time, if so, turning off the laser, executing the next step, and if not, continuously detecting;

S24, uploading the acquired front wheel images to a server;

the step S3 of this method comprises the steps of,

S31, detecting whether the vehicle entering sensor is triggered again, starting a laser generator, and if not, continuously detecting;

S32, detecting whether the photographing sensor is triggered again, if so, continuously acquiring the front-wheel image through laser imaging until the photographing sensor is triggered, executing the next step, and if not, continuously detecting;

S33, detecting whether the departure sensor is triggered again, if so, closing the laser and the high-speed camera, executing the next step, and if not, continuously detecting;

and S34, uploading the acquired rear wheel images to a server.

Preferably, before said step S2, there is provided the step of:

SX1, any front wheel image or any rear wheel image with deviation from the optimal front wheel image or the optimal rear wheel image is obtained;

SX2, extremuing the moving distance of the vehicle between the two exposure times of the high-speed camera to enable the moving distance to be equal to the horizontal distance between the center of the tire and the incident light;

SX3, in the extremum state, the test error of the current device,

SX4, in the extremum state, the lowest frame rate f _min of the high-speed camera;

T_min＝s_min/v_max；

Δt_max＜T_min/(n_min-1)；

1/f_min＜Δt_max；

SX5, presetting a test error of the equipment, and calculating the lowest frame rate f _min of the high-speed camera;

SX6, setting the frame rate f of the high-speed camera, wherein f is more than or equal to f _min;

Wherein v is the speed of the tire during passing through the photographing sensor, and the default vehicle passes at a constant speed; s is the distance the vehicle moves during triggering of the photographing sensor; r _min is the minimum value of the tire radius; Δt _max is the maximum value of the adjacent two exposure time differences; h _min is the minimum height of the photographing sensor.

Preferably, the step S4 includes:

s41, the server marks the front wheel image and the rear wheel image;

s42, numbering a plurality of front-wheel images or a plurality of rear-wheel images in sequence, and marking n (n E [ 1, k ]);

s43, selecting an mth picture as the optimal front wheel image or the optimal rear wheel image;

m＝int(n/2)；

Wherein n is equal to the number of images acquired between the two exposure times of the high-speed camera, and n is not less than n _min.

Preferably, the step S5 includes:

s51, identifying a corresponding tire layout according to the tire tread image;

S52, acquiring the front tire tread depth in the optimal front wheel image and the rear tire tread depth in the optimal rear wheel image.

Preferably, before said step S5, a step is provided,

SX5, setting the position of the high-speed camera, including an included angle with the horizontal plane and a horizontal distance from the laser generator, so that all the pattern of the tire during photographing are in the visual field of the high-speed camera;

SX6, the arbitrary vertex of the front wheel image or the rear wheel image is taken as an original point, the axial direction of the tire is taken as an x axis, and the depth direction of the groove is taken as a y axis to establish a coordinate system;

the step S52 includes the steps of,

S521, preprocessing the optimal front wheel image or the optimal rear wheel image, and screening to obtain a tire pattern image;

S522, acquiring y coordinates of all points on the surface of the tire, and taking an average value as y _surfaceAVG;

S523, acquiring position information of all grooves of the tire and corresponding y coordinates, taking an average value of the y coordinates on each groove, and marking as y _grooveAVGj (n E [1, k');

s524, calculating the number of pixel points corresponding to the depth of each groove;

ΔS_pixeln＝y_grooveAVGj-y_surfaceAVG；

S525, calculating the tread depth S of each groove according to the geometric relationship and the trigonometric function relationship;

S＝[tan(α+β)-tanα]*L；

β＝tan^-1((S_centre+S_pixel)/f _{Image distance} )-tan^-1(S_centre/f _{Image distance} )；

S_pixel＝b _{Pixel element} *ΔS_pixel；

S_centre＝b _{Pixel element} *ΔS_centre；

Wherein, α is the included angle between the high-speed camera and the horizontal plane, L is the horizontal distance from the high-speed camera to the laser transmitter, f is the image distance, B _{Pixel element} is the pixel size, Δs _pixel is the number of pixels projected on the high-speed camera by the tire tread depth, S _pixel is the length of the number of pixels corresponding to the tire tread depth, Δs _centre is the number of pixels projected on the high-speed camera from the center point of the pixel direction of the photosensitive chip B, and S _centre is the length of the number of pixels corresponding to the tire tread depth.

Preferably, before said step S5, a step is provided,

SX5, setting the position of the high-speed camera, including an included angle with the horizontal plane and a horizontal distance from the laser generator, so that all the pattern of the tire during photographing are in the visual field of the high-speed camera;

SX6, a coordinate system is established, any vertex of the image is taken as an origin, the direction of a B pixel is taken as a Y axis, the direction of an A pixel is taken as an X axis, and a single pixel point is taken as a minimum unit;

SX7, the incident ray of the high-speed camera is arranged to intersect with the measuring port at a point u, and the point u is imaged at the central position u' of the camera photosensitive chip in the B pixel direction;

SX8, judging the magnitudes of the incident light and the measuring port in the u 'coordinate and the n value of the minimum unit coordinate in the Y-axis direction, if the n value of the minimum unit coordinate is larger than u', executing the step SX9, otherwise, executing the step SX10;

SX9, calculating the length b _j of each pixel point corresponding to the real world on the Y axis,

b_j＝[tan)α+β₁+γ₁)-tan(α+β₁)]*L；

β₁＝tan^-1(ΔS_spixelβ1*b _{Pixel element} /f _{Image distance} )；

γ₁＝tan^-1((ΔS_spixelβ1+ΔS_spixelγ1)*b _{Pixel element} /f _{Image distance} )-β₁；

SX10, calculating the length b _j of each pixel point corresponding to the real world on the Y axis,

b_j＝[tan(α-β₂)-tan(α-β₂-γ₂)]*L；

β₂＝tan^-1(ΔS_spixelβ2*b _{Pixel element} /f _{Image distance} )；

γ₂＝tan^-1))ΔS_spixelβ2+ΔS_spixelγ2)*b _{Pixel element} /f _{Image distance} )-β₂；

SX11, establishing a tire tread depth lookup table;

the step S52 includes the steps of,

S521, obtaining a test pattern image;

s522, acquiring position information and corresponding y coordinates of all grooves of the tire, taking an average value of the y coordinates on the corresponding grooves, and marking as y _grooveAVGj (n E [1, k');

s523, acquiring y coordinates of all points on the surface of the tire, and taking an average value y _surfaceAVG;

S524, obtaining data b _j according to the tire tread depth lookup table and the real world distances represented by the lengths of all the pixel points from y _surfaceAVG to y _grooveAVGj in the lookup table;

S525, accumulating the data b _j to obtain the tread depth;

Wherein, alpha is the included angle between the incident light of the high-speed camera and the horizontal plane, L is the horizontal distance from the high-speed camera to the laser transmitter, f is the image distance, B is the resolution of the depth direction of the groove, B is the dimension of the pixel direction of the high-speed camera, deltaS _pixel is the number of pixels projected on the high-speed camera by the tire tread depth, S _pixel is the length of the number of corresponding pixels, deltaS _centre is the number of pixels projected on the high-speed camera from the center point of the pixel direction of the photosensitive chip B, and S _centre is the length of the number of corresponding pixels; b _j' is the real world distance corresponding to the y _surfaceAVG coordinates, j e [ 1, k' ]; b _j" is the real world distance corresponding to the Y _grooveAVGj coordinate, j ' E [ j ', k ', b _j is the real world distance corresponding to the pixel point with Y coordinate j, j E [ j ', j '.

Preferably, in the step S51, identifying the corresponding tire layout includes:

Calling a system coordinate system;

screening the tire pattern images of the optimal front wheel image and the optimal rear wheel image;

The lowest point of the tire pattern image is connected, and then the upward translation distance s is formed into a reference line L1;

s＝S*h*C；

The ordinate of the point on the reference line L1 is marked as y1;

the y coordinates of all points on the surface of the tire are marked as y _surface;

calculating the difference values of y1-y _surface in sequence from left to right, recording as an intersection point when the difference value crosses 0 at any time, and recording the intersection point;

calculating the number of the intersection points, and judging the tire layout according to the number of the intersection points;

Wherein, C is the number of pixels in the corresponding y-axis direction represented by the actual tire tread per millimeter; s is the maximum tread depth; h is a proportionality coefficient, and h is more than or equal to 1.1 and less than or equal to 1.2.

Preferably, in the step S51, identifying the corresponding tire layout includes:

Calling a system coordinate system;

screening the tire pattern images of the optimal front wheel image and the optimal rear wheel image;

smoothing the tire pattern images of the front wheel image and the rear wheel video;

the y coordinates of all points on the surface of the tire are marked as y _surface;

acquiring two point coordinates of the leftmost side and the rightmost side of the image, and respectively marking as P1 (x 1, y 1) and P2 (x 2, y 2);

Comparing the y coordinates of P1 and P2 to obtain a smaller value y _mix, and calculating y2;

y2＝y_mix*i(0.8≤i≤0.9)；

y3＝y2-y_surfaceAVG；

the image lowest point is connected, and then the image lowest point is translated upwards by a distance y3 to form a reference line L2, and at least 2 intersection points are necessarily formed between the reference line L2 and the tire pattern image;

Sequentially calculating values of y3-y _surface from left to right, recording as an intersection point when the difference value crosses 0 at any time, and recording the intersection point;

and calculating the number of the intersection points, and judging the tire layout according to the number of the intersection points.

From the above, the technical scheme provided by the invention can obtain the following beneficial effects:

Firstly, the technical proposal provided by the invention can reduce external interference by the definite geometric relationship between the tread depth and the pixel projection, thereby ensuring the accuracy of the detection result;

Secondly, by inputting the tire tread depth lookup table in advance, the technical scheme provided by the invention can avoid external interference, reduce the operation amount of the server in the actual test process, lighten the hardware pressure of the equipment, improve the calculation speed and improve the detection efficiency;

thirdly, the technical scheme provided by the invention can effectively identify the tire layout, so that the detection result is ensured to cover all tires comprehensively, and the applicability of the scheme to various different vehicle types is ensured.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.

FIG. 1 is a diagram showing the structure of a scanner in embodiment 1 of the present invention;

Fig. 2 is a schematic diagram of a front wheel image captured by the scanning device in embodiments 1 to 5 of the present invention;

fig. 3 is a schematic view of a front wheel image/rear wheel picture taken during the movement of the front wheel/rear wheel in embodiments 1 to 5 of the present invention;

FIG. 4 is a diagram showing the frame rate selection of the high-speed camera according to embodiments 2 to 5 of the present invention;

FIG. 5 is a calculation schematic diagram of the frame rate selection of the high-speed camera in embodiments 2 to 5 of the present invention;

FIG. 6 is a schematic diagram of the tire tread depth detection in the embodiment 2 and the embodiment 3 of the present invention;

FIG. 7 is a schematic structural diagram of the first case of the tire tread depth detection in the embodiments 4 and 5 of the present invention;

FIG. 8 is a schematic structural diagram of a second case of the tire tread depth detection in the embodiments 4 and 5 of the present invention;

FIG. 9 is a hypothetical imaging method for tire tread depth detection according to embodiments 4 and 5 of the present invention;

FIG. 10 is a schematic diagram of the tire layout algorithm identified in embodiments 2 and 4 of the present invention;

FIG. 11 is a schematic diagram of the tire layout algorithm identified in embodiments 3 and 5 of the present invention;

Fig. 12 is a schematic view of the tire layout identified in examples 2 to 5 of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are merely some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.

The existing tire tread depth can be deeply detected and obtained only through an instrument, but the detection result is easily influenced by a detection tool, a detection angle or a detection environment, so that the detection result is inaccurate, the safe use process of the tire is difficult to ensure, and meanwhile, the detection efficiency is low, and the requirement of rapid and accurate detection is difficult to meet.

Example 1

In order to solve the above-mentioned problems, the present embodiment proposes a device for measuring the tire tread depth by laser, which comprises a measurement support device 10, an in-car sensor 21, an out-car sensor 22, a high-speed camera 30, a laser emitter 40, and a photographing sensor 50.

Wherein, the measuring and supporting device 10 has a trapezoid structure, and the in-car sensor 21 and the out-car sensor 22 are arranged on the upper top surface of the measuring and supporting device 10 in parallel; high-speed camera 30, laser transmitter 40 are built into measurement support device 10; the laser emitter 40 and the photographing sensor 50 are positioned on the same straight line, and the straight line of the laser emitter 40 and the photographing sensor 50 is positioned at the center of the entrance sensor 21 and the exit sensor 22.

Preferably, but not limited to, the in-car sensor 21 and the out-car sensor 22 are symmetrically distributed along the vertical line where the laser transmitter 40 is located in the present embodiment.

Preferably, but not limited to, the upper top surface of the measurement supporting device 10 in this embodiment is a toughened glass structure, which can effectively transmit light and prevent dust from entering the measurement supporting device 10. Preferably, the glass surface is also provided with a degassing device which can effectively remove dust on the glass surface.

Preferably, but not limited to, the in-car sensor 21 and the out-car sensor 22 are both laser correlation sensors.

In this embodiment, the vehicle passes through the entrance sensor 21 and enters the measuring window corresponding to the laser emitter 40, and at this time, the linear laser emitted by the laser emitter 40 is perpendicular to the bottom surface of the testing device, and is reflected to the high-speed camera 30 after contacting the tire surface, so as to form a front wheel image or a rear wheel image.

In this embodiment, when the vehicle enters the detection preparation mode (for example, license plate information is collected), the degassing device may be turned on to keep the glass transparent, and detect whether the in-vehicle sensor 21 is activated, if so, the high-speed camera is turned on to enter the standby state, the laser generator is turned on, further, when the photographing sensor 50 is triggered for the first time, the front-wheel image is continuously photographed through laser imaging until the photographing sensor is triggered to end and when the out-of-vehicle sensor 22 is activated, the laser is turned off, when the in-vehicle sensor 21 and the photographing sensor 50 are activated again, the laser is turned on again and the rear-wheel image is continuously photographed until the photographing sensor 50 is triggered to end and when the out-of-vehicle sensor 22 is activated again, the laser is turned off.

Example 2

As shown in fig. 2 to 5, in order to solve the foregoing problems, the present embodiment proposes a method for measuring the tire tread depth by using a laser, the method including a first depth detection method and a first tire layout judgment algorithm, which mainly includes the following steps:

s1, acquiring vehicle information;

S2, detecting whether the front wheel enters a detection area, if so, acquiring a front wheel image through laser imaging, uploading the front wheel image to a server, then executing the next step, and if not, continuously detecting;

s3, detecting whether the rear wheel enters a detection area, if so, acquiring a rear wheel image through laser imaging, uploading the rear wheel image to a server, and then executing the next step, and if not, continuously detecting;

s4, the server marks the front wheel image and the rear wheel image, and respectively acquires the optimal front wheel image and the optimal rear wheel image;

s5, analyzing the optimal front wheel image and the optimal rear wheel image, identifying the tire layout, and calculating the tire tread depth of the front wheel and the tire tread depth of the rear wheel.

Preferably, in step S1, the vehicle information may be acquired by automatically identifying the license plate information to enter the license plate information, or by manually entering the license plate information.

More specifically, step S2 includes,

S21, detecting whether a vehicle entering sensor is triggered for the first time, if yes, starting a high-speed camera to enter a standby state, starting a laser generator, and if not, continuously detecting;

s22, detecting whether the photographing sensor is triggered for the first time, if so, continuously acquiring front-wheel images through laser imaging until the photographing sensor is triggered, executing the next step, and if not, continuously detecting;

S23, detecting whether the vehicle sensor is triggered for the first time, if so, turning off the laser, executing the next step, and if not, continuously detecting;

S24, uploading the acquired front wheel images to a server;

the step S3 of this method comprises the steps of,

S31, detecting whether the vehicle entering sensor is triggered again, starting a laser generator, and if not, continuously detecting;

s32, detecting whether the photographing sensor is triggered again, if so, continuously acquiring front-wheel images through laser imaging until the photographing sensor is triggered, executing the next step, and if not, continuously detecting;

s33, detecting whether the vehicle sensor is triggered again, if so, closing the laser and the high-speed camera, executing the next step, and if not, continuously detecting;

And S34, uploading the acquired rear wheel images to a server.

In this embodiment, the setting directions of the in-car sensor and the out-car sensor should be consistent with the moving direction of the vehicle, so as to ensure that the front wheels activate the in-car sensor and the out-car sensor in sequence, and the photographing sensor is arranged between the in-car sensor and the out-car sensor, so that the photographing front wheel image can be started and the photographing can be closed respectively, and the rear wheel activate the in-car sensor and the out-car sensor in sequence, so that the photographing rear wheel image can be started and the photographing can be closed respectively.

More specifically, before step S2, there is provided the step of:

SX1, any front wheel image or any rear wheel image with deviation from the optimal front wheel image or the optimal rear wheel image is obtained;

SX2, extremum the moving distance of the vehicle between the two exposure times of the high-speed camera, and make the moving distance equal to the horizontal distance between the center of the tire and the incident light;

SX3, in the extremum state, the test error of the current device,

SX4, in the extremum state, the lowest frame rate f _min of the high-speed camera;

T_min＝s_min/v_max；

Δt_max＜T_min/(n_min-1)；

1/f_min＜Δt_max；

SX5, presetting a test error of equipment, and calculating the lowest frame rate f _min of the high-speed camera;

SX6, setting the frame rate f of the high-speed camera, wherein f is more than or equal to f _min;

Wherein v is the speed of the tire during passing through the photographing sensor, and the default vehicle passes at a constant speed; s is the distance the vehicle moves during triggering of the photographing sensor; r _min is the minimum value of the tire radius; Δt _max is the maximum value of the adjacent two exposure time differences; h _min is the minimum height of the photographing sensor.

As shown in fig. 4 and fig. 5, the position right above the deviation measuring port is selected as the analysis object, the horizontal distance between the center of the tire and the incident light is defined as the distance of the vehicle movement between the adjacent two exposure times, and the distance of the vehicle movement between the adjacent two exposure times is necessarily smaller than the distance of the vehicle movement between the adjacent two exposure times in the actual process.

Assuming that incident laser crosses the tire surface at E, the bottom of an incident laser crossing groove is at H, the incident laser crossing groove is perpendicular to EH and extends to cross the J point through I, the center positions I and H of the tire are connected at the moment, the incident laser crossing groove extends to the K point on the tire surface, the uniform vehicle speed is defined as v, the tire radius is r, the time between two adjacent exposures is delta t, and the frame rate of a camera is f;

△t＝1/f；

test error= (HE/HK-1)% = (1/cos (+ KHE) -1)%;

since the tread depth is small relative to the tire radius, ki=hi=r, so sin (= JHI) =ji/ih=ji/ik= (vehicle speed x time between adjacent exposures)/tire radius=v x Δt/r; since the angle KHE is small, the triangle HKE is approximately considered to be a right triangle, so the triangle has

cos(∠KHE)＝KH/HE；

And = -KHE = -JHI;

The trigonometric function formula:

cos(∠KHE)²+sin(∠KHE)²＝1；

So 1/cos (+ KHE) =1/[ 1-sin (+ KHE) ²]^1/2;

Namely, the test error= [ (1-sin (< KHE) ²)^-1/2 -1]%, which is proved by substituting into a formula:

In general, the diameter of a tire for an automobile is 600mm to 800mm, and a small-sized tire is selected for analysis by using an extremum method, mainly because the smaller the tire, the shorter the time to pass through a photographing sensor, the faster the time to pass through the photographing sensor, and therefore, assuming that the tire radius r=300 mm, the maximum passing time v=10 km/h is set for the selected vehicle speed within an allowable range, the acceptance error is set to 0.1%, at this time Δt=5 ms is calculated, and the camera frame rate corresponds to 200fps.

Then, assuming that the height of the photographing sensor is h, the radius of the tire is r, and the vehicle speed is v, the test interval is as follows:

vehicle distance of movement = 2 x [ r ²-(r-h)²]^1/2;

Total photographing time t=vehicle moving distance/v;

Then T < T/(n-1),

In this embodiment, the shorter the photographing interval time is, the more the number of the obtained photos is, the closer the mth photo is to the position right above, but the shorter the photographing interval time is, the higher the requirement for the camera is, i.e. the larger the frame rate is, the more expensive the camera is, and the more data the server processes is, so that a proper photographing interval, i.e. a proper frame rate, needs to be selected, and the proper photographing interval can be calculated specifically by an error.

In a normal case, the height of the photographing sensor is a fixed value of the equipment, the range is 10 mm-20 mm, and the radius r of the tire is 300mm under the assumption that h=10 mm; deriving from the above formula, if the device error is less than 0.1%, the frame rate of the camera is 200fps; substitution proves that the vehicle travel distance is 0.33m, where a value of n _min is obtained as 12.

Therefore, in SX2 of the present embodiment, the high speed camera is set so that the apparatus can capture a minimum of 12 tire tread images, i.e., n.gtoreq.12, during the triggering of the photo sensor.

More specifically, the step S4 includes:

s41, the server marks the front wheel image and the rear wheel image;

s42, numbering a plurality of front-wheel images or a plurality of rear-wheel images in sequence, and marking n (n E [ 1, k ]);

s43, selecting an mth picture as the optimal front wheel image or the optimal rear wheel image;

m＝int(n/2)；

wherein n is equal to the number of images acquired between two exposure times of the high-speed camera, and n is not less than n _min.

In the present embodiment, the front wheel image and the rear wheel image are marked in step S41, and the images may be marked by the order in which the images are collected by the server, for example, the image preferentially acquired in the traveling direction of the vehicle is the front wheel image, and may be also marked as the left front wheel image or the right front wheel image.

Preferably, the vehicle in this embodiment is moving at a constant speed when passing over the device, so the photograph taken by the mth image is considered to be the image of the tire immediately above the measurement port, i.e., case 2 in fig. 3.

More specifically, step S5 includes:

s51, identifying a corresponding tire layout according to the tire tread image;

s52, acquiring the front tire tread depth in the optimal front wheel image and the rear tire tread depth in the optimal rear wheel image.

More specifically, before step S5, a step is provided,

SX7, setting the position of the high-speed camera, including an included angle with the horizontal plane and a horizontal distance from the laser generator, so that all the pattern of the tire during photographing are in the visual field of the high-speed camera;

SX8, the arbitrary vertex of the front wheel image or the rear wheel image is taken as an original point, the axial direction of the tire is taken as an x axis, and the depth direction of the groove is taken as a y axis to establish a coordinate system;

more specifically, step S52 includes,

S521, preprocessing the optimal front wheel image or the optimal rear wheel image, and screening to obtain a tire pattern image;

S522, acquiring y coordinates of all points on the surface of the tire, and taking an average value as y _surfaceAVG;

S523, acquiring position information of all grooves of the tire and corresponding y coordinates, taking an average value of the y coordinates on each groove, and marking as y _grooveAVGj (j E [1, k');

s524, calculating the number of pixel points corresponding to the depth of each groove;

ΔS_pixeln＝y_grooveAVGj-y_surfaceAVG；

S525, calculating the tread depth of each groove according to the geometric relationship and the trigonometric function relationship;

S＝[tan(α+β)-tanα]*L；

β＝tan^-1((S_centre+S_pixel)/f _{Image distance} )-tan^-1(S_centre/f _{Image distance} )；

S_pixel＝b _{Pixel element} *ΔS_pixel；

S_centre＝b _{Pixel element} *ΔS_centre；

Wherein, α is the included angle between the high-speed camera and the horizontal plane, L is the horizontal distance from the high-speed camera to the laser transmitter, f _{Image distance} is the image distance, B _{Pixel element} is the pixel size, Δs _pixel is the number of pixels projected on the high-speed camera by the tire tread depth, S _pixel is the length of the number of pixels corresponding to the tire tread depth, Δs _centre is the number of pixels projected on the high-speed camera from the center point of the photosensitive chip in the pixel direction, and S _centre is the length of the number of pixels corresponding to the tire tread depth.

As shown in fig. 2, an incident laser beam is defined to intersect the tire surface at point P, the reflected light beam passes through the center point O of the lens of the high-speed camera, and is imaged at point P 'on the photosensitive chip, and similarly, the incident light beam intersects the bottom of the tire groove at point R, the reflected light beam passes through the center point O of the lens of the high-speed camera, and is imaged at point R' on the photosensitive chip, the center position of the photosensitive chip is O ', the angle between the camera and the horizontal plane is known as α, the horizontal distance from the high-speed camera to the incident light beam, that is, the OQ length is L, the image distance OO' of the camera is f, the pixel length of O 'P' is S _base, the pixel length of P 'R' is S _pixel, and the tire depth RP (S) is calculated.

In Δo 'R' O, +_o 'OR' =tan ^-1((S_centre+S_pixel)/f _{Image distance} );

in Δo 'P' O, +_o 'OP' =tan ^-1(S_centre/f _{Image distance} );

so that ,∠P'OR'＝∠O'OR'-∠O'OP'＝tan^-1((S_centre+S_pixel)/f _{Image distance} )-tan^-1(S_centre/f _{Image distance} );

Because = = -P 'OR';

So +.beta=tan ^-1((S_centre+S_pixel)/f _{Image distance} )-tan^-1(S_centre/f _{Image distance} );

in Δorq, rq=l×tan (α+β);

in Δopq, pq=l tan α;

The tread depth s=pr=rq-pq=l [ tan (α+β) -tan α ].

Preferably, but not limited to, in the present embodiment SX5, assuming that the resolution of the image obtained by the high-speed camera is a×b, the imaging position direction is fixed by adjusting the position of the camera, for example, the sipe direction is imaged in the B pixel direction, and the tire surface direction is imaged in the a pixel direction.

Preferably, in the coordinate system established in step SX6 in this embodiment, the minimum unit of the coordinate axis is a pixel point, that is, each point of the coordinate area represents one pixel.

In this embodiment, the tire tread depth detected by the method mainly has a definite geometric relationship with the tire tread depth on the pixel projection, that is, the result can be calculated through the proportional relationship in the technical scheme of the application, and the tire tread depth is not interfered by the outside.

More specifically, in step S51, identifying the corresponding tire layout includes:

Calling a system coordinate system;

screening the tire pattern images of the optimal front wheel image and the optimal rear wheel image;

The reference line L1 (curve 2 in fig. 5) is formed by the upward translation distance s after connecting the lowest point of the pattern image (curve 1 in fig. 5);

s＝S*h*C；

The ordinate of the point on the reference line L1 is marked as y1;

The y coordinates of all points on the tire surface are marked as y _surface;

calculating the difference values of y1-y _surface in sequence from left to right, recording as an intersection point when the difference value crosses 0 at any time, and recording the intersection point;

Calculating the number of the intersection points, and judging the layout of the tire according to the number of the intersection points;

Wherein C is the number of pixels in the y-axis direction corresponding to each millimeter of the actual tread, and if c=25, the depth of each millimeter of the tread needs to be represented by 25 pixels in the y-axis direction of the image; s is the maximum tread depth, the unit is millimeter, and the factory depth of the general automobile tire tread is a fixed value, such as 10mm; h is a proportionality coefficient, and h is more than or equal to 1.1 and less than or equal to 1.2.

In the present embodiment, when the number of intersection points is 2, it is determined that the tires of the dolly are laid out, that is, 2 tires each in front and rear; when the number of intersection points is 4, it is judged that the double tire layout is made.

Example 3

As shown in fig. 4 to 6, in order to solve the foregoing problems, the present embodiment proposes a method for measuring the tire tread depth by using a laser, the method including a first depth detection method and a second tire layout judgment algorithm, specifically including the remaining steps except step S51 in embodiment 2, wherein the difference is that in step S51, identifying the corresponding tire layout includes:

Calling a system coordinate system;

screening the tire pattern images of the optimal front wheel image and the optimal rear wheel image;

smoothing the tire pattern images of the front wheel image and the rear wheel video; preferably, the smoothing process can be performed by performing an open operation on the image and then performing polynomial fitting;

The y coordinates of all points on the tire surface are marked as y _surface;

acquiring two point coordinates of the leftmost side and the rightmost side of the image, and respectively marking as P1 (x 1, y 1) and P2 (x 2, y 2);

Comparing the y coordinates of P1 and P2 to obtain a smaller value y _mix, and calculating y2;

y2＝y_mix*i(0.8≤i≤0.9)；

y3＝y2-y_surfaceAVG；

After connecting the lowest points of the images (curve 3 in fig. 6), translating upwards by a distance y2 to form a reference line L2 (curve 4 in fig. 6), and at least 2 intersection points are necessarily present between the reference line L2 and the tread pattern image;

Sequentially calculating values of y3-y _surface from left to right, recording as an intersection point when the difference value crosses 0 at any time, and recording the intersection point;

and calculating the number of the intersection points, and judging the tire layout according to the number of the intersection points.

In this embodiment, the value of i is adjusted according to the actual imaging effect of the laser camera.

In this embodiment, if there is no focus, it is determined that the tires of the trolley are arranged, that is, 2 tires each in front and rear; if the number of the intersection points is 2, the double-tire layout is judged.

Example 4

As shown in fig. 7 to 12, in order to solve the foregoing problems, the present embodiment proposes a method for measuring the tire tread depth by using a laser, which includes a second depth detection method and a first tire layout judgment algorithm, and specifically includes the steps except steps SX5 to SX8 and step S52 in embodiment 2, wherein the difference is that:

before step S5, a step is provided,

SX7, setting the position of the high-speed camera, including an included angle with the horizontal plane and a horizontal distance from the laser generator, so that all the pattern of the tire during photographing are in the visual field of the high-speed camera;

SX8, a coordinate system is established, any vertex of the image is taken as an origin, the direction of a B pixel is taken as a Y axis, the direction of an A pixel is taken as an X axis, and a single pixel point is taken as a minimum unit;

SX9, the incident ray of the high-speed camera is arranged to intersect with the measuring port at a point u, and the point u is imaged at the central position u' of the camera photosensitive chip in the B pixel direction;

SX10, judging the magnitudes of the incident light and the measurement port in the u 'coordinate and the j value of the minimum unit coordinate in the Y-axis direction, if the j value of the minimum unit coordinate is larger than u', executing step SX11, otherwise, executing step SX12;

SX11, calculating the length b _j of each pixel point corresponding to the real world on the Y axis,

b_j＝[tan)α+β₁+γ₁)-tan(α+β₁)]*L；

β₁＝tan^-1(ΔS_spixelβ1*b _{Pixel element} /f _{Image distance} )；

γ₁＝tan^-1((ΔS_spixelβ1+ΔS_spixelγ1)*b _{Pixel element} /f _{Image distance} )-β₁；

SX12, calculating the length b _j of each pixel point on the Y axis corresponding to the real world,

b_j＝[tan(α-β₂)-tan(α-β₂-γ₂)]*L；

β₂＝tan^-1(ΔS_spixelβ2*b _{Pixel element} /f _{Image distance} )；

γ₂＝tan^-1))ΔS_spixelβ2+ΔS_spixelγ2)*b _{Pixel element} /f _{Image distance} )-β₂；

SX13, building a tire tread depth lookup table;

The step S52 includes the steps of,

S521, obtaining a test pattern image;

S522, acquiring position information and corresponding y coordinates of all grooves of the tire, taking an average value of the y coordinates on the corresponding grooves, and marking as y _grooveAVGj (j E [1, k');

s523, acquiring y coordinates of all points on the surface of the tire, and taking an average value y _surfaceAVG;

S524, obtaining data b _j according to the tire tread depth lookup table and the real world distances represented by the lengths of all the pixels from y _surfaceAVG to y _grooveAVGj in the lookup table;

s525, accumulating the data b _j to obtain the tread depth;

Wherein, alpha is the included angle between the incident light of the high-speed camera and the horizontal plane, L is the horizontal distance from the high-speed camera to the laser transmitter, f is the image distance, B is the resolution of the depth direction of the groove, B is the dimension of the pixel direction of the high-speed camera, deltaS _pixel is the number of pixels projected on the high-speed camera by the tire tread depth, S _pixel is the length of the number of corresponding pixels, deltaS _centre is the number of pixels projected on the high-speed camera from the center point of the pixel direction of the photosensitive chip B, and S _centre is the length of the number of corresponding pixels; b _j' is the real world distance corresponding to the y _surfaceAVG coordinates, j e [ 1, k' ]; b _j" is that the Y _grooveAVGj coordinates correspond to real world distances, j 'E [ j', k ]. b _j is the real world distance corresponding to the pixel point with the Y coordinate of j, j epsilon [ j ', j'.

In this embodiment, each pixel corresponds to each distance on the incident light within the high-speed camera field of view.

Taking a specific example for analysis, consider a camera with a pixel size of 1024 in the B direction, a pixel distance of 6mm and a pixel size of 4.8 μm as an example, wherein the included angle α between the high-speed camera and the horizontal plane is equal to 40 °, the horizontal distance L from the high-speed camera to the incident light is 320mm, and the real distance lengths represented by three pixel points (B _spixel1、b_spixel512、b_spixel1024 in the figure) with Y coordinates of 1, 512 and 1024, respectively, i.e., the value of B ₁,b₅₁₂,b₁₀₂₄, are calculated. Substituting the following numerical values into a formula for pixel points with coordinates greater than or equal to 512, wherein DeltaS _pixelβ and DeltaS _pixelγ respectively represent the pixel points corresponding to beta and gamma;

Pixel point with y coordinate 512:

△_Spixelβ＝0

△_Spixelγ＝1

b₅₁₂＝0.438mm

pixel point with y coordinate 1024:

△_Spixelβ＝511

△_Spixelγ＝1

b₁₀₂₄＝1.005mm

for pixel points with coordinate positions between 0 and 512, the calculation formula needs to be adjusted:

Pixel point with y coordinate 1

△_Spixelβ＝511

△_Spixelγ＝1

b₀＝0.2401mm

Preferably, the tire tread depth lookup table established in step SX10 is as follows, and the abscissa of the lookup table is the Y-coordinate value of the pixel, and the ordinate is the real world distance represented by the pixel point length corresponding to the corresponding coordinate value.

Y coordinates	bn
		1	b1
2	b2
		......	......
1024	B1024

In this embodiment, the relationship between the y-coordinate and the tread depth is calculated in advance, and is imported into the system in a list or other form, and a "tread depth lookup table" is created, where the table contains two types of data, which are respectively the y-axis coordinate and the real world length represented by the pel size in the pixel B pixel direction of the corresponding position, for example, data (512,0.438) represents that when the y-axis coordinate of the bottom pixel of the trench is 512, the real world length tread depth represented by the pel size in the pixel B direction is 0.438mm. During testing, we obtain the y-axis coordinate information of the pixels at the bottom of the grooves of the tire pattern image, such as y620, the y-axis coordinate information of the pixels at the tire surface of the tire pattern image, such as y515, by processing the images, compare the "tire pattern depth lookup table" according to the yn information to find all the values in the interval from b515 to b620, and calculateThe tread depth is/>

Example 5

As shown in fig. 2 to 5, in order to solve the foregoing problems, the present embodiment proposes a method for measuring the tire tread depth by using a laser, which includes a second depth detection method and a second tire layout judgment algorithm, and specifically includes the steps except steps SX5 to SX8 and step S52 in embodiment 2, wherein the difference is that:

before step S5, a step is provided,

SX7, setting the position of the high-speed camera, including an included angle with the horizontal plane and a horizontal distance from the laser generator, so that all the pattern of the tire during photographing are in the visual field of the high-speed camera;

SX8, establishing a coordinate system, taking any vertex of the image as an origin, taking the direction of a B pixel as a Y axis, taking the direction of an A pixel as an X axis, and taking a single pixel point as a minimum unit;

SX9, the incident ray of the high-speed camera is arranged to intersect with the measuring port at a point u, and the point u is imaged at the central position u' of the camera photosensitive chip in the B pixel direction;

SX10, judging the magnitudes of the incident light and the measuring port in the u 'coordinate and the n value of the minimum unit coordinate in the Y-axis direction, if the n value of the minimum unit coordinate is larger than u', executing step SX11, otherwise, executing step SX12;

SX11, calculating the length b _j of each pixel point corresponding to the real world on the Y axis,

b_j＝[tan(α+β₁+γ₁)-tan(α+β₁0]*L；

β₁＝tan^-1(ΔS_spixelβ1*b _{Pixel element} /f _{Image distance} )；

γ₁＝tan^-1((ΔS_spixelβ1+ΔS_spixelγ1)*b _{Pixel element} /f _{Image distance} )-β₁；

SX12, calculating the length b _j of each pixel point on the Y axis corresponding to the real world,

b_j＝[tan(α-β₂)-tan(α-β₂-γ₂)]*L；

β₂＝tan^-1(ΔS_spixelβ2*b _{Pixel element} /f _{Image distance} )；

γ₂＝tan^-1((ΔS_spixelβ2+ΔS_spixelγ2)*b _{Pixel element} /f _{Image distance} )-β₂；

SX13, building a tire tread depth lookup table;

The step S52 includes the steps of,

S521, obtaining a test pattern image;

S522, acquiring position information and corresponding y coordinates of all grooves of the tire, taking an average value of the y coordinates on the corresponding grooves, and marking as y _grooveAVGj (j E [1, k');

s523, acquiring y coordinates of all points on the surface of the tire, and taking an average value y _surfaceAVG;

S524, obtaining data b _j according to the tire tread depth lookup table and the real world distances represented by the lengths of all the pixels from y _surfaceAVG to y _grooveAVGj in the lookup table;

s525, accumulating the data b _j to obtain the tread depth;

Wherein, α is the included angle between the incident light of the high-speed camera and the horizontal plane, L is the horizontal distance from the high-speed camera to the laser transmitter, f is the image distance, B is the resolution of the depth direction of the groove, B is the dimension of the pixel direction of the high-speed camera, Δs _pixel is the number of pixels projected on the high-speed camera by the tire tread depth, S _pixel is the length of the corresponding number of pixels, Δs _centre is the number of pixels projected on the high-speed camera from the center point of the pixel direction of the photosensitive chip, and S _centre is the length of the corresponding number of pixels; b _j is the real world distance corresponding to the y _surfaceAVG coordinates, j e [1, k' ]; b _j" is that the Y _grooveAVGj coordinates correspond to real world distances, j 'E [ j', k ]. b _j is the real world distance corresponding to the pixel point with the Y coordinate of j, j epsilon [ j ', j'.

In this embodiment, each pixel corresponds to each distance on the incident light within the high-speed camera field of view.

Taking a specific example for analysis, consider a camera with a pixel size of 1024 in the B direction, a pixel distance of 6mm and a pixel size of 4.8 μm as an example, wherein the included angle α between the high-speed camera and the horizontal plane is equal to 40 °, the horizontal distance L from the high-speed camera to the incident light is 320mm, and the real distance lengths represented by three pixel points (B _spixel1、b_spixel512、b_spixel1024 in the figure) with Y coordinates of 1, 512 and 1024, respectively, i.e., the value of B ₁,b₅₁₂,b₁₀₂₄, are calculated. Substituting the following numerical values into a formula for pixel points with coordinates greater than or equal to 512, wherein DeltaS _pixelβ and DeltaS _pixelγ respectively represent the pixel points corresponding to beta and gamma;

Pixel point with y coordinate 512:

△_Spixelβ＝0

△_Spixelγ＝1

b₅₁₂＝0.438mm

pixel point with y coordinate 1024:

△_Spixelβ＝511

△_Spixelγ＝1

b₁₀₂₄＝1.005mm

for pixel points with coordinate positions between 0 and 512, the calculation formula needs to be adjusted:

Pixel point with y coordinate 1

△_Spixelβ＝511

△_Spixelγ＝1

b₀＝0.2401mm

Preferably, the tire tread depth lookup table established in step SX10 is as follows, and the abscissa of the lookup table is the Y-coordinate value of the pixel, and the ordinate is the real world distance represented by the pixel point length corresponding to the corresponding coordinate value.

/>

In this embodiment, the relationship between the y-coordinate and the tread depth is calculated in advance, and is imported into the system in a list or other form, and a "tread depth lookup table" is created, where the table contains two types of data, which are respectively the y-axis coordinate and the real world length represented by the pel size in the pixel B pixel direction of the corresponding position, for example, data (512,0.438) represents that when the y-axis coordinate of the bottom pixel of the trench is 500, the real world length tread depth represented by the pel size in the pixel B direction is 0.438mm. During testing, we obtain the y-axis coordinate information of the pixels at the bottom of the grooves of the tire pattern image, such as y ₆₂₀, the y-axis coordinate information of the pixels at the tire surface of the tire pattern image, such as y ₅₁₅, compare the "tire pattern depth lookup table" according to the y _n information to find all the values in the interval b515 to b620, and calculateThe tread depth is/>

It should be emphasized that SX11 and SX12 of the depth detection algorithms in embodiments 4 and 5 represent that u 'is imaged above the u point and u' is imaged below the u point, respectively, and that in the case of a tread pattern, the tread pattern only exists above the u point. Therefore, the depth detection algorithms in embodiments 4 and 5 can be extended, and are not limited to the tire tread depth detection.

Further, in embodiments 2 to 5, after step S5, an appropriate user interface may be selected according to the identified tire layout, and the tire pattern depth information and the corresponding tire position information may be displayed on the corresponding user interface.

In summary, the device and the method for testing the tire tread depth by using the laser according to embodiments 1 to 5 acquire the tire tread image by means of the laser vertical incidence, and then improve the detection accuracy by selecting a proper photographing time interval and an image selecting method, and meanwhile, the device and the method are also provided with an AI algorithm capable of identifying the tire layout, so as to realize automatic identification of the tire layout.

The above-described embodiments do not limit the scope of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the above embodiments should be included in the scope of the present invention.

Claims (12)

1. A method of laser measurement of tire tread depth comprising the steps of:

s1, acquiring vehicle information;

s2, detecting whether a front wheel enters a detection area, if so, acquiring a front wheel image through laser imaging, uploading the front wheel image to a server, then executing the next step, and if not, continuously detecting;

S3, detecting whether the rear wheel enters a detection area, if so, acquiring a rear wheel image through laser imaging, uploading the rear wheel image to a server, and then executing the next step, and if not, continuously detecting;

S4, the server marks the front wheel image and the rear wheel image, and respectively acquires an optimal front wheel image and an optimal rear wheel image;

S5, analyzing the optimal front wheel image and the optimal rear wheel image, identifying the tire layout, and then calculating the tire tread depth of the front wheel and the tire tread depth of the rear wheel;

The step S5 of said step comprises the steps of,

S51, identifying a corresponding tire layout according to the tire tread image;

s52, acquiring the front tire tread depth in the optimal front wheel image and the rear tire tread depth in the optimal rear wheel image;

before said step S5, a step is provided,

SX7, setting the position of the high-speed camera, including an included angle with the horizontal plane and a horizontal distance from the laser generator, so that all the pattern of the tire during photographing are in the visual field of the high-speed camera;

SX8, the arbitrary vertex of the front wheel image or the rear wheel image is taken as an original point, the axial direction of the tire is taken as an x axis, and the depth direction of the groove is taken as a y axis to establish a coordinate system;

the step S52 includes the steps of,

S521, preprocessing the optimal front wheel image or the optimal rear wheel image, and screening to obtain a tire pattern image;

S522, acquiring y coordinates of all points on the surface of the tire, and taking an average value as y _surfaceAVG;

S523, acquiring position information of all grooves of the tire and corresponding y coordinates, taking an average value of the y coordinates on each groove, and marking as y _grooveAVGj (j E [1, k');

s524, calculating the number of pixel points corresponding to the depth of each groove;

ΔS_pixeln＝y_grooveAVGj-y_surfaceAVG；

S525, calculating the tread depth S of each groove according to the geometric relationship and the trigonometric function relationship;

S＝[tan(α+β)-tanα]*L；

β＝tan^-1((S_centre+S_pixel)/f _{Image distance} )-tan^-1(S_centre/f _{Image distance} )；

S_pixel＝b _{Pixel element} *ΔS_pixel；

S_centre＝b _{Pixel element} *ΔS_centre；

Wherein, alpha is the included angle between the high-speed camera and the horizontal plane, L is the horizontal distance from the high-speed camera to the laser transmitter, f _{Image distance} is the image distance, B _{Pixel element} is the pixel size, deltaS _pixel is the number of pixels projected on the high-speed camera by the tire tread depth, S _pixel is the length of the corresponding number of pixels, deltaS _centre is the number of pixels projected on the high-speed camera by the tire surface from the center point of the pixel direction of the photosensitive chip B, and the pixel direction B is the depth direction of the groove of the tire; s _centre is the length of the corresponding pixel number.

2. A method of laser measuring tire tread depth as in claim 1 wherein:

The step S2 of said step comprises the steps of,

S21, detecting whether a vehicle entering sensor is triggered for the first time, if yes, starting a high-speed camera to enter a standby state, starting a laser generator, and if not, continuously detecting;

S22, detecting whether the photographing sensor is triggered for the first time, if so, continuously acquiring the front-wheel image through laser imaging until the photographing sensor is triggered, executing the next step, and if not, continuously detecting;

S23, detecting whether the vehicle sensor is triggered for the first time, if so, turning off the laser, executing the next step, and if not, continuously detecting;

S24, uploading the acquired front wheel images to a server;

the step S3 of this method comprises the steps of,

S31, detecting whether the vehicle entering sensor is triggered again, starting a laser generator, and if not, continuously detecting;

S32, detecting whether the photographing sensor is triggered again, if so, continuously acquiring the front-wheel image through laser imaging until the photographing sensor is triggered, executing the next step, and if not, continuously detecting;

S33, detecting whether the departure sensor is triggered again, if so, closing the laser and the high-speed camera, executing the next step, and if not, continuously detecting;

and S34, uploading the acquired rear wheel images to a server.

3. A method of laser measurement of the tread depth of a tyre according to claim 2, characterized in that, before said step S2, there is provided the step of:

SX1, any front wheel image or any rear wheel image with deviation from the optimal front wheel image or the optimal rear wheel image is obtained;

SX2, extremuing the moving distance of the vehicle between the two exposure times of the high-speed camera to enable the moving distance to be equal to the horizontal distance between the center of the tire and the incident light;

SX3, in the extremum state, the test error of the current device,

SX4, in the extremum state, the lowest frame rate f _min of the high-speed camera;

T_min＝s_min/v_max；

Δt_max＜T_min/(n_min-1)；

1/f_min＜Δt_max；

SX5, presetting a test error of the equipment, and calculating the lowest frame rate f _min of the high-speed camera;

SX6, setting the frame rate f of the high-speed camera, wherein f is more than or equal to f _min;

Wherein v is the speed of the tire during passing through the photographing sensor, and the default vehicle passes at a constant speed; s is the distance the vehicle moves during triggering of the photographing sensor; r _min is the minimum value of the tire radius; Δt _max is the maximum value of the adjacent two exposure time differences; h _min is the minimum height of the photographing sensor.

4. A method for measuring the tire tread depth by laser light according to claim 3, wherein said step S4 comprises:

s41, the server marks the front wheel image and the rear wheel image;

s42, numbering a plurality of front-wheel images or a plurality of rear-wheel images in sequence, and marking n (n E [ 1, k ]);

s43, selecting an mth picture as the optimal front wheel image or the optimal rear wheel image;

m＝int(n/2)；

Wherein n is equal to the number of images acquired between the two exposure times of the high-speed camera, and n is not less than n _min.

5. A method of measuring the tread depth of a tire with a laser as in any one of claims 1 to 4, wherein in step S51, identifying the corresponding tire layout comprises:

Calling a system coordinate system;

screening the tire pattern images of the optimal front wheel image and the optimal rear wheel image;

The lowest point of the tire pattern image is connected, and then the upward translation distance s is formed into a reference line L1;

s＝S*h*C；

The ordinate of the point on the reference line L1 is marked as y1;

the y coordinates of all points on the surface of the tire are marked as y _surface;

calculating the difference values of y1-y _surface in sequence from left to right, recording as an intersection point when the difference value crosses 0 at any time, and recording the intersection point;

calculating the number of the intersection points, and judging the tire layout according to the number of the intersection points;

Wherein, C is the number of pixels in the corresponding y-axis direction represented by the actual tire tread per millimeter; s is the maximum tread depth; h is a proportionality coefficient, and h is more than or equal to 1.1 and less than or equal to 1.2.

6. A method of measuring the tread depth of a tire with a laser as in any one of claims 1 to 4, wherein in step S51, identifying the corresponding tire layout comprises:

Calling a system coordinate system;

screening the tire pattern images of the optimal front wheel image and the optimal rear wheel image;

smoothing the tire pattern images of the front wheel image and the rear wheel video;

the y coordinates of all points on the surface of the tire are marked as y _surface;

acquiring two point coordinates of the leftmost side and the rightmost side of the image, and respectively marking as P1 (x 1, y 1) and P2 (x 2, y 2);

Comparing the y coordinates of P1 and P2 to obtain a smaller value y _mix, and calculating y2;

y2＝y_mix*i(0.8≤i≤0.9)；

y3＝y2-y_surfaceAVG；

the image lowest point is connected, and then the image lowest point is translated upwards by a distance y3 to form a reference line L2, and at least 2 intersection points are necessarily formed between the reference line L2 and the tire pattern image;

Sequentially calculating values of y3-y _surface from left to right, recording as an intersection point when the difference value crosses 0 at any time, and recording the intersection point;

and calculating the number of the intersection points, and judging the tire layout according to the number of the intersection points.

7. A method of laser measurement of tire tread depth comprising the steps of:

s1, acquiring vehicle information;

s2, detecting whether a front wheel enters a detection area, if so, acquiring a front wheel image through laser imaging, uploading the front wheel image to a server, then executing the next step, and if not, continuously detecting;

S3, detecting whether the rear wheel enters a detection area, if so, acquiring a rear wheel image through laser imaging, uploading the rear wheel image to a server, and then executing the next step, and if not, continuously detecting;

S4, the server marks the front wheel image and the rear wheel image, and respectively acquires an optimal front wheel image and an optimal rear wheel image;

S5, analyzing the optimal front wheel image and the optimal rear wheel image, identifying the tire layout, and then calculating the tire tread depth of the front wheel and the tire tread depth of the rear wheel;

The step S5 of said step comprises the steps of,

S51, identifying a corresponding tire layout according to the tire tread image;

s52, acquiring the front tire tread depth in the optimal front wheel image and the rear tire tread depth in the optimal rear wheel image;

before said step S5, a step is provided,

SX7, setting the position of the high-speed camera, including an included angle with the horizontal plane and a horizontal distance from the laser generator, so that all the pattern of the tire during photographing are in the visual field of the high-speed camera;

SX8, a coordinate system is established, any vertex of the image is taken as an origin, the direction of a B pixel is taken as a Y axis, the direction of an A pixel is taken as an X axis, and a single pixel point is taken as a minimum unit;

SX9, the incident ray of the high-speed camera is arranged to intersect with the measuring port at a point u, and the point u is imaged at the central position u' of the camera photosensitive chip in the B pixel direction;

SX10, judging the magnitudes of the incident light and the measuring port in the u 'coordinate and the n value of the minimum unit coordinate in the Y-axis direction, if the n value of the minimum unit coordinate is larger than u', executing step SX11, otherwise, executing step SX12;

SX11, calculating the length b _j of each pixel point corresponding to the real world on the Y axis,

b_j＝[tan(α+β₁+γ₁)-tan(α+β₁)]*L；

β₁＝tan^-1(ΔS_spixelβ1*b _{Pixel element} /f _{Image distance} )；

γ₁＝tan^-1((ΔS_spixelβ1+ΔS_spixelγ1)*b _{Pixel element} /f _{Image distance} )-β₁；

SX12, calculating the length b _j of each pixel point on the Y axis corresponding to the real world,

b_j＝[tan(α-β₂)-tan(α-β₂-γ₂)]*L；

β₂＝tan^-1(ΔS_spixelβ2*b _{Pixel element} /f _{Image distance} )；

γ₂＝tan^-1((ΔS_spixelβ2+ΔS_spixelγ2)*b _{Pixel element} /f _{Image distance} )-β₂；

SX13, building a tire tread depth lookup table;

the step S52 includes the steps of,

S521, obtaining a test pattern image;

S522, acquiring position information and corresponding y coordinates of all grooves of the tire, taking an average value of the y coordinates on the corresponding grooves, and marking as y _grooveAVGj (j E [1, k');

s523, acquiring y coordinates of all points on the surface of the tire, and taking an average value y _surfaceAVG;

S524, obtaining data b _j according to the tire tread depth lookup table and the real world distances represented by the lengths of all the pixel points from y _surfaceAVG to y _grooveAVGj in the lookup table;

S525, accumulating the data b _j to obtain the tread depth;

Wherein, alpha is the included angle between the incident light of the high-speed camera and the horizontal plane, L is the horizontal distance from the high-speed camera to the laser transmitter, f _{Image distance} is the image distance, B the pixel direction is the depth direction of the tire groove, B is the size of the pixel direction of the high-speed camera, deltaS _pixel is the number of pixels projected on the high-speed camera by the tire groove depth, S _pixel is the length of the number of corresponding pixels, deltaS _centre is the number of pixels projected on the high-speed camera from the center point of the pixel direction of the photosensitive chip B, and S _centre is the length of the number of corresponding pixels; b _j' is the real world distance corresponding to the y _surfaceAVG coordinates, j 'e [ 1, k'; b _j" is the real world distance corresponding to the Y _grooveAVGn coordinate, j ' E [ j ', k ', b _j is the real world distance corresponding to the pixel point with Y coordinate j, j E [ j ', j '.

8. A method of laser measuring tire tread depth as in claim 7 wherein:

The step S2 of said step comprises the steps of,

S21, detecting whether a vehicle entering sensor is triggered for the first time, if yes, starting a high-speed camera to enter a standby state, starting a laser generator, and if not, continuously detecting;

S22, detecting whether the photographing sensor is triggered for the first time, if so, continuously acquiring the front-wheel image through laser imaging until the photographing sensor is triggered, executing the next step, and if not, continuously detecting;

S23, detecting whether the vehicle sensor is triggered for the first time, if so, turning off the laser, executing the next step, and if not, continuously detecting;

S24, uploading the acquired front wheel images to a server;

the step S3 of this method comprises the steps of,

S31, detecting whether the vehicle entering sensor is triggered again, starting a laser generator, and if not, continuously detecting;

S32, detecting whether the photographing sensor is triggered again, if so, continuously acquiring the front-wheel image through laser imaging until the photographing sensor is triggered, executing the next step, and if not, continuously detecting;

S33, detecting whether the departure sensor is triggered again, if so, closing the laser and the high-speed camera, executing the next step, and if not, continuously detecting;

and S34, uploading the acquired rear wheel images to a server.

9. A method of laser measurement of the tread depth of a tyre as claimed in claim 8, wherein, before said step S2, the steps of:

SX1, any front wheel image or any rear wheel image with deviation from the optimal front wheel image or the optimal rear wheel image is obtained;

SX2, extremuing the moving distance of the vehicle between the two exposure times of the high-speed camera to enable the moving distance to be equal to the horizontal distance between the center of the tire and the incident light;

SX3, in the extremum state, the test error of the current device,

SX4, in the extremum state, the lowest frame rate f _min of the high-speed camera;

T_min＝s_min/v_max；

Δt_max＜T_min/(n_min-1)；

1/f_min＜Δt_max；

SX5, presetting a test error of the equipment, and calculating the lowest frame rate f _min of the high-speed camera;

SX6, setting the frame rate f of the high-speed camera, wherein f is more than or equal to f _min;

Wherein v is the speed of the tire during passing through the photographing sensor, and the default vehicle passes at a constant speed; s is the distance the vehicle moves during triggering of the photographing sensor; r _min is the minimum value of the tire radius; Δt _max is the maximum value of the adjacent two exposure time differences; h _min is the minimum height of the photographing sensor.

10. A method for measuring the tire tread depth by laser as in claim 9, wherein said step S4 comprises:

s41, the server marks the front wheel image and the rear wheel image;

s42, numbering a plurality of front-wheel images or a plurality of rear-wheel images in sequence, and marking n (n E [ 1, k ]);

s43, selecting an mth picture as the optimal front wheel image or the optimal rear wheel image;

m＝int(n/2)；

Wherein n is equal to the number of images acquired between the two exposure times of the high-speed camera, and n is not less than n _min.

11. A method of measuring the tread depth of a tyre by means of a laser according to any one of claims 7 to 10, wherein in step S51, identifying the corresponding tyre layout comprises:

Calling a system coordinate system;

screening the tire pattern images of the optimal front wheel image and the optimal rear wheel image;

The lowest point of the tire pattern image is connected, and then the upward translation distance s is formed into a reference line L1;

s＝S*h*C；

The ordinate of the point on the reference line L1 is marked as y1;

the y coordinates of all points on the surface of the tire are marked as y _surface;

calculating the difference values of y1-y _surface in sequence from left to right, recording as an intersection point when the difference value crosses 0 at any time, and recording the intersection point;

calculating the number of the intersection points, and judging the tire layout according to the number of the intersection points;

Wherein, C is the number of pixels in the corresponding y-axis direction represented by the actual tire tread per millimeter; s is the maximum tread depth; h is a proportionality coefficient, and h is more than or equal to 1.1 and less than or equal to 1.2.

12. A method of measuring the tread depth of a tyre by means of a laser according to any one of claims 7 to 10, wherein in step S51, identifying the corresponding tyre layout comprises:

Calling a system coordinate system;

screening the tire pattern images of the optimal front wheel image and the optimal rear wheel image;

smoothing the tire pattern images of the front wheel image and the rear wheel video;

the y coordinates of all points on the surface of the tire are marked as y _surface;

acquiring two point coordinates of the leftmost side and the rightmost side of the image, and respectively marking as P1 (x 1, y 1) and P2 (x 2, y 2);

Comparing the y coordinates of P1 and P2 to obtain a smaller value y _mix, and calculating y2;

y2＝y_mix*i(0.8≤i≤0.9)；

y3＝y2-y_surfaceAVG；

the image lowest point is connected, and then the image lowest point is translated upwards by a distance y3 to form a reference line L2, and at least 2 intersection points are necessarily formed between the reference line L2 and the tire pattern image;

Sequentially calculating values of y3-y _surface from left to right, recording as an intersection point when the difference value crosses 0 at any time, and recording the intersection point;

and calculating the number of the intersection points, and judging the tire layout according to the number of the intersection points.