CN115096203A - Method for measuring tire tread depth by laser - Google Patents
Method for measuring tire tread depth by laser Download PDFInfo
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- CN115096203A CN115096203A CN202210621297.8A CN202210621297A CN115096203A CN 115096203 A CN115096203 A CN 115096203A CN 202210621297 A CN202210621297 A CN 202210621297A CN 115096203 A CN115096203 A CN 115096203A
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- 230000001960 triggered effect Effects 0.000 claims description 25
- 238000005259 measurement Methods 0.000 claims description 9
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- 239000000203 mixture Substances 0.000 description 6
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- 238000007872 degassing Methods 0.000 description 2
- 239000000428 dust Substances 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/22—Measuring arrangements characterised by the use of optical techniques for measuring depth
Abstract
The invention relates to the technical field of tread depth detection, in particular to a method for measuring the tread depth of a tire by laser, which comprises S1, obtaining 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 obtains the optimal front wheel image and the optimal rear wheel image; and S5, analyzing the optimal front wheel image and the optimal rear wheel image, identifying the tire layout, and calculating the tread depth of the front wheel and the tread depth of the rear wheel. According to the invention, through the combination of an imaging mode and an image processing algorithm, testing errors caused by photographing of different tire sizes, different tire layouts and different positions are avoided, and the problem of low accuracy of the conventional laser tire grain detection device is solved.
Description
Technical Field
The invention relates to the technical field of tread depth detection, in particular to a method for measuring the tread depth of a tire by laser.
Background
A tire is a ground-rolling circular ring-shaped elastic rubber article mounted on various vehicles or machines. Generally mounted on a metal rim, and is capable of supporting a vehicle body, buffering external impact, achieving contact with a road surface and ensuring the driving performance of a vehicle. The tire pattern is also one of the important factors in determining the quality of a tire, since the main role of the tire pattern is to increase the friction between the tread and the road surface to prevent the wheel from slipping.
Two important factors of tire tread are tread pattern and tread depth. The larger the grounding elastic deformation of the pattern block is, the larger the rolling resistance formed by the elastic hysteresis loss of the tire is, the deeper the pattern is, the more the tire heat dissipation is not facilitated, the faster the tire temperature rise is, and the more the root of the pattern is torn and dropped due to severe stress. The too shallow pattern not only affects the water storage and drainage capability of the tyre, and is easy to generate harmful 'water slipping phenomenon', but also makes the defect that the tyre with smooth tread is easy to slip appear, thereby deteriorating the performance of the automobile mentioned above. Therefore, it is not good to have the pattern too deep or too shallow, and the objective rule is that the pattern becomes smaller in use.
At present stage auto repair in-process, go deep into through the instrument and detect and acquire the child line degree of depth usually, but this testing method's testing result easily receives the influence of detection instrument, detection angle or detection ring border, and then leads to the testing result inaccurate, is difficult to guarantee the safe use process of tire, and this detection efficiency is lower simultaneously, is difficult to satisfy the requirement of quick accurate detection.
Therefore, a method for measuring the tread depth of a tire by using laser has been developed.
Disclosure of Invention
The invention provides a method for measuring the tire tread depth by laser, which mainly solves the problems that the conventional method can only deeply detect and acquire 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 a tire is difficult to ensure, and meanwhile, the detection efficiency is low, and the requirement of quick 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 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 executing the next step, otherwise, 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 executing the next step, otherwise, continuously detecting;
s4, the server marks the front wheel image and the rear wheel image and respectively obtains an optimal front wheel image and an optimal rear wheel image;
and S5, analyzing the optimal front wheel image and the optimal rear wheel image, and calculating the tread depth of the front wheel and the tread depth of the rear wheel after identifying the tire layout.
Preferably, the step S2 includes,
s21, detecting whether the vehicle entering sensor is triggered for the first time, if so, starting the high-speed camera to enter a standby state, starting the 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 to be finished, 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;
said step S3 includes the steps of,
s31, detecting whether the vehicle entering sensor is triggered again, starting the 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 vehicle-out 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 the step S2, there is provided a step of:
SX1, acquiring any front wheel image or rear wheel image with deviation from the optimal front wheel image or the optimal rear wheel image;
the SX2 is used for extremizing the moving distance of the vehicle between two times of exposure time of the high-speed camera, so that the moving distance is equal to the horizontal distance between the center of the tire and the incident light;
SX3, the error in testing the current device,
SX4, frame rate f of high-speed camera in extremized state min ；
T min ＝s min /v max ；
Δt max ＜T min /(n min -1)；
1/f min ＜t max ；
SX5, presetting the test error of the equipment and calculating the lowest frame rate f of the high-speed camera min ；
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 the period of passing through the photographing sensor, and the default vehicle passes through at a constant speed; s is the distance the vehicle moved during the triggering of the photo sensor; r is min Is the minimum value of the radius of the tire; Δ t max The maximum value of the time difference between two adjacent exposures is obtained; h is a total of min Is the minimum height of the photo sensor.
Preferably, the step S4 includes:
s41, the server marks the front wheel image and the rear wheel image;
s42, numbering the front wheel images or the rear wheel images in sequence, and recording the images as n (n epsilon [1, k ]);
s43, selecting the 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 times of exposure of the high-speed camera, and n is more than or equal to n min 。
Preferably, the step S5 includes:
s51, identifying the corresponding tire layout according to the tire pattern image;
and S52, acquiring the front tyre thread depth in the optimal front wheel image and the rear tyre thread depth in the optimal rear wheel image.
Preferably, before the step S5, there is provided a step,
SX5, setting the position of the high speed camera, including the angle to the horizontal plane and the horizontal distance from the laser generator, so that all the fetal pattern is in the field of view of the high speed camera during photographing;
an SX6, which takes any vertex of the front wheel image or the rear wheel image as an origin, the axial direction of the tire is an x axis, and the depth direction of the groove is 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 the average value as y surfaceAVG ；
S523, acquiring position information of all grooves of the tire and corresponding y coordinates, and taking an average value of the y coordinates on each groove and recording the average value as y grooveAVGn (n∈【1，k’】)；
S524, calculating the number of pixel points corresponding to the depth of each groove;
ΔS pixeln ＝y grooveAVGn -y surfaceAVG ；
s525, calculating the pattern depth of each groove according to the geometric relation and the trigonometric function relation;
S＝[tan(α+β)-tana]*L；
β＝tan -1 ((S centre +S pixel )/f)-tan -1 (S centre /f)；
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 emitter, f is the image distance, b Pixel element Is the pixel size,. DELTA.S pixel Number of pixel points, S, projected on a high speed camera for the depth of the tread pixel For the length of the corresponding pixel point number, Δ S centre The number of pixel points S of the projection distance B resolution direction central point of the photosensitive chip on the high-speed camera on the surface of the tire centre Is the length of the corresponding pixel point number.
Preferably, before the step S5, there is provided a step,
SX5, setting the position of the high speed camera, including the angle to the horizontal plane and the horizontal distance from the laser generator, so that all the fetal pattern is in the field of view of the high speed camera during photographing;
an SX6, establishing a coordinate system, taking any vertex of the image as an origin, taking the B pixel direction as a Y axis, taking the A pixel direction as an X axis, and taking a single pixel point as a minimum unit;
an SX7, setting the incident ray of the high-speed camera to intersect with the measuring port at a point u, and imaging the point u 'at the central position u' of the B resolution direction of the camera photosensitive chip;
SX8, judging the size of the incident light and the measurement port in the u 'coordinate and the minimum unit coordinate n value in the Y axis direction, if the minimum unit coordinate n value is larger than u', executing the step SX9, otherwise executing the step SX 10;
SX9, calculating the length b of each pixel point on the Y axis corresponding to the real world j ，
b j ＝[tan(α+β 1 +γ 1 )-tan(α+β 1 )]*L；
β 1 ＝tan -1 (ΔS spixelβ1 *b Pixel element /f Image distance )；
γ 1 ＝tan -1 ((ΔS spixelβ1 +ΔS spixelγ1 )*b Pixel element /f Image distance )-β 1 ；
SX10, calculating the length b of each pixel point on the Y axis corresponding to the real world j ，
b j ＝[tan(α-β 2 )-tan(α-β 2 -γ 2 )]*L；
β 2 ＝tan -1 (ΔS spixelβ2 *b Pixel element /f Image distance )；
γ 2 ＝tan -1 ((ΔS spixelβ2 +ΔS spixelγ2 )*b Pixel element /f Image distance )-β 2 ；
SX11, establishing a pattern depth lookup table;
the step S52 includes the steps of,
s521, acquiring a test pattern image;
s522, acquiring the position information of all grooves of the tire and corresponding y coordinates, and taking the average value of the y coordinates on the corresponding grooves as y grooveAVGn (n∈【1，k’】)；
S523, acquiring y coordinates of all points on the surface of the tire, and taking an average value y surfaceAVG ；
S524, according to the pattern depth lookup table, looking up y in the table surfaceAVG To y grooveAVGn The real world distance represented by the length of all the pixel points is obtained to obtain data b j ；
S525, accumulating the data b j Obtaining the depth of the tyre;
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 emitter, f is the image distance,b is the resolution of the depth direction of the groove, B is the size of the resolution direction of the high-speed camera B, and Delta S pixel Number of pixel points projected on high speed camera for the depth of the pattern, S pixel For the length of the corresponding pixel point number, Δ S centre The number of pixel points of the projection distance B resolution direction central point of the photosensitive chip on the high-speed camera on the tire surface, S centre The length of the corresponding pixel point number; b is a mixture of j Is y surfaceAVG The real world distance corresponding to the coordinates, j is formed by the epsilon (1, k'); b j” Is Y grooveAVGn The coordinates correspond to real world distances, j ∈ [ j ', k' ], b j And the pixel point with the Y coordinate of j corresponds to the real world distance, and j belongs to [ j ', j' ].
Preferably, in the step S51, the step of 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;
connecting the lowest points of the tread pattern images and then translating the distance s upwards to form a reference line L1;
s＝S*h*C：
the vertical coordinate of the point position on the reference line L1 is marked as y 1;
the y-coordinate of all points of the tire surface is denoted as y surface ；
Calculating y1-y from left to right surface When the difference value crosses 0 at any time, recording the difference value as an intersection point, 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 y-axis direction corresponding to each millimeter of the real tread; s is the maximum profile 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, the step of 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;
performing smoothing processing on the front wheel image and the tire pattern image of the rear wheel video;
the y-coordinate of all points of the tire surface is denoted as y surface ；
Acquiring coordinates of two points on the leftmost side and the rightmost side of the image, and respectively recording the coordinates as P1(x1, y1) and P2(x2, y 2);
comparing the y coordinates of P1 and P2 yields a smaller value y mix And calculating to obtain y 2;
y2＝y mix *i(0.8≤i≤0.9)；
y3＝y2-y surfaceAVG ；
the upward translation distance y3 after connecting the lowest points of the images forms a reference line L2, and the reference line L2 necessarily has at least 2 intersections with the pattern images;
from left to right, calculating y3-y in turn surface When the difference value crosses 0 at any time, recording as an intersection point, 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 following beneficial effects can be obtained by applying the technical scheme provided by the invention:
firstly, the technical scheme provided by the invention can reduce external interference by the definite geometric relationship between the depth of the pattern and the projection of the pattern on a pixel, thereby ensuring the accuracy of a detection result;
secondly, according to the technical scheme provided by the invention, the pattern depth lookup table is input in advance, so that external interference can be avoided, the calculation amount of the server in the actual test process is reduced, the hardware pressure of equipment is reduced, the calculation speed can be increased, and the detection efficiency is improved;
thirdly, the tire layout can be effectively identified in the technical scheme provided by the invention, so that the detection result can be ensured to completely cover all tires, and the applicability of the scheme to various different vehicle types is also ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a scanner according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a scanning device for capturing images of front wheels according to embodiments 1 to 5 of the present invention;
fig. 3 is a schematic view of a front wheel image/rear wheel picture taken while the front wheel/rear wheel is moving in embodiments 1 to 5 of the present invention;
fig. 4 is a schematic diagram illustrating the calculation of frame rate selection of a high-speed camera in embodiments 2 to 5 of the present invention;
fig. 5 is a schematic diagram illustrating calculation of frame rate selection of a high-speed camera in embodiments 2 to 5 of the present invention;
fig. 6 is a schematic structural diagram of the tread depth detection in embodiments 2 and 3 of the present invention;
FIG. 7 is a schematic structural diagram of a first case in the tread depth detection in embodiments 4 and 5 of the present invention;
FIG. 8 is a schematic structural diagram of a second case in the tread depth detection in embodiments 4 and 5 of the present invention;
FIG. 9 shows a hypothetical imaging method for detecting the tread depth in examples 4 and 5 of the present invention;
FIG. 10 is a schematic diagram of an algorithm for identifying tire layout in examples 2 and 4 of the present invention;
FIG. 11 is a schematic view of an algorithm for identifying tire layout in examples 3 and 5 of the present invention;
fig. 12 is a schematic view of tire layouts identified in examples 2 to 5 of the present invention.
Detailed Description
The technical solution 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 is to be understood that the embodiments described are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
The existing method can only deeply detect and acquire the tire pattern depth through an instrument generally, 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 guarantee, and meanwhile, the detection efficiency is low, and the requirement of quick and accurate detection is difficult to meet.
Example 1
In order to solve the above problems, the present embodiment proposes a device for laser measuring the tire tread depth, which comprises a measuring support device 10, an entering sensor 21, an exiting sensor 22, a high-speed camera 30, a laser emitter 40 and a photographing sensor 50.
The measuring and supporting device 10 is in a trapezoidal structure, and the vehicle entering sensor 21 and the vehicle exiting sensor 22 are arranged on the upper top surface of the measuring and supporting device 10 in parallel; the high-speed camera 30 and the laser emitter 40 are arranged in the measurement supporting device 10; the laser emitter 40 and the photographing sensor 50 are located on the same straight line, and the straight line where the laser emitter 40 and the photographing sensor 50 are located is located at the center of the vehicle entering sensor 21 and the vehicle exiting sensor 22.
Preferably, but not limited to, the entering sensor 21 and the exiting sensor 22 are symmetrically distributed along a vertical line of the laser emitter 40 in the embodiment.
Preferably, but not limited to, the upper top surface of the measurement support device 10 in this embodiment is a tempered glass structure, which can effectively transmit light and prevent dust from entering the inside of the measurement support device 10. Preferably, the glass surface is also provided with a degassing device, so that dust on the glass surface can be effectively removed.
Preferably, but not limitatively, the entry sensor 21 and the exit sensor 22 are both laser-coupled sensors.
In this embodiment, the vehicle enters the measurement window corresponding to the laser emitter 40 after passing through the vehicle entering sensor 21, 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 a vehicle enters a detection preparation mode (for example, license plate information is acquired), the degassing device may be turned on to keep the glass transparent, and detect whether the vehicle entering sensor 21 is activated, if so, the high-speed camera may be turned on to enter a standby state, the laser generator may be turned on, and further, when it is detected that the photographing sensor 50 is triggered for the first time, the front wheel image may be continuously photographed through laser imaging, until the photographing sensor is triggered to end and when the vehicle exiting sensor 22 is activated, the laser may be turned off, and when the vehicle entering sensor 21 and the photographing sensor 50 are activated again, the laser may be turned on again and the rear wheel image may be continuously photographed, until the photographing sensor 50 is triggered to end and when the vehicle exiting sensor 22 is activated again, the laser may be 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 a tire tread depth by using a laser, the method including a first depth detection method and a first tire layout determination 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, and executing the next step, otherwise, 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 executing the next step, otherwise, continuously detecting;
s4, the server marks the front wheel image and the rear wheel image and respectively obtains the optimal front wheel image and the optimal rear wheel image;
and S5, analyzing the optimal front wheel image and the optimal rear wheel image, and calculating the tread depth of the front wheel and the tread depth of the rear wheel after identifying the tire layout.
Preferably, in step S1, the vehicle information may be acquired by automatically recognizing the license plate information to enter the license plate information, or by manually inputting the license plate information.
More specifically, step S2 includes,
s21, detecting whether the vehicle entering sensor is triggered for the first time, if so, starting the high-speed camera to enter a standby state, starting the 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 triggering of the photographing sensor is finished, and executing the next step, otherwise, continuously detecting;
s23, detecting whether the vehicle sensor is triggered for the first time, if so, closing the laser, executing the next step, and if not, continuously detecting;
s24, uploading the acquired front wheel images to a server;
the step S3 includes the steps of,
s31, detecting whether the vehicle entering sensor is triggered again, starting the 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 triggering of the photographing sensor is finished, and executing the next step, otherwise, 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 multiple rear wheel images to a server.
In this embodiment, the setting direction of the vehicle entering sensor and the vehicle exiting sensor should be consistent with the moving direction of the vehicle, so as to ensure that the front wheels are sequentially activated to the vehicle entering sensor and the vehicle exiting sensor, and meanwhile, the photographing sensor is arranged between the vehicle entering sensor and the vehicle exiting sensor, so that the photographing can be respectively started to photograph images of the front wheels and closed to photograph, and the rear wheels are sequentially activated to the vehicle entering sensor and the vehicle exiting sensor, so that the photographing can be respectively started to photograph images of the rear wheels and closed to photograph.
More specifically, before step S2, there is provided the step of:
SX1, acquiring any front wheel image or rear wheel image having a deviation from the optimal front wheel image or optimal rear wheel image;
SX2, limiting the moving distance of the vehicle between two times of exposure of the high-speed camera to be equal to the horizontal distance between the center of the tire circle and the incident light;
SX3, the error in testing the current device,
SX4, frame rate f of high-speed camera in extremized state min ；
T min ＝s min /v max ；
Δt max ＜T min /(n min -1)；
1/f min ＜t max ；
SX5, presetting the test error of the equipment and calculating the lowest frame rate f of the high-speed camera min ；
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 the period of passing through the photographing sensor, and the default vehicle passes through at a constant speed; s is the distance moved by the vehicle during the triggering of the photographing sensor; r is min Is the minimum value of the radius of the tire; Δ t max The maximum value of the time difference between two adjacent exposures is obtained; h is min The minimum height of the photo sensor.
As shown in fig. 4 and 5, the position right above the measurement opening is selected as the analysis object, the horizontal distance from the center of the tire to the incident light at this time is defined as the distance of the vehicle moving between two adjacent exposure times, and in the actual process, the distance of the vehicle moving between two adjacent exposure times is necessarily smaller than the distance of the vehicle moving between two adjacent exposure times.
Assuming that the incident laser crosses the tire surface E, the incident laser crosses the bottom of the groove at H, a straight line passing through I is perpendicular to EH and extends to intersect at J point, the center position I and the point H of the tire are connected, and the straight line extends to the tire surface at K point, the uniform vehicle speed is defined as v, the tire radius is defined as r, the time between two adjacent exposures is defined as delta t, and the frame rate of the camera is defined as f;
△t＝1/f；
the test error is (HE/HK-1)% (1/cos (& lt KHE) -1)%;
since the tread depth is small relative to the tire radius, we approximate KI ═ HI ═ r, so sin (· JHI) ═ JI/IH ═ JI/IK ═ JI (vehicle speed · time between two adjacent exposures)/tire radius ═ v · Δ t/r; since < KHE is very small, Δ HKE is considered as a right triangle by approximation, and therefore
cos(∠KHE)＝KH/HE；
And < KHE ═ JHI;
by the formula of trigonometric function:
cos(∠KHE) 2 +sin(∠KHE) 2 ＝1；
so that 1/cos (. sub. KHE). sub.1/[ 1-sin (. sub. KHE) 2 ] 1/2 ；
That is, the test error is [ (1-sin (shun KHE) 2 ) -1/2 -1]Percent, substituting into the formula can prove that:
generally, the diameter of an automobile tire is 600mm to 800mm, and a small-size tire is selected and analyzed by an extreme value method, mainly because the smaller the tire is, the shorter the time for passing through a photographing sensor is, the faster the speed is, and the shorter the time for passing through the photographing sensor is, so that assuming that the tire radius r is 300mm, the maximum passing speed v within an allowable range is 10km/h, the acceptance error is 0.1%, at this time, Δ t is calculated to be 5ms, and the camera frame rate corresponds to 200 fps.
And then assuming that the height of the photographing sensor is h, the radius of the tire is r, and the vehicle speed is v, then the test interval is as follows:
distance of vehicle movement 2 r 2 -(r-h) 2 ] 1/2 ；
The total photographing time T is equal to the moving distance/v of the vehicle;
t < T/(n-1),
in this embodiment, the photographing interval is short enough, the more the number of the acquired photos is, the closer the mth photo is to the position right above the mth photo, but the shorter the photographing interval is, the higher the requirement on the camera is, that is, the higher the frame rate is, the more expensive the camera is, and the more data the server processes is, so that an appropriate photographing interval, that is, an appropriate frame rate needs to be selected, and the appropriate photographing interval can be specifically calculated by an error.
In general, the height of the photographing sensor is a fixed value of the equipment, and ranges from 10mm to 20mm, and assuming that h is 10mm and the radius r of the tire is 300 mm; deducing by the formula, if the equipment error is less than 0.1%, the frame rate of the camera is 200 fps; substituting verifiable vehicle moving distance of 0.33m, here obtaining n min Has a value of 12.
Therefore, in SX2 of the present embodiment, the device can capture a minimum of 12 tire tread images, i.e., n ≧ 12, by providing a high-speed camera during the photo sensor trigger.
More specifically, the step S4 includes:
s41, the server marks the front wheel image and the rear wheel image;
s42, numbering the front wheel images or the rear wheel images in sequence, and recording the images as n (n epsilon [1, k ]);
s43, selecting the 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 times of exposure of the high-speed camera, and n is more than or equal to n min 。
In this embodiment, the marking of the front wheel image and the rear wheel image in step S41 may be performed by labeling in the order in which the images are received by the server, for example, in the traveling direction of the vehicle, the image acquired preferentially is the front wheel image, and may be marked as the left front wheel image or the right front wheel image.
Preferably, in the present embodiment, when the vehicle passes above the apparatus, the uniform movement is adopted, so that the picture taken by the m-th image is considered to be the image when the tire is located right above the measuring port, i.e. case 2 in fig. 3.
More specifically, step S5 includes:
s51, identifying the corresponding tire layout according to the tire pattern image;
s52, the front tread depth in the best front wheel image and the rear tread depth in the best rear wheel image are obtained.
More specifically, before step S5, there is provided a step,
SX5, setting the position of the high speed camera, including the angle to the horizontal plane and the horizontal distance from the laser generator, so that all the fetal pattern is in the field of view of the high speed camera during photographing;
an SX6 coordinate system is established by taking any vertex of the front wheel image or the rear wheel image as an origin, taking the axial direction of the tire as an x axis and taking the depth direction of the groove as a y axis;
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 the average value as y surfaceAVG ；
S523, acquiring position information of all grooves of the tire and corresponding y coordinates, and taking an average value of the y coordinates on each groove and recording the average value as y grooveAVGn (n∈【1，k’】)；
S524, calculating the number of pixel points corresponding to the depth of each groove;
ΔS pixeln ＝y grooveAVGn -y surfaceAVG ；
s525, calculating the pattern depth of each groove according to the geometric relation and the trigonometric function relation;
S＝[tan(α+β)-tana]*L；
β＝tan -1 ((S centre +S pixel )/f)-tan -1 (S centre /f)；
S pixel ＝b pixel element *ΔS pixel ；
S centre ＝b Pixel element *ΔS centre ；
Wherein alpha is an included angle between the high-speed camera and a horizontal plane, L is a horizontal distance from the high-speed camera to the laser emitter, f is an image distance, b Pixel element Is the pixel size,. DELTA.S pixel Number of pixel points projected on high speed camera for the depth of the pattern, S pixel For the length of the corresponding pixel point number, Δ S centre The number of pixel points S of the projection distance B resolution direction central point of the photosensitive chip on the high-speed camera on the surface of the tire centre Is the length of the corresponding pixel point number.
As shown in fig. 2, an incident laser is defined to cross the tire surface at a point P, a reflected light passes through a lens center point O of a high-speed camera, and is imaged at a point P 'on a photosensitive chip, similarly, the incident light crosses the bottom of the tire groove at a point R, the reflected light passes through the lens center point O of the high-speed camera, and is imaged at a point R' on the photosensitive chip, the center position of the photosensitive chip is O ', an included angle between the camera and a horizontal plane is α, the horizontal distance from the high-speed camera to the incident light, that is, the length of OQ is L, the image distance OO' of the camera is f, and the pixel length of O 'P' is S base P 'R' pixel length of S pixel The length of the tread depth RP (S) is determined.
In < delta > O ' R ' O, < O ' OR ═ tan -1 ((S centre +S pixel )/f)；
In Δ O ' P ' O, angle O ' OP ═ tan -1 (S centre /f)；
So that the angle P 'OR ═ O' OP ═ tan -1 ((S centre +S pixel )/f)-tan -1 (S centre /f)；
Because ═ β ═ P 'OR';
so < beta > tan -1 ((S centre +S pixel )/f)-tan -1 (S centre /f)；
In Δ ORQ, RQ ═ L × tan (α + β);
in Δ OPQ, PQ ═ L × tan α;
therefore, the tread depth S ═ PR ═ RQ ═ PQ ═ L ═ tan (α + β) -tan α.
Preferably, but not limited to, in this 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 depth of the pattern detected by the method is mainly a definite geometric relationship with the depth of the pattern in the pixel projection, that is, the result can be calculated by the proportional relationship in the technical scheme of the present application, and the method 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 lowest point of the connected sipe image (curve 1 in FIG. 5) is translated upward by a distance s to form a reference line L1 (curve 2 in FIG. 5);
s＝S*h*C；
the vertical coordinate of the point position on the reference line L1 is marked as y 1;
the y-coordinate of all points on the surface of the tyre, denoted as y surface ；
Calculating y1-y from left to right surface When the difference value crosses 0 at any time, recording the difference value as an intersection point, 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 y-axis direction corresponding to each millimeter of the real tread, and if C is 25, the tread depth per millimeter 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 10 mm; h is a proportionality coefficient, and h is more than or equal to 1.1 and less than or equal to 1.2.
In this embodiment, when the number of the intersection points is 2, it is determined that the layout of the tires is a car tire layout, that is, 2 tires in front and rear; when the number of intersections is 4, the two-tire layout is determined.
Example 3
As shown in fig. 4 to 6, in order to solve the foregoing problems, the present embodiment proposes a method for measuring a tire tread depth by using a laser, the method including a first depth detection method and a second tire layout determination algorithm, specifically including the remaining steps except for step S51 in embodiment 2, except that in step S51, the step of identifying a 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;
performing smoothing treatment on the tire pattern images of the front wheel image and the rear wheel video; preferably, the smoothing process may adopt a mode of performing an opening operation on the image and then performing polynomial fitting;
the y-coordinate of all points on the surface of the tyre, denoted as y surface ；
Acquiring coordinates of two points on the leftmost side and the rightmost side of the image, and respectively recording the coordinates as P1(x1, y1) and P2(x2, y 2);
comparing the y coordinates of P1 and P2 yields a smaller value y mix And calculating to obtain y 2;
y2＝y mix *i(0.8≤i≤0.9)；
y3＝y2-y surfaceAVG ；
the upward translation by a distance y2 after connecting the image nadirs (curve 3 in fig. 6) forms a reference line L2 (curve 4 in fig. 6), and there must be at least 2 intersections of the reference line L2 with the pattern image;
from left to right, calculating y3-y in turn surface When the difference value crosses 0 at any time, recording the difference value as an intersection point, and recording the intersection point;
the number of the intersection points is calculated, and the tire layout is judged 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 layout of the tires is a car tire layout, that is, 2 tires in front and at the back; 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 laser measuring the tread depth of a tire, which includes a second depth detection method and a first tire layout determination algorithm, and specifically includes the remaining steps of embodiment 2 except for steps SX5 to SX8 and step S52, except that:
before step S5, there is provided a step,
SX5, setting the position of the high speed camera, including the angle to the horizontal plane and the horizontal distance from the laser generator, so that all the fetal pattern is in the field of view of the high speed camera during photographing;
an SX6, establishing a coordinate system, taking any vertex of the image as an origin, taking the B pixel direction as a Y axis, taking the A pixel direction as an X axis, and taking a single pixel point as a minimum unit;
an SX7, setting the incident ray of the high-speed camera to intersect with the measuring port at a point u, and imaging the point u 'at the central position u' of the B resolution direction of the camera photosensitive chip;
SX8, judging the size of the incident light and the measurement port in the u 'coordinate and the minimum unit coordinate n value in the Y axis direction, if the minimum unit coordinate n value is larger than u', executing the step SX9, otherwise executing the step SX 10;
SX9, calculating the length b of each pixel point on the Y axis corresponding to the real world j ，
b j ＝[tan(α+β 1 +γ 1 )-tan(α+β 1 )]*L；
β 1 ＝tan -1 (ΔS spixelβ1 *b Pixel element /f Image distance )；
γ 1 ＝tan -1 ((ΔS spixelβ1 +ΔS spixelγ1 )*b Pixel element /f Image distance )-β 1 ；
SX10 calculating each image on the Y-axisElement point corresponding to real world length b j ，
b j ＝[tan(α-β 2 )-tan(α-β 2 -γ 2 )]*L；
β 2 ＝tan -1 (ΔS spixelβ2 *b Pixel element /f Image distance )；
γ 2 ＝tan -1 ((ΔS spixelβ2 +ΔS spixelγ2 )*b Pixel element /f Image distance )-β 2 ；
SX11, establishing a pattern depth lookup table;
the step S52 includes the steps of,
s521, acquiring a test pattern image;
s522, obtaining the position information of all grooves of the tire and the corresponding y coordinates, and taking the average value of the y coordinates on the corresponding grooves and recording the average value as y grooveAVGn (n∈【1，k’】)；
S523, acquiring y coordinates of all points on the surface of the tire, and taking an average value y surfaceAVG ；
S524, according to the tire pattern depth lookup table, y in the lookup table surfaceAVG To y grooveAVGn The real world distance represented by the length of all the pixel points is obtained to obtain data b j ；
S525, accumulating the data b j Obtaining the depth of the tyre;
wherein alpha is an included angle between incident light of the high-speed camera and a horizontal plane, L is a horizontal distance from the high-speed camera to the laser emitter, f is an image distance, B is a resolution ratio in a depth direction of the groove, B is a size in a resolution ratio direction of the high-speed camera, and Delta S pixel Number of pixel points, S, projected on a high speed camera for the depth of the tread pixel For the length of the corresponding pixel point number, Δ S centre The number of pixel points S of the projection distance B resolution direction central point of the photosensitive chip on the high-speed camera on the surface of the tire centre The length of the corresponding pixel point number; b j， Is y surfaceAVG The real world distance corresponding to the coordinates, j is formed by the epsilon (1, k'); b j” Is Y grooveAVGn The coordinates correspond to the real world distance, j ∈ [ j ', k'). b j And the pixel point with the Y coordinate of j corresponds to the real world distance, and j belongs to [ j ', j' ].
In this embodiment, each pixel point corresponds to each distance on the incident ray in the high speed camera field of view.
Taking a specific case as an example, assuming that the pixel size in the B direction is 1024, the image distance is 6mm, and the pixel size is 4.8 μm, wherein the included angle α between the high-speed camera and the horizontal plane is 40 °, the horizontal distance L to the incident light is 320mm, and three pixel points (B in the figure) of 1, 512, and 1024 are respectively calculated for the Y coordinate (in the figure, B is a point) spixel1 、b spixel512 、b spixel1024 ) The true distance length represented, i.e. b 1 ，b 512 ，b 1024 The value of (c). For the pixel points with the coordinate more than or equal to 512, the following values are substituted into a formula to obtain the coordinate value, wherein delta S pixelβ And Δ S pixelγ Respectively representing the pixel points corresponding to beta and gamma;
pixel with y coordinate 512:
△ Spixelβ ＝0
△ Spixelγ ＝1
b 512 ＝0.438mm
pixel point with y coordinate of 1024:
△ Spixelβ ＝511
△ Spixelγ ＝1
b 1024 ＝1.005mm
for the pixel points with the coordinate positions between 0 and 512, the calculation formula needs to be adjusted:
pixel point with y coordinate of 1
△ Spixelβ ＝511
△ Spixelγ ＝1
b 0 ＝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 length of the pixel point corresponding to the corresponding coordinate value.
| Y coordinate | b n |
| 1 | b 1 |
| 2 | b 2 |
| ...... | ...... |
| 1024 | B 1024 |
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, so as to create a "tread depth lookup table", where the table includes two types of data, i.e. the y-axis coordinate and the real world length represented by the pixel size in the pixel B resolution direction of the corresponding position, for example, data (512, 0.438) indicates that the real world length tread depth represented by the pixel size in the B resolution direction is 0.438mm when the y-axis coordinate of the pixel at the bottom of the groove is 512. During testing, the image is processed to obtain the pixel y-axis coordinate information of the bottom of the groove of the pattern image, such as y620, the pixel y-axis coordinate information of the tire surface of the pattern image, such as y515, all values between b515 and b620 are searched according to yn information comparison 'pattern depth lookup table', and calculation is carried out
The depth of the tyre tread is
Example 5
As shown in fig. 2 to 5, in order to solve the foregoing problems, the present embodiment proposes a method for laser measuring the tread depth of a tire, which includes a second depth detection method and a second tire layout determination algorithm, and specifically includes the remaining steps of embodiment 2 except for steps SX5 to SX8 and step S52, except that:
before step S5, there is provided a step,
SX5, setting the position of the high speed camera, including the angle to the horizontal plane and the horizontal distance from the laser generator, so that all the fetal pattern is in the field of view of the high speed camera during photographing;
an SX6, establishing a coordinate system, taking any vertex of the image as an origin, taking the direction of a pixel B as an axis Y, taking the direction of a pixel A as an axis X, and taking a single pixel point as a minimum unit;
an SX7, setting the incident ray of the high-speed camera to intersect with the measuring port at a point u, and imaging the point u 'at the central position u' of the B resolution direction of the camera photosensitive chip;
SX8, judging the size of the incident light and the measurement port in the u 'coordinate and the minimum unit coordinate n value in the Y axis direction, if the minimum unit coordinate n value is larger than u', executing the step SX9, otherwise executing the step SX 10;
SX9, calculating the length b of each pixel point on the Y axis corresponding to the real world j ，
b j ＝[tan(α+β 1 +γ 1 )-tan(α+β 1 )]*L；
β 1 ＝tan -1 (ΔS spixelβ1 *b Pixel element /f Image distance )；
γ 1 ＝tan -1 ((ΔS spixelβ1 +ΔS spixelγ1 )*b Pixel element /f Image distance )-β 1 ；
SX10, calculating the length b of each pixel point on the Y axis corresponding to the real world j ，
b j ＝[tan(α-β 2 )-tan(α-β 2 -γ 2 )]*L；
β 2 ＝tan -1 (ΔS spixelβ2 *b Pixel element /f Image distance )；
γ 2 ＝tan -1 ((ΔS spixelβ2 +ΔS spixelγ2 )*b Pixel element /f Image distance )-β 2 ；
SX11, establishing a pattern depth lookup table;
the step S52 includes the steps of,
s521, acquiring a test pattern image;
s522, acquiring the position information of all grooves of the tire and corresponding y coordinates, and taking the average value of the y coordinates on the corresponding grooves as y grooveAVGn (n∈【1，k’】)；
S523, acquiring y coordinates of all points on the surface of the tire, and taking an average value y surfaceAVG ；
S524, according to the tire pattern depth lookup table, y in the lookup table surfaceAVG To y grooveAVGn The real world distance represented by the length of all the pixel points is obtained to obtain data b j ；
S525, accumulating the data b j Obtaining the depth of the tyre;
wherein, alpha is an included angle between incident light of the high-speed camera and a horizontal plane, L is a horizontal distance from the high-speed camera to the laser emitter, f is an image distance, B is a resolution ratio in a depth direction of the groove, B is a size in a resolution ratio direction of the high-speed camera, and Delta S pixel Number of pixel points, S, projected on a high speed camera for the depth of the tread pixel For the length of the corresponding pixel point number, Δ S centre The number of pixel points S of the projection distance B resolution direction central point of the photosensitive chip on the high-speed camera on the surface of the tire centre The length of the corresponding pixel point number; b j Is y surfaceAVG The real world distance corresponding to the coordinates, j is formed by the epsilon (1, k'); b is a mixture of j” Is Y grooveAVGn The coordinates correspond to the real world distance, j ∈ [ j', k ]. b j And (4) corresponding the real world distance to the pixel point with the Y coordinate of j, wherein j belongs to [ j ', j').
In this embodiment, each pixel point corresponds to each distance on the incident ray in the field of view of the high speed camera.
Taking a specific case as an example, assuming that the pixel size in the B direction is 1024, the image distance is 6mm, and the pixel size is 4.8 μm, wherein the included angle α between the high-speed camera and the horizontal plane is 40 °, the horizontal distance L to the incident light is 320mm, and three pixel points (B in the figure) of 1, 512, and 1024 are respectively calculated for the Y coordinate (in the figure, B is a point) spixel1 、b spixel512 、b spixel1024 ) The true distance length represented, i.e. b 1 ，b 512 ，b 1024 The value of (c). For the pixel points with the coordinate more than or equal to 512, the following values are substituted into a formula to obtain the coordinate value, wherein delta S pixelβ And Δ S pixelγ Respectively representing the pixel points corresponding to beta and gamma;
pixel with y coordinate 512:
△ Spixelβ ＝0
△ Spixelγ ＝1
b 512 ＝0.438mm
pixel with y coordinate of 1024:
△ Spixelβ ＝511
△ Spixelγ ＝1
b 1024 ＝1.005mm
for pixel points with coordinate positions between 0 and 512, the calculation formula needs to be adjusted:
pixel point with y coordinate of 1
△ Spixelβ ＝511
△ Spixelγ ＝1
b 0 ＝0.2401mm
Preferably, the fetal pattern depth lookup table established in step SX10 is as follows, the abscissa of the lookup table is the Y coordinate value of the pixel, and the ordinate is the distance of the real world represented by the length of the pixel point 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, so as to establish a "tread depth lookup table", where the table includes two types of data, i.e. the y-axis coordinate and the real-world length represented by the pixel size in the resolution direction of the pixel B at the corresponding position, for example, the data (512, 0.438) indicates that the real-world length tread depth represented by the pixel size in the resolution direction of the pixel B is 0.438mm when the y-axis coordinate of the pixel at the bottom of the groove is 500. During testing, the image is processed to acquire the coordinate information of the pixel y axis at the bottom of the groove of the pattern image, such as y 620 Image of the tyre surface in terms of pixel y-coordinate information, e.g. y 515 According to y n The information comparison 'tyre pattern depth lookup table' looks up all values between b515 and b620, and calculates
The depth of the tyre thread is
It should be emphasized that SX9 and SX10 for the depth detection algorithms in examples 4 and 5 represent u' imaged above and below the u point, respectively, in the case of a sipe, the fact that a sipe may only exist above the u point. Therefore, the depth detection algorithms in embodiments 4 and 5 can be widely applied, and are not limited to the detection of the depth of the tire tread.
Further, in example 2 to example 5, after step S5, it is also possible to select an appropriate user interface on the basis of the identified tire layout, and display the tread depth information and the corresponding tire position information on the corresponding user interface.
In summary, in the device and the method for testing the tire tread depth by using laser provided in embodiments 1 to 5, the tire tread image is obtained by using a laser vertical incidence mode, the detection accuracy is improved by selecting a proper photographing time interval and an image selection method, and meanwhile, an AI algorithm capable of recognizing the tire layout is further provided, so that the tire layout is automatically recognized.
The above-described embodiments do not limit the scope of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the above-described embodiments should be included in the protection scope of the technical solution.
Claims (9)
1. A method for measuring the tread depth of a tire by laser is characterized by comprising 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, and executing the next step, otherwise, 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 executing the next step, otherwise, continuously detecting;
s4, the server marks the front wheel image and the rear wheel image and respectively obtains an optimal front wheel image and an optimal rear wheel image;
and S5, analyzing the optimal front wheel image and the optimal rear wheel image, and calculating the tread depth of the front wheel and the tread depth of the rear wheel after identifying the tire layout.
2. The method of claim 1, wherein the laser measuring the depth of the tread of the tire comprises:
the step S2 includes the steps of,
s21, detecting whether the vehicle entering sensor is triggered for the first time, if so, starting the high-speed camera to enter a standby state, starting the 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 to end, executing the next step, and if not, continuously detecting;
s23, detecting whether the vehicle sensor is triggered for the first time, if so, closing the laser, executing the next step, and if not, continuously detecting;
s24, uploading the acquired front wheel images to a server;
the step S3 includes the steps of,
s31, detecting whether the vehicle entering sensor is triggered again, starting the 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 vehicle-out 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. The method for laser measuring the tread depth of a tire as claimed in claim 2, wherein before the step S2, there is provided the steps of:
SX1, acquiring any front wheel image or rear wheel image with deviation from the optimal front wheel image or the optimal rear wheel image;
the SX2 is used for extremizing the moving distance of the vehicle between two times of exposure time of the high-speed camera, so that the moving distance is equal to the horizontal distance between the center of the tire and the incident light;
SX3, the error in testing the current device,
SX4, frame rate f of high-speed camera in extremized state min ；
T min ＝s min /v max ；
Δt max ＜T min /(n min -1)；
1/f min ＜t max ；
SX5, presetting the test error of the equipment and calculating the lowest frame rate f of the high-speed camera min ；
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 the period of passing through the photographing sensor, and the default vehicle passes through at a constant speed; s is the distance the vehicle moved during the triggering of the photo sensor; r is min Is the minimum value of the radius of the tire; Δ t max The maximum value of the time difference between two adjacent exposures is obtained; h is min The minimum height of the photo sensor.
4. The method for laser measuring the tread depth of a tire as claimed in claim 3, wherein said step S4 comprises:
s41, the server marks the front wheel image and the rear wheel image;
s42, numbering the front wheel images or the rear wheel images in sequence, and recording the images as n (n epsilon [1, k ]);
s43, selecting the mth picture as the optimal front wheel image or the optimal rear wheel image;
m＝int(n/2)；
wherein n is equal to an image acquired between two exposure times of the high speed cameraNumber, and n is not less than n min 。
5. The method for laser measuring the tread depth of a tire as claimed in claim 4, wherein said step S5 comprises:
s51, identifying the corresponding tire layout according to the tire pattern image;
and S52, acquiring the front tyre thread depth in the optimal front wheel image and the rear tyre thread depth in the optimal rear wheel image.
6. The method of claim 5, wherein the laser measuring the depth of the tread of the tire comprises:
before the step S5, there is provided a step,
SX5, setting the position of the high speed camera, including the angle to the horizontal plane and the horizontal distance from the laser generator, so that all the fetal pattern is in the field of view of the high speed camera during photographing;
an SX6, which takes any vertex of the front wheel image or the rear wheel image as an origin, the axial direction of the tire is an x axis, and the depth direction of the groove is 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 the average value as y surfaceAVG ；
S523, obtaining the position information of all grooves of the tire and the corresponding y coordinates, and taking the average value of the y coordinates on each groove and recording the average value as y grooveAVGn (n∈【1，k’】)；
S524, calculating the number of pixel points corresponding to the depth of each groove;
ΔS pixeln ＝y grooveAVGn -y surfaceAVG ；
s525, calculating the tread depth of each groove according to the geometric relation and the trigonometric function relation;
S＝[tan(α+β)-tanα]*L；
β＝tan -1 ((S centre +S pixel )/f)-tan -1 (S centre /f)；
S pixel ＝b pixel element *ΔS pixel ；
S centre ＝b Pixel element *ΔS centre ；
Wherein alpha is an included angle between the high-speed camera and a horizontal plane, L is a horizontal distance from the high-speed camera to the laser emitter, f is an image distance, b Pixel element Is the pixel size, Δ S pixel Number of pixel points projected on high speed camera for the depth of the pattern, S pixel For the length of the corresponding pixel point number, Δ S centre The number of pixel points S of the projection distance B resolution direction central point of the photosensitive chip on the high-speed camera on the surface of the tire centre Is the length of the corresponding pixel point number.
7. The method of claim 5, wherein the laser measuring the depth of the tread of the tire comprises:
before the step S5, there is provided a step,
SX5, setting the position of the high speed camera, including the angle to the horizontal plane and the horizontal distance from the laser generator, so that all the fetal pattern is in the field of view of the high speed camera during photographing;
an SX6, establishing a coordinate system, taking any vertex of the image as an origin, taking the direction of a pixel B as an axis Y, taking the direction of a pixel A as an axis X, and taking a single pixel point as a minimum unit;
an SX7, setting the incident ray of the high-speed camera to intersect with the measuring port at a point u, and imaging the point u 'at the universal central position u' of the B resolution of the camera photosensitive chip;
SX8, judging the size of the incident light and the measurement port in the u 'coordinate and the minimum unit coordinate n value in the Y axis direction, if the minimum unit coordinate n value is larger than u', executing the step SX9, otherwise executing the step SX 10;
SX9, calculating the length b of each pixel point on the Y axis corresponding to the real world j ，
b j ＝[tan(α+β 1 +γ 1 )-tan(α+β 1 )]*L；
β 1 ＝tan -1 (ΔS spixelβ1 *b Pixel element /f Image distance )；
γ 1 ＝tan -1 ((ΔS spixelβ1 +ΔS spixelγ1 )*b Pixel element /f Image distance )-β 1 ；
SX10, calculating the length b of each pixel point on the Y axis corresponding to the real world j ，
b j ＝[tan(α-β 2 )-tan(α-β 2 -γ 2 )]*L；
β 2 ＝tan -1 (ΔS spixelβ2 *b Pixel element /f Image distance )；
γ 2 ＝tan -1 ((ΔS spixelβ2 +ΔS spixelγ2 )*b Pixel element /f Image distance )-β 2 ；
SX11, establishing a pattern depth lookup table;
the step S52 includes the steps of,
s521, acquiring a test pattern image;
s522, obtaining the position information of all grooves of the tire and the corresponding y coordinates, and taking the average value of the y coordinates on the corresponding grooves and recording the average value as y grooveAVGn (n∈【1，k’】)；
S523, acquiring y coordinates of all points on the surface of the tire, and taking an average value y surfaceAVG ；
S524, according to the pattern depth lookup table, y in the lookup table surfaceAVG To y grooveAVGn The real world distance represented by the length of all the pixel points is obtained to obtain data b j ；
S525, accumulating the data b j Obtaining the depth of the tyre;
wherein alpha is an included angle between incident light of the high-speed camera and a horizontal plane, L is a horizontal distance from the high-speed camera to the laser emitter, f is an image distance,b is the resolution of the depth direction of the groove, B is the size of the high-speed camera in the resolution direction B, and delta S pixel Number of pixel points, S, projected on a high speed camera for the depth of the tread pixel For the length of the corresponding number of pixel points, Δ S centre The number of pixel points S of the projection distance B resolution direction central point of the photosensitive chip on the high-speed camera on the surface of the tire centre The length of the corresponding pixel point number; b j’ Is y surfaceAVG The real world distance corresponding to the coordinates, j is formed by the epsilon (1, k'); b j” Is Y grooveAVGn The coordinates correspond to real world distances, j ∈ [ j ', k' ], b j And the pixel point with the Y coordinate of j corresponds to the real world distance, and j belongs to [ j ', j' ].
8. The method for laser measuring the tread depth of a tire as claimed in claim 6 or 7, wherein the 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;
connecting the lowest points of the tread pattern images and then translating the distance s upwards to form a reference line L1;
s＝S*h*C：
the vertical coordinate of the point position on the reference line L1 is marked as y 1;
the y-coordinate of all points of the tire surface is denoted as y surface ；
Calculating y1-y from left to right surface When the difference value crosses 0 at any time, recording the difference value as an intersection point, 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 y-axis direction corresponding to each millimeter of the real tread; 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.
9. The method for laser measuring the tread depth of a tire as claimed in claim 6 or 7, wherein the 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;
performing smoothing processing on the front wheel image and the tire pattern image of the rear wheel video;
the y-coordinate of all points of the tire surface is denoted as y surface ；
Acquiring coordinates of two points on the leftmost side and the rightmost side of the image, and respectively recording the coordinates as P1(x1, y1) and P2(x2, y 2);
comparing the y coordinates of P1 and P2 yields a smaller value y mix And calculating to obtain y 2;
y2＝y mix *i(0.8≤i≤0.9)；
y3＝y2-y surfaceAVG ；
the upward translation distance y3 after connecting the lowest points of the images forms a reference line L2, and the reference line L2 necessarily has at least 2 intersections with the pattern images;
from left to right, calculating y3-y in turn surface When the difference value crosses 0 at any time, recording the difference value as an intersection point, 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.
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