CN111429505A - Tire abnormal deformation amount detection method based on tire thickness measurement - Google Patents

Tire abnormal deformation amount detection method based on tire thickness measurement Download PDF

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CN111429505A
CN111429505A CN202010203370.0A CN202010203370A CN111429505A CN 111429505 A CN111429505 A CN 111429505A CN 202010203370 A CN202010203370 A CN 202010203370A CN 111429505 A CN111429505 A CN 111429505A
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hub
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CN111429505B (en
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肖梅
黄洪滔
明秀玲
张雷
徐婷
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Changan University
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Abstract

The invention provides a method for detecting abnormal deformation of a tire based on tire thickness measurement, which comprises the following steps: based on the circular characteristic of the hub, the deformation rate of the tire is calculated by using an image processing technology, and compared with the deformation rates under standard high pressure and low pressure, the state judgment of the tire deformation can be judged; the invention can realize the detection of the abnormal deformation of the tire, improves the driving safety of the vehicle, and is particularly suitable for monitoring the tire of a high-risk vehicle in real time.

Description

Tire abnormal deformation amount detection method based on tire thickness measurement
Technical Field
The invention relates to the technical field of automobile appearance detection and diagnosis, in particular to a method for detecting abnormal deformation of a tire based on tire thickness measurement.
Background
The automobile tire is a strong elastic rubber product, is in direct contact with the road surface, is one of important parts of the automobile, and plays a very important role in running: bear the weight of the automobile; the shock absorber and the automobile suspension are used together for relieving the impact of the automobile during running, so that the comfort and the smoothness of the automobile are ensured; the good adhesion between the wheels and the road surface is ensured, and the traction, braking and passing performance of the automobile are improved.
The deformation of the tire during running can be changed under the influence of various factors such as tire pressure, road surface temperature, load capacity, tire parameters, road surface flatness and the like, and various hidden dangers can be caused when the deformation is too large or too small. When the deformation of tires is large during the running of a vehicle, the friction coefficient with the road surface is increased, the oil consumption is increased, and further, a steering wheel is heavy and easy to deviate; the tire wall and the tire body of the tire are extruded and deformed (flexed) for a long time, so that the aging of the tire is accelerated, and the tire shoulder is worn; the friction between the tire and the ground is multiplied, the tire temperature is sharply increased, the tire is softened, the strength is reduced, and the tire burst is easily caused during high-speed running. If the deformation of the tire is small, the grip force of the tire is reduced, the friction adhesive force is reduced, and the braking performance of the vehicle is influenced; the vehicle body has large vibration, which can affect the comfort of drivers and passengers and indirectly affect the service life of other parts; the local abrasion of the central pattern of the tire tread is accelerated, so that the service life of the tire is reduced; the tire wall tension is too large, so that the elasticity of the tire body is reduced, the load on the vehicle is increased, and the probability of tire burst caused by rupture during the impact of foreign matters is greatly increased. Therefore, the automatic detection of the deformation of the vehicle is very important for slowing down the abrasion of the tire, improving the driving safety and the like. However, the slight deformation rate of the tire is difficult to be directly observed by naked eyes, and therefore, an automatic detection method of the deformation rate is needed to solve the safety problem caused by abnormal deformation of the vehicle tire.
Disclosure of Invention
The invention aims to provide a method for detecting abnormal deformation of a tire based on tire thickness measurement, which solves the problem that the driving safety of a vehicle is caused because the abnormal deformation of the tire of the existing vehicle cannot be detected.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides a method for detecting abnormal deformation of a tire based on tire thickness measurement, which comprises the following steps:
step 1, preprocessing an acquired tire image to obtain a tire binary image;
step 2, extracting a hub area and a ground area on the tire binary image obtained in the step 1, and respectively using the hub area and the ground area as a hub initial image and a ground area image;
step 3, performing morphological processing on the hub initial image in the step 2 to obtain a hub filling image;
step 4, calculating hub parameters according to the hub filling map obtained in the step 3;
step 5, constructing a correction circle according to the hub parameters obtained in the step 4;
step 6, updating the hub filling map by using the correction circle obtained in the step 5; calculating the centroid coordinate and the virtual radius of the updated hub filling map;
step 7, processing the virtual radius of the hub filling map in the step 3 and the virtual radius of the hub filling map updated in the step 6, wherein when | r' -r | is less than or equal to τ, the step 8 is carried out, otherwise, the step 5 is carried out;
step 8, extracting the lowest point group of the hub from the updated hub filling map;
step 9, calculating the shortest distance between the lowest point group of the hub and the ground area;
step 10, calculating the actual tire thickness of the tire according to the shortest distance between the lowest point group of the hub and the ground area obtained in the step 9;
step 11, calculating the load deformation rate of the tire according to the actual tire thickness of the tire obtained in the step 10;
and step 12, judging the deformation state of the tire according to the load deformation rate of the tire obtained in the step 11.
Preferably, in step 1, the acquired tire image is preprocessed by the specific method:
s1, carrying out graying and denoising treatment on the collected tire image to obtain a preprocessed tire image;
s2, performing two-classification segmentation on the preprocessed tire image to obtain a tire binary primary image;
s3, performing morphological processing on the obtained tire binary primary image to obtain a tire binary image;
when the tire image is collected, the collecting equipment and the center of the wheel hub are at the same height, and the shooting angle is coincident with the wheel axle.
Preferably, in step 2, the hub region and the ground region are extracted from the tire binary map obtained in step 1, and the specific method is as follows:
setting H communicating area blocks on a tire binary image, and if one of the communicating area blocks simultaneously satisfies the following formula, the communicating area block is a hub area and is used as a hub initial image:
Figure BDA0002420121220000031
Figure BDA0002420121220000032
Figure BDA0002420121220000033
wherein, ba isiIs the sum of the pixels of the ith connected region in the tire binary image α1Is the hub size threshold; bciThe pixel sum of the circumscribed rectangle template of the ith connected region is obtained;
Figure BDA0002420121220000034
the squareness of the ith connected region in the tire binary map β1Is a rectangle degree threshold; oxiA row coordinate value of the centroid of the ith connected region; gamma ray1Is a hub position threshold;
if a certain connected region block in the tire binary image simultaneously satisfies the following formula, the connected region block is regarded as a ground region, and is taken as a ground region image:
Figure BDA0002420121220000035
Figure BDA0002420121220000036
preferably, in step 3, the form processing is performed on the hub initial map in step 2 to obtain a hub filling map, and the specific method is as follows:
performing closed operation on the hub initial image by using a circular structure operator sf to fill small area blocks which are missed to be detected in the hub initial image to obtain a hub filling image;
in step 4, calculating hub parameters according to the hub filling map obtained in step 3, wherein the specific method comprises the following steps:
setting the pixel value of a hub area in the hub filling graph to be 1, and setting the pixel values of the other areas to be 0; the parameters of the hub region are calculated according to the following formula:
Figure BDA0002420121220000041
Figure BDA0002420121220000042
Figure BDA0002420121220000043
wherein cx and cy are respectively the centroid row and column coordinate values of the hub area; r is the virtual radius.
Preferably, in step 5, a correction circle is constructed according to the hub parameters obtained in step 4, and the specific method is as follows:
determining the center coordinates (cx ', cy') and the radius r 'of the correction circle, wherein cx' ═ cx; cy' ═ cy; r' ═ r;
in step 6, updating the hub filling map by using the correction circle obtained in step 5, wherein the specific method comprises the following steps:
removing a noise area in the hub filling map by using the correction circle to obtain an updated hub filling map;
preferably, in step 8, the lowest point group of the hub is extracted from the updated hub patch map by the specific method:
in the updated hub filling map, taking all pixel points with the row coordinates as the maximum value as a hub lowest point group;
in step 9, the shortest distance between the lowest point group of the hub and the ground area is calculated according to the following formula:
Figure BDA0002420121220000044
wherein d ispThe shortest distance between a pixel point p in the lowest point group and the ground area is obtained; x is the number ofpAnd ypThe coordinate values of the row and the column of the pixel point p are shown; x is the number ofqAnd yqAnd the coordinate values of the row and the column of the pixel point q are shown.
Preferably, in step 10, the actual tire thickness of the tire is calculated according to the shortest distance between the lowest point group of the hub and the ground area obtained in step 9, and the specific method is as follows:
Figure BDA0002420121220000045
wherein dt is the actual tire thickness of the tire;
Figure BDA0002420121220000051
in terms of the size of a unit pixel,
Figure BDA0002420121220000052
dl is the actual diameter of the hub; 2r is the pixel diameter of the hub.
Preferably, in step 11, the load deformation rate of the tire is calculated according to the actual tire thickness of the tire obtained in step 10, and the specific method is as follows:
Figure BDA0002420121220000053
wherein ξ is the load deflection of the tire, db is the standard tire thickness of the tire, db is S.mu, S is the tire width, and mu is the flat ratio.
Preferably, in step 12, the deformation state of the tire is determined according to the load deformation rate of the tire obtained in step 11, specifically, the deformation state of the tire is determined
Figure BDA0002420121220000054
Wherein fl is a state flag of the tire deformation amount, fl ═ 1 indicates that the tire deformation amount is abnormal, and fl ═ 0 indicates that the tire deformation amount is normal; k is an upper threshold value of the deformation amount,
Figure BDA0002420121220000055
lambda is the lower threshold value of the deformation amount,
Figure BDA0002420121220000056
dd is the tire thickness value measured by the new tire under the standard load and low air pressure; dh is the tire thickness value measured under standard load and high air pressure for the new tire.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method for detecting abnormal deformation of a tire based on tire thickness measurement, which is characterized in that the deformation rate of the tire is calculated by utilizing an image processing technology based on the circular characteristic of a hub, and compared with the deformation rates under standard high pressure and low pressure, the state judgment of the tire deformation can be judged. The method can detect the abnormal deformation of the tire when no special tire pressure measuring tool is available or the air temperature, the load and the road surface condition and the like change, improves the driving safety of the vehicle, and is particularly suitable for monitoring the tire of a high-risk vehicle in real time.
Drawings
FIG. 1 is a tire image f;
FIG. 2 is a pre-processed tire image g;
FIG. 3 is a tire binary initial map b;
FIG. 4 is a tire binary map e;
FIG. 5 is a front view c of the hub;
FIG. 6 is a ground area view R;
fig. 7 is a hub filling view ca.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The method for detecting the abnormal deformation of the tire based on the tire thickness measurement can be operated on a client side in an off-line mode, and can also be combined with cloud data to achieve on-line automatic detection; the method specifically comprises the following steps:
and (0) acquiring an automobile tire image, acquiring the image of the automobile tire by using image acquisition equipment (or a smart phone) under the condition that the automobile tire is not disassembled, wherein the image acquisition equipment has the same height with the center of a wheel hub, the shooting angle is coincident with the wheel axle, the complete tire image is required to be shot, the tire part is positioned at the middle upper part of the image, the acquired tire image is represented by a symbol f, the image size is marked as M and N, namely the total line number and the total column number of the image f are respectively M lines and N columns, and the total pixel point is M × N.
In this embodiment, M is 773, N is 604, and the tire image f is shown in fig. 1.
Step 1: and (5) image preprocessing. The image preprocessing comprises the following steps: the gray level processing method comprises the following steps of processing such as image graying and denoising, wherein the image graying can reduce the calculation amount of subsequent processing, and the gray level processing method has a plurality of methods, and can adopt a weighted average method to perform graying processing so as to obtain a more reasonable gray level image; the denoising processing can reduce noise (such as mud spots, stains and the like) in the tire image, improve the precision of subsequent processing, and can adopt a median filter aiming at salt and pepper noise. The tire image f is preprocessed to obtain a preprocessed tire image g.
In the example, the preprocessed tire image g is shown in fig. 2.
Step 2: and preprocessing the tire image for binarization. The preprocessed tire image g has bright contrast, the ground and the wheel hub are high-brightness colors, the tire is dark, the preprocessed tire image g is subjected to two-classification segmentation by utilizing the pair difference of brightness, and a tire binary initial image b is obtained as shown in a formula (1).
Figure BDA0002420121220000071
Wherein x and y are the coordinate values of row and column of a certain pixel point in the image, both are integer values, and x is more than or equal to 1 and less than or equal to M; y is more than or equal to 1 and less than or equal to N; t is an image segmentation threshold value and can be determined by a maximum inter-class method.
In this embodiment, the initial tire binary image b is shown in fig. 3.
And step 3: and (5) processing the form of the tire binary image. Due to the influence of noise, shadow and the like, in the tire binary initial image b, cavities exist in areas such as a hub and the ground, which is not beneficial to subsequent processing, and therefore morphological processing should be performed on the tire binary initial image. The method specifically comprises the following steps: and (3) carrying out closed operation (namely expansion operation and corrosion operation) on the tire binary initial image b by using a small structural operator se, and then carrying out void filling operation to obtain a tire binary image e.
In this embodiment, in consideration of the shape of the hub, the structure operator se is a circular structure operator with a small radius (radius 5) to prevent the non-hub region and the hub region from being connected, and the tire binary map e is shown in fig. 4.
And 4, step 4: and extracting a hub area of the tire binary image e. The hub has the characteristics of being circular, large in size, located on the upper portion of the image and the like in the tire image, and the hub area in the tire binary image e is extracted based on the hub characteristics. Assuming that there are H connected region blocks in the tire binary image e, if a certain connected region block simultaneously satisfies the formula (2-4), the connected region block is considered as a hub region, and the region block is extracted to obtain a hub initial image c.
Figure BDA0002420121220000072
Figure BDA0002420121220000073
Figure BDA0002420121220000074
Wherein, ba isiIs the sum of the pixels of the ith connected region in the tire binary map e α1The hub size threshold is usually 0.18-0.5; bciThe image of the circumscribed rectangle template (the circumscribed rectangle and the internal pixel value thereof are both 1) of the ith connected regionThe sum of elements;
Figure BDA0002420121220000081
the squareness of the ith connected region in the tire binary map e β1A squareness threshold, typically 0.70-0.85; oxiA row coordinate value of the centroid of the ith connected region; gamma ray1The value of the hub position threshold is 0.6-0.75.
In this embodiment, α1Value of 0.2, β1Value of 0.7, gamma1The value is 0.65; the parameters of the hub region i ═ l are as follows: bal＝144843，bcl＝189570，Oxl369, there are:
Figure BDA0002420121220000082
and
Figure BDA0002420121220000083
all conform to the conditions in the formula (2-4), and the initial drawing c of the extracted hub is shown in FIG. 5.
And 5: and extracting the ground area of the tire binary image e. Based on the fact that the ground area in the tire image is large in size and located at the bottom of the image, if a certain connected area block in the tire binary image e satisfies the formula (5-6) at the same time, the connected area block is regarded as the ground area, and the ground area image is denoted as R.
Figure BDA0002420121220000084
Figure BDA0002420121220000085
In this embodiment, the parameters of the ground area i ═ j are as follows: baj＝128001，Oxj684, there are:
Figure BDA0002420121220000086
and
Figure BDA0002420121220000087
in accordance with the conditions in the formula (5-6)The ground area map taken as R is shown in fig. 6.
Step 6: and (3) performing closed operation (namely expansion operation and corrosion operation) on the hub initial image c by using a larger circular structure operator sf to fill small area blocks which are missed to be detected in the hub initial image c, so as to obtain a hub filling image ca.
In this embodiment, a circular structural operator sf with a radius of 35 is selected, and an obtained hub filling map ca is shown in fig. 7.
And 7: the parameters of the hub are calculated. In the hub filling map ca, only the hub area is marked as 1, the pixel values of the other areas are 0, and the parameter calculation process of the hub area is shown as formula (7-9).
Figure BDA0002420121220000091
Figure BDA0002420121220000092
Figure BDA0002420121220000093
Wherein cx and cy are respectively the centroid row and column coordinate values of the hub area; r is the virtual radius.
In this embodiment, cx and cy are 337 and 369, respectively, and the virtual radius r is 216.3.
And 8: a correction circle is constructed. The purpose of constructing the correction circle is to accurately divide a hub area, the parameters of the correction circle to be determined comprise center coordinates (cx ', cy ') and a radius r ', and the initialization or updating strategy of the parameters of the correction circle is as follows: cx ' ═ cx, cy ' ═ cy and r ' ═ r.
And step 9: the hub patch map ca is updated. The noise area in ca is removed by the correction circle, that is, the pixel values outside the correction circle are all set to 0, and the pixel values inside the correction circle are kept unchanged.
Step 10: the centroid coordinates (cx, cy) and the virtual radius r of the updated hub patch ca are calculated as shown in (7-9).
Step 11: if the r' -r is less than or equal to tau, turning to the step 12; otherwise, go to step 8. Wherein tau represents a radius threshold value, and the values are respectively 0.001-0.1.
In this embodiment, the radius threshold τ is 0.05. After 21 iterations of the loop of steps 8 to 11, r '212.4075, r 212.4367, we know that | r' -r | ═ 0.0292<0.05 ═ τ.
Step 12: and extracting the lowest point group of the hub. And in the updated hub filling map ca, all pixel points with the largest row coordinates are the hub lowest point group, and W is recorded.
In this embodiment, the row and column coordinate values of the hub lowest point group W are shown in table 1.
TABLE 1 coordinate values of the lowest point group W of the hub
Figure BDA0002420121220000101
Step 13: and calculating the shortest distance between the lowest point group of the hub and the ground area.
Wherein d ispThe shortest distance between a pixel point p in the lowest point group and the ground area is obtained; x is the number ofpAnd ypThe coordinate values of the row and the column of the pixel point p are shown; x is the number ofqAnd yqAnd the coordinate values of the row and the column of the pixel point q in the ground area are shown.
In this embodiment, the shortest distance between the lowest point group and the ground area is shown in table 2.
TABLE 2 shortest distance between the lowest Point group and the ground area
Figure BDA0002420121220000103
Figure BDA0002420121220000111
Step 14: calculating unit pixel size
Figure BDA0002420121220000112
Figure BDA0002420121220000113
Wherein dl is the actual diameter of the hub known from the specification and size; 2r is the pixel diameter of the hub.
In this example, dl is 15 inches, i.e., 38.1cm, to obtain
Figure BDA0002420121220000114
Step 15: the actual tire thickness of the tire is calculated. And the average value of the shortest distances of the lowest point group of the wheel hub is the minimum tire thickness of the tire.
Figure BDA0002420121220000115
Where dt is the actual tire thickness of the tire.
In this example, dt is 5.1382.
Step 16: the standard tire thickness of the tire is calculated. The standard tire thickness of the tire is calculated from the tire's gauge dimensions (ratio of tire width to flat).
db＝S·μ (13)
Wherein S is the tread width and mu is the aspect ratio.
In this example, the tire width S was 195cm, and the aspect ratio μ was: 65% of the total amount of db was found to be 12.87 cm.
And step 17, calculating the load deformation rate ξ of the tire.
Figure BDA0002420121220000116
In this example, the load deformation ratio ξ of the tire was 0.3992.
Step 18: an abnormal state of tire deformation is determined. Judging the deformation state of the tire according to the deformation rate: normal or abnormal.
Figure BDA0002420121220000121
Wherein fl is a state flag of the tire deformation amount, and is a binary variable, fl ═ 1 indicates that the tire deformation amount is abnormal, and fl ═ 0 indicates that the tire deformation amount is normal; the upper and lower thresholds of the deformation of kappa and lambda are determined by the tire thickness of the automobile under standard low pressure and high pressure, and the calculation formula is shown as (16-17).
And when the standard load is carried, measuring the tire thickness of the new tire under the standard low pressure and high pressure, wherein the tire thickness is a reference value, and directly inquiring after the measurement is finished.
Figure BDA0002420121220000122
Figure BDA0002420121220000123
Wherein dd is the tire thickness value measured by the new tire under the standard load and low air pressure; dh is the tire thickness value measured under standard load and high air pressure for the new tire.
In this example, κ and λ were measured as 0.75 and 0.85, respectively, and since ξ < κ, fl ═ 0.
Step 19: the algorithm ends.

Claims (9)

1. A method for detecting abnormal deformation of a tire based on tire thickness measurement is characterized by comprising the following steps:
step 1, preprocessing an acquired tire image to obtain a tire binary image;
step 2, extracting a hub area and a ground area on the tire binary image obtained in the step 1, and respectively using the hub area and the ground area as a hub initial image and a ground area image;
step 3, performing morphological processing on the hub initial image in the step 2 to obtain a hub filling image;
step 4, calculating hub parameters according to the hub filling map obtained in the step 3;
step 5, constructing a correction circle according to the hub parameters obtained in the step 4;
step 6, updating the hub filling map by using the correction circle obtained in the step 5; calculating the centroid coordinate and the virtual radius of the updated hub filling map;
step 7, processing the virtual radius of the hub filling map in the step 3 and the virtual radius of the hub filling map updated in the step 6, wherein when | r' -r | is less than or equal to τ, the step 8 is carried out, otherwise, the step 5 is carried out;
step 8, extracting the lowest point group of the hub from the updated hub filling map;
step 9, calculating the shortest distance between the lowest point group of the hub and the ground area;
step 10, calculating the actual tire thickness of the tire according to the shortest distance between the lowest point group of the hub and the ground area obtained in the step 9;
step 11, calculating the load deformation rate of the tire according to the actual tire thickness of the tire obtained in the step 10;
and step 12, judging the deformation state of the tire according to the load deformation rate of the tire obtained in the step 11.
2. The method for detecting the abnormal deformation of the tire based on the tire thickness measurement as claimed in claim 1, wherein in the step 1, the acquired tire image is preprocessed, and the specific method is as follows:
s1, carrying out graying and denoising treatment on the collected tire image to obtain a preprocessed tire image;
s2, performing two-classification segmentation on the preprocessed tire image to obtain a tire binary primary image;
s3, performing morphological processing on the obtained tire binary primary image to obtain a tire binary image;
when the tire image is collected, the collecting equipment and the center of the wheel hub are at the same height, and the shooting angle is coincident with the wheel axle.
3. The method for detecting the abnormal deformation of the tire based on the tire thickness measurement as claimed in claim 1, wherein in the step 2, the hub region and the ground region are extracted from the tire binary image obtained in the step 1, and the specific method is as follows:
setting H communicating area blocks on a tire binary image, and if one of the communicating area blocks simultaneously satisfies the following formula, the communicating area block is a hub area and is used as a hub initial image:
Figure FDA0002420121210000021
Figure FDA0002420121210000022
Figure FDA0002420121210000023
wherein, ba isiIs the sum of the pixels of the ith connected region in the tire binary image α1Is the hub size threshold; bciThe pixel sum of the circumscribed rectangle template of the ith connected region is obtained;
Figure FDA0002420121210000024
the squareness of the ith connected region in the tire binary map β1Is a rectangle degree threshold; oxiA row coordinate value of the centroid of the ith connected region; gamma ray1Is a hub position threshold;
if a certain connected region block in the tire binary image simultaneously satisfies the following formula, the connected region block is regarded as a ground region, and is taken as a ground region image:
Figure FDA0002420121210000025
Figure FDA0002420121210000026
4. the method for detecting the abnormal deformation of the tire based on the tire thickness measurement as claimed in claim 1, wherein in the step 3, the form processing is performed on the initial wheel hub image in the step 2 to obtain a wheel hub filling image, and the specific method is as follows:
performing closed operation on the hub initial image by using a circular structure operator sf to fill small area blocks which are missed to be detected in the hub initial image to obtain a hub filling image;
in step 4, calculating hub parameters according to the hub filling map obtained in step 3, wherein the specific method comprises the following steps:
setting the pixel value of a hub area in the hub filling graph to be 1, and setting the pixel values of the other areas to be 0; the parameters of the hub region are calculated according to the following formula:
Figure FDA0002420121210000031
Figure FDA0002420121210000032
Figure FDA0002420121210000033
wherein cx and cy are respectively the centroid row and column coordinate values of the hub area; r is the virtual radius.
5. The method for detecting the abnormal deformation of the tire based on the tire thickness measurement as claimed in claim 1, wherein in the step 5, the correction circle is constructed according to the hub parameters obtained in the step 4, and the specific method is as follows:
determining the center coordinates (cx ', cy') and the radius r 'of the correction circle, wherein cx' ═ cx; cy' ═ cy; r' ═ r;
in step 6, updating the hub filling map by using the correction circle obtained in step 5, wherein the specific method comprises the following steps:
and removing the noise area in the hub filling map by using the correction circle to obtain an updated hub filling map.
6. The method for detecting the abnormal deformation of the tire based on the tire thickness measurement as claimed in claim 1, wherein in step 8, the lowest point group of the wheel hub is extracted from the updated wheel hub patch map by the following specific method:
in the updated hub filling map, taking all pixel points with the row coordinates as the maximum value as a hub lowest point group;
in step 9, the shortest distance between the lowest point group of the hub and the ground area is calculated according to the following formula:
Figure FDA0002420121210000034
wherein d ispThe shortest distance between a pixel point p in the lowest point group and the ground area is obtained; x is the number ofpAnd ypThe coordinate values of the row and the column of the pixel point p are shown; x is the number ofqAnd yqAnd the coordinate values of the row and the column of the pixel point q are shown.
7. The method for detecting the abnormal deformation of the tire based on the tire thickness measurement as claimed in claim 1, wherein in the step 10, the actual tire thickness of the tire is calculated according to the shortest distance between the wheel hub lowest point group obtained in the step 9 and the ground area, and the specific method is as follows:
Figure FDA0002420121210000041
wherein dt is the actual tire thickness of the tire;
Figure FDA0002420121210000042
in terms of the size of a unit pixel,
Figure FDA0002420121210000043
dl is the actual diameter of the hub; 2r is the pixel diameter of the hub.
8. The method for detecting the abnormal deformation of the tire based on the tire thickness measurement as claimed in claim 1, wherein in step 11, the deformation rate of the tire under load is calculated according to the actual tire thickness of the tire obtained in step 10 by:
Figure FDA0002420121210000044
wherein ξ is the load deflection of the tire, db is the standard tire thickness of the tire, db is S.mu, S is the tire width, and mu is the flat ratio.
9. The method for detecting the abnormal deformation of the tire based on the tire thickness measurement as claimed in claim 1, wherein in step 12, the deformation state of the tire is judged according to the load deformation rate of the tire obtained in step 11, and the specific method is as follows:
Figure FDA0002420121210000045
wherein fl is a state flag of the tire deformation amount, fl ═ 1 indicates that the tire deformation amount is abnormal, and fl ═ 0 indicates that the tire deformation amount is normal; k is an upper threshold value of the deformation amount,
Figure FDA0002420121210000046
lambda is the lower threshold value of the deformation amount,
Figure FDA0002420121210000047
dd is the tire thickness value measured by the new tire under the standard load and low air pressure; dh is the tire thickness value measured under standard load and high air pressure for the new tire.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021185332A1 (en) * 2020-03-20 2021-09-23 长安大学 Tire thickness measurement based method for detecting abnormal deformation amount of tire

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116205923B (en) * 2023-05-06 2023-07-14 威海锐鑫丰金属科技有限公司 Nondestructive testing method for internal defects of automobile hub based on X-RAY

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004038794A (en) * 2002-07-05 2004-02-05 Toyota Motor Corp Image processing device and image processing method
CN101856947A (en) * 2010-06-09 2010-10-13 孟祥厚 Automobile wheel hub with tyre inclination retainer ring and tyre explosion wheel
CN105021126A (en) * 2015-06-18 2015-11-04 中国计量学院 Truck side guard rail mounting size measurement system and method based on machine vision technology

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105172494B (en) * 2015-09-18 2018-06-05 浙江宇视科技有限公司 A kind of tire safety detection method and tire safety detecting system
JP7073891B2 (en) * 2018-05-02 2022-05-24 コニカミノルタ株式会社 Overload detection processing equipment, overload detection system and programs
JP2019211223A (en) * 2018-05-31 2019-12-12 コニカミノルタ株式会社 Image processing device, overload detection device, overload detection system and program
CN109003258B (en) * 2018-06-15 2021-10-19 广东工业大学 High-precision sub-pixel circular part measuring method
CN111383225B (en) * 2020-03-20 2023-04-07 长安大学 Method for detecting abnormal state of tire by comparing standard deformation rates
CN111429505B (en) * 2020-03-20 2023-03-28 长安大学 Tire abnormal deformation amount detection method based on tire thickness measurement

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004038794A (en) * 2002-07-05 2004-02-05 Toyota Motor Corp Image processing device and image processing method
CN101856947A (en) * 2010-06-09 2010-10-13 孟祥厚 Automobile wheel hub with tyre inclination retainer ring and tyre explosion wheel
CN105021126A (en) * 2015-06-18 2015-11-04 中国计量学院 Truck side guard rail mounting size measurement system and method based on machine vision technology

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
吕聪颖等: "一种基于图像的轮胎表面破损识别算法", 《科技通报》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021185332A1 (en) * 2020-03-20 2021-09-23 长安大学 Tire thickness measurement based method for detecting abnormal deformation amount of tire

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