CN113983947A - Tire pattern depth detection system and method - Google Patents
Tire pattern depth detection system and method Download PDFInfo
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- CN113983947A CN113983947A CN202111127005.7A CN202111127005A CN113983947A CN 113983947 A CN113983947 A CN 113983947A CN 202111127005 A CN202111127005 A CN 202111127005A CN 113983947 A CN113983947 A CN 113983947A
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- 230000002159 abnormal effect Effects 0.000 claims description 15
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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 provides a tire pattern depth detection system and a method thereof, belonging to the technical field of automobile tire detection, wherein the tire pattern depth detection system comprises an acquisition module, a processing module and a calibration module, wherein the acquisition module is used for acquiring three-dimensional image data of each sampling point on the surface of a tire; the processing module is used for processing a first axial horizontal plane of the three-dimensional image data to obtain accurate depth data; the calibration module is used for determining the tire groove depth data based on the comparison of the accurate depth data to the data of the second axial horizontal plane.
Description
Technical Field
The invention belongs to the technical field of automobile tire detection, and particularly relates to a tire pattern depth detection system and a tire pattern depth detection method.
Background
The tire is the only place of contact with the ground of whole car, and the year-round contact with the ground, and it is natural that the wearing and tearing are. Not only the abrasion of the road surface but also the oxidation of the road surface threatens the safety of the tire. The wear of a tire generally refers to the wear of the tire pattern, which is very important. The most important three main components of the tire are respectively: tread, carcass, and rubber formulations, which may vary in their detailed design. The three most important roles of the tread pattern are: 1. the grip force is improved; 2. noise is reduced; 3. the drainage is increased. Generally the tread pattern must have a thickness greater than 1.6 mm, but if driving is to be carried out on slippery ground, it is preferable to ensure a tread pattern thickness greater than 3.2 mm.
At present, most of the grooves on the market adopt a groove depth measuring mode of a tire depth gauge, so that the error is large, the operation is inconvenient, and the measured data needs to be manually input into a supervisory system, so that the working efficiency is reduced.
Disclosure of Invention
The embodiment of the invention provides a tire pattern depth detection system and a method thereof, aiming at solving the problems that most of the conventional groove depth measurement modes adopting tire depth rulers on the market have large errors and inconvenient operation, and measurement data needs to be input into a supervision system manually.
In view of the above problems, the technical solution proposed by the present invention is:
a tire pattern depth detection system comprising a 3D structured light scanning device, the detection system comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring three-dimensional image data of each sampling point on the surface of the tire;
the processing module is used for processing a first axial horizontal plane of the three-dimensional image data to obtain accurate depth data;
a calibration module to determine tire groove depth data based on a data comparison of the precise depth data to a second axial level.
As a preferred technical solution of the present invention, the acquisition module includes:
the obtaining unit is used for scanning each sampling point on the surface of the tire through the 3D structured light scanning device to obtain sampling data;
the conversion unit is used for converting the sampling data into the three-dimensional image data after processing;
and the identification unit is used for identifying the tire characteristics according to the three-dimensional image data to obtain tire depth data.
As a preferable aspect of the present invention, the tire is characterized by a tire pattern, the number of water guide grooves, and a depth of the water guide grooves.
As a preferred technical solution of the present invention, the processing module includes:
a finding unit for finding abnormal noise data in the tire depth data based on a first axial level of the three-dimensional image data;
and the screening unit is used for filtering the abnormal noise data by using a Gaussian filtering method to obtain accurate depth data.
As a preferred technical solution of the present invention, the calibration module includes:
a sorting unit for obtaining specific angle data from the accurate depth data according to a second axial horizontal plane of the three-dimensional image data;
the comparison unit is used for comparing the specific angle data to obtain a comparison result;
and the calibration unit is used for compensating and calibrating the specific angle data larger than the preset threshold value of the inclination angle according to the comparison result, and determining the tire groove depth data.
In a preferred embodiment of the present invention, the first axial horizontal plane is based on x and z axes of a coordinate system, and the second axial horizontal plane is based on x and y axes of the coordinate system.
On the other hand, the embodiment of the invention also provides a tire pattern depth detection method, which comprises a 3D structured light scanning device, and the detection method comprises the following steps:
s1, obtaining three-dimensional image data of each sampling point on the surface of the tire;
s2, processing the first axial horizontal plane of the three-dimensional image data to obtain accurate depth data;
and S3, determining tire groove depth data based on the data comparison of the accurate depth data to the second axial horizontal plane.
As a preferred technical solution of the present invention, the obtaining of three-dimensional image data of each sampling point on the tire surface specifically comprises the steps of:
s11, scanning each sampling point on the surface of the tire through the 3D structured light scanning device to obtain sampling data;
s12, converting the sampled data into the three-dimensional image data after processing;
and S13, identifying the tire characteristics according to the three-dimensional image data to obtain tire depth data.
As a preferred technical solution of the present invention, the processing of the first axial horizontal plane of the three-dimensional image data to obtain the accurate depth data specifically includes:
s21 finding abnormal noise data in the tire depth data based on a first axial level of the three-dimensional image data;
and S22, filtering the abnormal noise data by using a Gaussian filtering method to obtain accurate depth data.
As a preferred technical solution of the present invention, the determining of the tire groove depth data based on the comparison of the accurate depth data with the data on the second axial horizontal plane specifically includes:
s31, obtaining specific angle data according to the second axial horizontal plane of the three-dimensional image data from the accurate depth data;
s32, comparing the specific angle data to obtain a comparison result;
and S33, performing compensation calibration on the specific angle data larger than the preset inclination angle threshold according to the comparison result, and determining the tire groove depth data.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
(1) the processing module of the invention processes the first axial horizontal plane of the three-dimensional image data to obtain accurate depth data, thereby not only avoiding unnecessary interference when processing real tire groove depth data, but also avoiding system data redundancy.
(2) The calibration module of the invention determines the tire groove depth data based on the comparison of the accurate depth data to the data of the second axial horizontal plane, and obtains the reliable tire groove depth data after processing, thereby not only saving time and labor, but also improving the working effect, and effectively avoiding manual participation and ensuring the accuracy of the data.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
FIG. 1 is a schematic structural diagram of a tire pattern depth detection system according to the present disclosure;
FIG. 2 is a flow chart of a tire pattern depth detection method disclosed in the present invention;
FIG. 3 is a flowchart of step S1 of a tire pattern depth detection method disclosed in the present invention;
FIG. 4 is a flowchart of step S2 of a method for detecting the depth of a tire tread according to the present invention
Fig. 5 is a flowchart of step S3 of the tire pattern depth detection method disclosed in the present invention.
Description of reference numerals: 100. an acquisition module; 110. an obtaining unit; 120. a conversion unit; 130. an identification unit; 200. a processing module; 210. a searching unit; 220. a screening unit; 300. a calibration module; 310. a finishing unit; 320. a comparison unit; 330. and a calibration unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Example one
Referring to the attached figure 1, the invention provides a technical scheme: the utility model provides a tire pattern depth detecting system, includes 3D structure optical scanning device, folds reflection of mirror surface and 3D scanner's cooperation through surveying the window and gathers each sampling point, obtains 3D point cloud picture after handling, also is three-dimensional image data, and in addition, 3D structure optical scanning device has been prior art, and its principle and structure are no longer detailed repeated herein.
Referring to fig. 1, the detection system includes an acquisition module 100, a processing module 200, and a calibration module 300.
The acquisition module 100 is used to obtain three-dimensional image data of each sampling point on the tire surface.
Further, the acquisition module 100 includes an obtaining unit 110, a conversion unit 120, and an identification unit 130, where the obtaining unit 110 is configured to scan each sampling point on the surface of the tire through the 3D structured light scanning device to obtain sampling data, the conversion unit 120 is configured to perform processing according to the sampling data and then convert the sampling data into three-dimensional image data, and the identification unit 130 is configured to identify the tire characteristics according to the three-dimensional image data to obtain tire depth data.
Specifically, the obtaining unit 110 obtains sampling data sampled by the 3D structured light scanning device, and converts the sampling data into three-dimensional image data by the converting unit 120, wherein the three-dimensional image data further includes tire characteristics, that is, in a preferred embodiment of the present invention, the tire characteristics include a tire pattern, the number of water chutes, and the depth of the water chutes.
That is, the recognition unit 130 actually recognizes the effective tire pattern, the number of water chutes, and the depth of the water chute in the three-dimensional image data when recognizing the tire characteristics from the three-dimensional image data, so as to facilitate the subsequent processing of each data.
The processing module 200 is configured to process a first axial level of the three-dimensional image data to obtain accurate depth data.
Further, the processing module 200 includes a finding unit 210 and a screening unit 220, the finding unit 210 is configured to find abnormal noise data in the tire depth data based on the first axial level of the three-dimensional image data, and the screening unit 220 is configured to filter the abnormal noise data by using a gaussian filtering method to obtain accurate depth data.
Specifically, since abnormal noise data exists in the tire depth data of the x-axis and z-axis axial horizontal planes of the three-dimensional image data, the abnormal noise data needs to be found one by one, and the abnormal noise data is filtered by a gaussian filtering method, so that accurate depth data can be obtained.
The calibration module 300 is configured to determine tire groove depth data based on a data comparison of the precision depth data to a second axial level.
Further, the calibration module 300 includes a sorting unit 310, a comparing unit 320, and a calibration unit 330, where the sorting unit 310 is configured to obtain specific angle data from the precise depth data according to a second axial horizontal plane of the three-dimensional image data, the comparing unit 320 is configured to compare the specific angle data to obtain a comparison result, and the calibration unit 330 is configured to perform compensation calibration on the specific angle data greater than the preset inclination threshold according to the comparison result, so as to determine the tire groove depth data.
Specifically, a deviation exists in part of specific angle data in the accurate depth data, and particularly, an inclination angle of the specific angle data does not meet an inclination angle preset threshold, so that the specific angle data of the x-axis axial horizontal plane and the y-axis axial horizontal plane need to be compared, and compensation calibration needs to be performed on the specific angle data larger than the inclination angle preset threshold.
In a preferred embodiment of the invention, the first axial level is referenced to the x, z axes of the coordinate system and the second axial level is referenced to the x, y axes of the coordinate system.
Example two
The embodiment of the invention also discloses a tire pattern depth detection method, which is shown by referring to the attached figures 2-5 and comprises a 3D structure optical scanning device, wherein the detection method comprises the following steps:
and S1, obtaining three-dimensional image data of each sampling point on the surface of the tire.
Further, three-dimensional image data of each sampling point on the surface of the tire is obtained, and the method specifically comprises the following steps:
s11, scanning each sampling point on the surface of the tire through a 3D structured light scanning device to obtain sampling data;
s12, converting the sampled data into three-dimensional image data after processing;
and S13, identifying the tire characteristics according to the three-dimensional image data to obtain tire depth data.
And S2, processing the first axial horizontal plane of the three-dimensional image data to obtain accurate depth data.
Further, the first axial horizontal plane of the three-dimensional image data is processed to obtain accurate depth data, and the method specifically comprises the following steps:
s21, abnormal noise data in the tire depth data is found based on the first axial level of the three-dimensional image data.
And S22, filtering the abnormal noise data by using a Gaussian filtering method to obtain accurate depth data.
And S3, determining tire groove depth data based on the data comparison of the accurate depth data to the second axial horizontal plane.
Further, the tire groove depth data is determined based on the data comparison of the accurate depth data to the second axial horizontal plane, and the specific steps are as follows:
s31, obtaining specific angle data from the accurate depth data according to the second axial horizontal plane of the three-dimensional image data;
s32, comparing the specific angle data to obtain a comparison result;
and S33, performing compensation calibration on the specific angle data larger than the preset inclination angle threshold according to the comparison result, and determining the tire groove depth data.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (10)
1. A tire pattern depth detection system, includes 3D structure light scanning device, its characterized in that, detecting system includes:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring three-dimensional image data of each sampling point on the surface of the tire;
the processing module is used for processing a first axial horizontal plane of the three-dimensional image data to obtain accurate depth data;
a calibration module to determine tire groove depth data based on a data comparison of the precise depth data to a second axial level.
2. The tire pattern depth detection system of claim 1, wherein the acquisition module comprises:
the obtaining unit is used for scanning each sampling point on the surface of the tire through the 3D structured light scanning device to obtain sampling data;
the conversion unit is used for converting the sampling data into the three-dimensional image data after processing;
and the identification unit is used for identifying the tire characteristics according to the three-dimensional image data to obtain tire depth data.
3. A tire tread depth detection system according to claim 1 or 2, wherein the tire characteristics are tire tread, number of water chutes, and depth of water chute.
4. The tire pattern depth detection system of claim 2, wherein the processing module comprises:
a finding unit for finding abnormal noise data in the tire depth data based on a first axial level of the three-dimensional image data;
and the screening unit is used for filtering the abnormal noise data by using a Gaussian filtering method to obtain accurate depth data.
5. The tire pattern depth detection system of claim 2, wherein the calibration module comprises:
a sorting unit for obtaining specific angle data from the accurate depth data according to a second axial horizontal plane of the three-dimensional image data;
the comparison unit is used for comparing the specific angle data to obtain a comparison result;
and the calibration unit is used for compensating and calibrating the specific angle data larger than the preset threshold value of the inclination angle according to the comparison result, and determining the tire groove depth data.
6. A tire tread depth detection system according to claim 5, wherein said first axial level is referenced to the x, z axes of a coordinate system and said second axial level is referenced to the x, y axes of a coordinate system.
7. A tire tread depth detection method applied to the tire tread depth detection system according to any one of claims 1 to 6, comprising a 3D structured light scanning device, wherein the detection method comprises:
s1, obtaining three-dimensional image data of each sampling point on the surface of the tire;
s2, processing the first axial horizontal plane of the three-dimensional image data to obtain accurate depth data;
and S3, determining tire groove depth data based on the data comparison of the accurate depth data to the second axial horizontal plane.
8. The method for detecting the tire pattern depth according to claim 7, wherein the step of obtaining the three-dimensional image data of each sampling point on the tire surface comprises the following specific steps:
s11, scanning each sampling point on the surface of the tire through the 3D structured light scanning device to obtain sampling data;
s12, converting the sampled data into the three-dimensional image data after processing;
and S13, identifying the tire characteristics according to the three-dimensional image data to obtain tire depth data.
9. The detecting method of the tire pattern depth detecting system according to claim 7, wherein the processing of the first axial horizontal plane of the three-dimensional image data to obtain the accurate depth data comprises the following specific steps:
s21 finding abnormal noise data in the tire depth data based on a first axial level of the three-dimensional image data;
and S22, filtering the abnormal noise data by using a Gaussian filtering method to obtain accurate depth data.
10. The method for detecting the tire pattern depth according to claim 7, wherein the step of determining the tire groove depth data based on the data comparison of the accurate depth data to the second axial horizontal plane comprises the following specific steps:
s31, obtaining specific angle data according to the second axial horizontal plane of the three-dimensional image data from the accurate depth data;
s32, comparing the specific angle data to obtain a comparison result;
and S33, performing compensation calibration on the specific angle data larger than the preset inclination angle threshold according to the comparison result, and determining the tire groove depth data.
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Cited By (1)
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| WO2023151244A1 (en) * | 2022-02-14 | 2023-08-17 | 苏州大学 | Tire tread depth extraction method and system based on 3d structured light technology |
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