CN115195357B - Tire wear monitoring method, system and storage medium - Google Patents
Tire wear monitoring method, system and storage medium Download PDFInfo
- Publication number
- CN115195357B CN115195357B CN202210499591.6A CN202210499591A CN115195357B CN 115195357 B CN115195357 B CN 115195357B CN 202210499591 A CN202210499591 A CN 202210499591A CN 115195357 B CN115195357 B CN 115195357B
- Authority
- CN
- China
- Prior art keywords
- tire
- image data
- wear
- monitoring
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 114
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000000605 extraction Methods 0.000 claims abstract description 42
- 208000035874 Excoriation Diseases 0.000 claims description 44
- 238000005299 abrasion Methods 0.000 claims description 44
- 230000002159 abnormal effect Effects 0.000 claims description 16
- 238000007781 pre-processing Methods 0.000 claims description 15
- 238000001914 filtration Methods 0.000 claims description 10
- 230000011218 segmentation Effects 0.000 claims description 10
- 238000000354 decomposition reaction Methods 0.000 claims description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000005070 sampling Methods 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 5
- 238000003709 image segmentation Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 12
- 230000008569 process Effects 0.000 abstract description 4
- 238000004891 communication Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000026676 system process Effects 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60C—VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES
- B60C11/00—Tyre tread bands; Tread patterns; Anti-skid inserts
- B60C11/24—Wear-indicating arrangements
- B60C11/246—Tread wear monitoring systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/0008—Industrial image inspection checking presence/absence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30232—Surveillance
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Mechanical Engineering (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Tires In General (AREA)
Abstract
The application discloses a method, a system and a storage medium for monitoring tire wear, which relate to the technical field of tire wear detection, wherein the method for monitoring tire wear is applied to a tire monitoring system and comprises the steps of acquiring image data of a tire; performing extraction operation on the image data according to a tire tread projection extraction method to obtain a tire tread projection of the tire; and calculating tire wear data of the tire according to the tire pattern projection. The tire wear monitoring method can calculate the tire wear data, simplifies the detection process and improves the detection efficiency.
Description
Technical Field
The present application relates to the field of tire wear detection, and in particular, to a method, a system, and a storage medium for monitoring tire wear.
Background
Currently, in an automobile repair factory or an automobile 4S workshop, the wear degree of an automobile tire is usually diagnosed by manually measuring the depth of a pattern by a professional serviceman, in the measurement process, the serviceman often needs to detach the tire to check whether the tire is worn or not and defect, then hand-hold a special pattern depth ruler for measuring the tire pattern, measure the depth of a groove at a specified position in the axial direction and the circumferential direction of the worn tire pattern, and calculate the maximum speed and the average depth of the tire pattern so as to determine the wear condition of the tire, and the manual detection procedure is complicated and affects the detection efficiency.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application provides a method, a system and a storage medium for monitoring tire wear, which can calculate tire wear data, simplify detection procedures and improve detection efficiency.
In order to solve the technical problems, the application provides the following technical scheme:
An embodiment of a first aspect of the present application provides a method for monitoring tire wear, applied to a tire monitoring system, including:
Acquiring image data of a tire;
performing extraction operation on the image data according to a tire tread projection extraction method to obtain a tire tread projection of the tire;
and calculating tire wear data of the tire according to the tire pattern projection.
The tire wear monitoring method provided by the embodiment of the first aspect of the application has at least the following beneficial effects that the tire wear monitoring method of the application is used for monitoring and inquiring the tire wear condition in real time by acquiring the image data of the automobile tire, processing the image data by adopting the tire pattern projection extraction method, extracting the tire pattern projection in the image data and further calculating the tire wear data in the tire pattern projection, so that the detection process is simplified, the labor cost and the time cost are saved, and the detection efficiency is improved.
According to some embodiments of the first aspect of the present application, the image data comprises first image data and second image data, the tire monitoring system comprises a monitoring module, the monitoring module comprises a first monitoring component, a second monitoring component and a laser transmitter, the first monitoring component and the second monitoring component are respectively movably arranged at different positions of the tire, and the acquiring the image data of the tire comprises:
performing a laser irradiation operation on the tire by the laser emitter;
Acquiring first image data of the tire in real time through the first monitoring component;
And acquiring second image data of the tire in real time through the second monitoring component.
According to some embodiments of the first aspect of the present application, the performing an extraction operation on the image data according to a tire tread projection extraction method to obtain a tread projection of the tire includes:
performing mean filtering operation on the image data to obtain smooth image data;
performing preprocessing operation on the smooth image data to obtain preprocessed image data;
and executing feature extraction operation on the preprocessed image data to obtain the tire pattern projection of the tire.
According to some embodiments of the first aspect of the present application, the performing a preprocessing operation on the smoothed image data to obtain preprocessed image data includes:
Performing image segmentation operation on the smooth image data according to a preset tire projection local threshold value to obtain segmented image data;
performing region decomposition operation on the segmented image data according to a preset region decomposition operator to obtain first decomposed image data, and performing deburring operation on the first decomposed image data to obtain second decomposed image data;
and performing interference filtering operation on the second decomposed image data according to a preset screening operator to obtain preprocessed image data.
According to some embodiments of the first aspect of the present application, the performing a feature extraction operation on the preprocessed image data to obtain a tread projection of the tire includes:
Performing intersection operation on the smooth image data and the preprocessed image data according to a preset intersection operator to obtain intersection image data;
And executing region segmentation operation on the intersection image data according to a preset binarization threshold segmentation operator to obtain the tire tread projection of the tire.
According to some embodiments of the first aspect of the application, the tire wear data comprises groove depth, groove average depth, and groove depth extremum, and the calculating the tire wear data for the tire from the tread projection comprises:
Performing laser triangulation extraction operation on the tire pattern projection to obtain tire wear two-dimensional data and tire wear three-dimensional data;
Calculating a minimum circumscribed rectangle of the tire pattern projection according to the tire wear two-dimensional data, and obtaining groove approximate pixel depth data and groove missing data of the tire according to the minimum circumscribed rectangle;
Performing splicing operation on the tire wear three-dimensional data, the groove approximate pixel depth data and the groove missing data according to a characteristic point cloud algorithm to obtain a characteristic point cloud model, and performing optimization operation on the characteristic point cloud model according to a curvature sampling algorithm to obtain an optimized point cloud model;
and performing curved surface reconstruction operation on the optimized point cloud model, and performing calculation operation on the reconstructed optimized point cloud model to obtain the groove depth, the average groove depth and the groove depth extremum.
According to some embodiments of the first aspect of the present application, the method further comprises combining the tire wear data with a preset tire wear threshold range to obtain a wear condition of the tire, comprising one of:
When the groove depth belongs to the tire abrasion threshold range, the abrasion condition is abnormal;
When the average depth of the groove belongs to the tire abrasion threshold range, the abrasion condition is abnormal;
when the groove depth extremum belongs to the tire abrasion threshold range, the abrasion condition is abnormal, wherein the tire abrasion threshold range comprises at least one of the following:
The groove depth being less than a tire groove depth threshold, or,
The average groove depth is less than the average tire groove depth threshold or,
Or the groove depth extremum is less than a tire groove depth extremum threshold.
According to some embodiments of the first aspect of the application, the method further comprises:
And when the abrasion condition is abnormal, sending early warning information corresponding to the abrasion condition and the abrasion condition to the terminal through a cloud server, wherein the early warning information comprises the current abrasion stage, the abrasion mode and the early warning time of the tire.
In a second aspect, embodiments of the present application provide a tire wear simulation system comprising:
At least one memory;
At least one processor;
At least one program;
the program is stored in the memory, and the processor executes at least one of the programs to implement:
a method of monitoring tyre wear as claimed in any one of the first aspects of the application.
An embodiment of a third aspect of the present application provides a computer-readable storage medium storing a computer-executable signal for performing:
a method of monitoring tyre wear as claimed in any one of the first aspects of the application.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
Additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a method for monitoring tire wear provided by some embodiments of the present application;
FIG. 2 is a flowchart of an extraction operation performed on image data according to a tire tread projection extraction method according to some embodiments of the present application;
FIG. 3 is a flow chart of a method of performing preprocessing operations on smooth image data provided by some embodiments of the present application;
FIG. 4 is a flow chart of a method of performing feature extraction operations on pre-processed image data provided by some embodiments of the application;
FIG. 5 is a flow chart of a method of calculating tire wear data for a tire based on a tread projection provided in accordance with some embodiments of the present application;
FIG. 6 is a block diagram of a simulation system for tire wear provided in accordance with some embodiments of the present application.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It should be noted that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that in the flowchart. The terms and the like in the description and in the claims, and in the above-described drawings, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In the description of the present application, the description of the first and second is only for the purpose of distinguishing technical features, and should not be construed as indicating or implying relative importance or implying the number of technical features indicated or the precedence of the technical features indicated.
In the description of the present application, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present application can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.
Referring to fig. 1, in a first aspect, an embodiment of the present application provides a method for monitoring tire wear, including, but not limited to, steps S110, S120, S130.
Step S110, obtaining image data of a tire;
Step S120, performing extraction operation on the image data according to a tire tread projection extraction method to obtain tire tread projections of the tire;
Step S130, calculating tire wear data of the tire according to the tire pattern projection.
It can be understood that the tire wear monitoring method of the application processes the image data by collecting the image data of the automobile tire and adopting the tire pattern projection extraction method to extract the tire pattern projection in the image data, so as to calculate the tire wear data in the tire pattern projection, so that a user can obtain the tire wear data in real time through a terminal, simplify the detection procedure, save the labor cost and the time cost and improve the detection efficiency. Specifically, the terminal may be a desktop or notebook computer, or may be a mobile terminal.
According to one embodiment of the application, a user can monitor the wear condition of a tire in real time through tire wear monitoring software in a desktop or notebook computer, and can understand that the tire wear monitoring software is provided with a tire monitoring system, on one hand, the user can check tire wear data of the tire in real time through the tire wear monitoring software and select an interested tire area through the tire wear monitoring software, so that a monitoring module in communication with the tire monitoring system preferentially collects image data of the interested tire area, on the other hand, the user can adjust relevant parameters in a tire tread projection extraction method, the frame rate of the image data captured by the monitoring module or the setting of a machine position in the monitoring module through the tire wear monitoring software, so that the tire tread projection extraction method is suitable for different tire models and different environmental conditions, and the tire wear monitoring system can automatically generate tire wear data of the tire in one time after each monitoring, stores the tire wear data of the tire in the last period into a database, so that the user can inquire the historical wear data of the tire wear data in the previous period through the tire wear monitoring software, namely, screen the historical data of the tire wear data, and can conveniently check whether the historical wear data are normally or not, and can be checked by the user, and whether the wear historical data are further can be conveniently observed through the user.
According to another embodiment of the application, a user can also view real-time tire wear data of the tire through a tire wear monitoring applet built in the mobile terminal so as to monitor the wear condition of the tire, and it can be understood that the tire wear monitoring applet is in communication connection with the tire wear monitoring software through the cloud server, on one hand, the user can view the tire wear data through the tire wear monitoring applet, and on the other hand, when the user clicks a historical data page in the tire wear monitoring applet or pulls down and refreshes the historical data page each time, the tire wear monitoring applet updates and displays the historical data of the tire wear data in real time.
Specifically, in order to improve the safety of the tire wear monitoring applet, when a user checks the wear condition of the tire at preset intervals, a login operation is required on the tire wear monitoring applet, when the user opens the tire wear monitoring applet for the first time and logs in a personal account, the user needs to be connected with a cloud server, after the tire wear monitoring applet is successfully connected with the cloud server, the tire wear monitoring applet receives and displays the wear condition of the tire through the cloud server, and at the moment, a desktop or notebook computer connected with the cloud server displays that the mobile terminal equipment is in an on-line state. More specifically, after the tire wear monitoring applet is successfully connected with the cloud server, a timer built in the tire wear monitoring applet will be started, and after a default preset period of time, and during this period, the user does not perform any operation on the tire wear monitoring applet, the desktop or notebook computer will display the mobile terminal in an off-line state.
More specifically, when the user opens the tire wear monitoring applet and logs in to the personal account number, the mobile terminal will simultaneously send relevant information such as contact information, online information, etc. of the user to the tire monitoring system in order to determine the identity of the user.
The tire monitoring system comprises a monitoring module, wherein the monitoring module comprises a first monitoring component, a second monitoring component and a laser emitter, the first monitoring component and the second monitoring component are respectively and movably arranged in different directions of a tire, and the image data of the tire is obtained.
It can be understood that the tire monitoring system comprises a monitoring module which is used for collecting image data and sending the image data to a desktop or notebook computer, and the monitoring module comprises a first monitoring component, a second monitoring component and a laser transmitter, on one hand, the orientation of the laser transmitter is high, the brightness is high and the energy density is high, the definition of the image data is improved, and on the other hand, the first monitoring component and the second monitoring component are arranged at different positions of the tire, so that the image data of the tire can be conveniently collected from different angles, and the accuracy of the image data is further improved. Specifically, the data transmission interfaces of the first monitoring component and the second monitoring component are ethernet interfaces with unlimited transmission distance, high speed and moderate cost, so that the tire monitoring system can receive the image data of the tires sent by the first monitoring component and the second monitoring component in real time. More specifically, when the user first turns on the tire wear monitoring software, i.e., first runs the tire monitoring system, the user needs to click on the open camera button to invoke the first monitoring component and the second monitoring component, so that the first monitoring component and the second monitoring component can send the collected image data to the tire monitoring system.
It should be noted that the first monitoring component and the second monitoring component are not fixedly arranged, and the first monitoring component and the second monitoring component can also acquire image data of different directions of the tire through movement, so that the image data is acquired more comprehensively, and the accuracy of the image data is improved.
According to one embodiment of the application, a laser transmitter projects a line laser on a tire surface to form a tire tread projection, a first monitoring component arranged on the side surface of the tire collects first image data of the tire and transmits the first image data to a tire monitoring system in communication with a monitoring module, and a second monitoring component arranged on the tire surface collects second image data of the tire and transmits the second image data to the tire monitoring system. The tire monitoring system processes the first image data and the second image data through a tire pattern projection extraction method, extracts the tire pattern projection of the tire, further calculates tire wear data in the tire projection, and is convenient for a user to check the tire wear data and historical data thereof in real time through a tire wear monitoring applet of the mobile terminal in real time.
Referring to fig. 2, in a first aspect, an embodiment of the present application provides a method for performing an extraction operation on image data according to a tire tread projection extraction method, including, but not limited to, steps S210, S220, S230.
Step S210, performing mean filtering operation on the image data to obtain smooth image data;
step S220, preprocessing operation is carried out on the smooth image data to obtain preprocessed image data;
Step S230, performing a feature extraction operation on the preprocessed image data to obtain a tire pattern projection of the tire.
It will be appreciated that, after the monitoring module sends the image data to the tire monitoring system, the tire monitoring system needs to perform an extraction operation on the image data according to the tire tread projection extraction method in order to extract the tire tread projection of the tire. Specifically, in the tire tread projection extraction method, gray processing is required to be performed on color image data to filter some useless information in the original image data, and a subsequent preprocessing operation is padded, wherein an rgb1-to-gray operator provides an interface for converting the color image data into gray image data. After the gray image data is obtained, mean filtering operation is needed to be carried out on the image data so as to smooth the image, noise in the image data is primarily reduced, interference of a background environment is reduced as much as possible, subsequent preprocessing operation is convenient, after the smooth image data is obtained, preprocessing operation is needed to be carried out on the smooth image data so as to further filter interference factors in the image data, improve the precision of the image data and facilitate subsequent feature extraction operation, after the preprocessed image data is obtained, feature extraction operation is needed to be carried out on the preprocessed image data, interference areas in the preprocessed image data are filtered, and tire pattern projection of a tire is extracted.
Specifically, the RGB1-to-gray operator is used to convert an RGB color image into a gray scale image.
Referring to fig. 3, in a first aspect, an embodiment of the present application provides a method for performing a preprocessing operation on smooth image data, including but not limited to step S310, step S320, and step S330.
Step S310, performing image segmentation operation on the smooth image data according to a preset tire projection local threshold value to obtain segmented image data;
Step S320, performing region decomposition operation on the split image data according to a preset region decomposition operator to obtain first decomposed image data, and performing deburring operation on the first decomposed image data to obtain second decomposed image data;
And step S330, performing interference filtering operation on the second decomposed image data according to a preset filtering operator to obtain preprocessed image data.
It is understood that the region decomposition operator is a connection operator, and the filtering operator is a select-shape operator. More specifically, the connection operator is used to separate connected parts in one region and distinguish the multiple regions, and the select-shape operator is used to screen the connected regions to remove contours that do not meet the condition.
It can be understood that after the smooth image data is obtained, preprocessing operation needs to be performed on the smooth image data to further filter interference factors in the image data, improve the accuracy of the image data, and facilitate subsequent feature extraction operation. The preprocessing operation comprises three steps, namely, image segmentation, wherein the first step is to execute region decomposition operation on smooth image data according to a preset tire pattern projection local threshold value, namely, the smooth image data is divided into a plurality of regions with different pixels, so that rough images of tire projections are further extracted on the basis of the smooth image data, and segmented image data are obtained; the second step of the preprocessing operation is region processing, because the tire tread projection has the obvious characteristics of larger region communication area and higher brightness, the region where the tire tread projection is located in the segmented image data also contains other clutters, in order to filter the clutters, the region decomposition operation is required to be performed on the segmented image data obtained in the last step according to a preset connection operator so as to decompose the region where the tire tread projection is located in the segmented image data into a plurality of communication sets, so as to obtain first decomposed image data, in order to further filter the clutters in the first decomposed image data, the deburring operation is required to be performed on the first decomposed image data so as to obtain second decomposed image data, the burrs of each region outline and the small bright spots in the first decomposed image data are removed through the deburring operation, the region outlines are smoothed, the adhesion between the regions is broken, the second decomposed image data further improves the accuracy of the tire projection on the basis of the segmented image data, the third step of the preprocessing operation is the interference filtering operation is performed on the second decomposed image data according to a preset select-shape, the interference operation is performed on the second decomposed image data, the filtered image data is filtered out of the smaller region processed by the first decomposed image data, the preprocessing of the image data further improves the accuracy of the tire projection on the basis of the second decomposed image data.
Referring to fig. 4, in a first aspect, an embodiment of the present application provides a method for performing a feature extraction operation on pre-processed image data, including, but not limited to, steps S410, S420.
Step S410, performing intersection operation on the smooth image data and the preprocessed image data according to a preset intersection operator to obtain intersection image data;
Step S420, performing region segmentation operation on the intersection image data according to a preset binarization threshold segmentation operator to obtain tire tread projection of the tire.
It is understood that the intersection operator is a reduce-domain operator, and the binarization threshold segmentation operator is a binary-threshold operator. More specifically, the reduce-domain operator is used to narrow down the definition field of the preprocessed image data to a specified definition field, and it is understood that the definition field of the resultant intersection image data is calculated as the intersection of the definition field of the preprocessed image data and the definition field of the smoothed image data, but the image size is not changed from the preprocessed image data to the intersection image data. And the binary-threshold operator is a binary threshold segmentation operator, and the binary-threshold segmentation operator has the function of automatically selecting dark areas in the intersection image data or automatically selecting bright areas in the intersection image data.
It will be appreciated that after the pre-processed image data is obtained, it is necessary to perform a feature extraction operation on the pre-processed image data, filter the interference areas in the pre-processed image data, and extract the tire tread projections. The feature extraction operation comprises two steps, wherein the first step of the feature extraction operation is to execute intersection operation on smooth image data and preprocessed image data according to a preset reduction-domain operator, further obtain a tire projection area in the smooth image data to obtain intersection image data, and the second step of the feature extraction operation is to execute area segmentation operation on the intersection image data according to a preset binary-threshold operator to find the minimum value between two peaks in the intersection image data, and segment bright areas and dark areas in the intersection image data to remove impurity interference so as to successfully obtain tire tread projection of the tire from the intersection image data.
Referring to fig. 5, in a first aspect, embodiments of the present application provide a method of calculating tire wear data for a tire based on a tire pattern projection, including, but not limited to, steps S510, S520, S530, S540.
Step S510, performing laser triangulation extraction operation on the tire pattern projection to obtain tire wear two-dimensional data and tire wear three-dimensional data;
step S520, calculating a minimum circumscribed rectangle of tire tread projection according to the two-dimensional data of tire wear, and obtaining groove approximate pixel depth data and groove missing data of the tire according to the minimum circumscribed rectangle;
Step S530, performing splicing operation on the tire wear three-dimensional data, the groove approximate pixel depth data and the groove missing data according to a characteristic point cloud algorithm to obtain a characteristic point cloud model, and performing optimization operation on the characteristic point cloud model according to a curvature sampling algorithm to obtain an optimized point cloud model;
And S540, performing curved surface reconstruction operation on the optimized point cloud model, and performing calculation operation on the reconstructed optimized point cloud model to obtain groove depth, average groove depth and groove depth extremum.
It can be understood that the tire wear data comprise groove depth, average groove depth and groove depth extremum, and in order to obtain the groove depth, average groove depth and groove depth extremum, the tire wear monitoring method of the application performs laser triangulation extraction operation on the tire pattern projection to obtain tire wear two-dimensional data and tire wear three-dimensional data, namely tire section two-dimensional data and tire table point cloud data.
Specifically, to further extract the depth value of each groove of the tire, the minimum circumscribed rectangle of the tire tread projection area in the tire wear two-dimensional data needs to be calculated and calculated, the minimum circumscribed rectangle is divided into a plurality of rectangles, the size of each rectangle is adjusted by adjusting the number of the rectangles, each groove in the tire wear two-dimensional data can be completely contained in the divided rectangles, at the moment, the minimum circumscribed rectangle with angles of the area where the groove is located is solved, and the height of the obtained rectangle is the approximate pixel depth data of the groove of the tire. Because the two-dimensional data of the tire wear is limited by the section data, the depth data of partial grooves are missing, and the method for monitoring the tire wear is used for counting the depth data of the missing partial grooves to obtain the groove missing data, so that the tire wear data with higher accuracy can be obtained in the subsequent steps.
It can be understood that, in order to extract more accurate tire wear data, the tire wear monitoring method disclosed by the application performs a splicing operation on the tire wear three-dimensional data, the groove approximate depth data and the groove missing data according to the characteristic point cloud algorithm, splices the missing grooves to obtain a characteristic point cloud model which more comprehensively reflects the tire wear condition, further optimizes the characteristic point cloud model according to the curvature sampling algorithm, and in the place with larger curvature in the characteristic point cloud model, the number of sampling points is more, the sampling result is stronger in noise resistance through the division of geometric characteristic areas, so that an optimized point cloud model is obtained, noise is further filtered by the optimized point cloud model on the basis of the characteristic point cloud model, and the tire wear data is accurate.
It can be understood that after the optimized point cloud model is obtained, the tire wear monitoring method of the application also carries out curved surface reconstruction operation on the optimized point cloud model, converts point cloud data in the optimized point cloud model into a smooth and closed curved surface, and realizes the conversion from the data point model to the curved surface model. In order to determine the abrasion condition of the tire conveniently, after the curved surface model is obtained, the reconstructed curved surface characteristic value is further calculated according to the curved surface model so as to obtain the groove depth, the average groove depth and the groove depth extremum. Specifically, the tire wear monitoring software of the desktop or notebook computer directly displays the groove depth, the average groove depth and the extreme groove depth value calculated at the current moment, and the user can also check the depth of each groove in the tire through the tire wear monitoring software.
The tire wear monitoring method further comprises the step of combining tire wear data and a preset tire wear threshold range to obtain the tire wear condition, wherein the tire wear condition comprises one of the following steps that when the groove depth belongs to the tire wear threshold range, the wear condition is abnormal, when the average groove depth belongs to the tire wear threshold range, the wear condition is abnormal, and when the groove depth extremum belongs to the tire wear threshold range, the wear condition is abnormal;
the tire wear threshold range includes at least one of a groove depth less than a tire groove depth threshold, or a groove average depth less than a tire groove average depth threshold, or a groove depth extremum less than a tire groove depth extremum threshold.
It will be appreciated that after the tire wear data is calculated, the tire wear data may also need to be compared to calibration features at various stages of tire wear to determine tire wear. Specifically, the groove depth extremum includes a groove depth maximum and a groove depth minimum, and when at least one of the groove depth maximum being less than a preset tire groove depth maximum threshold or the groove depth minimum being less than a preset tire groove depth minimum threshold is satisfied, the groove depth extremum is less than a tire groove depth extremum threshold.
According to one embodiment of the application, when the groove depth is less than the tire groove depth threshold, the average groove depth is less than the tire groove average depth threshold, and the groove depth extremum is greater than the tire groove depth extremum threshold, the average groove depth falls within the tire wear threshold range, at which time the average groove depth exceeds the wear threshold that the tire can withstand, and the tire wear is abnormal.
According to another embodiment of the present application, when the groove depth is less than the tire groove depth threshold, the average groove depth is greater than the tire groove average depth threshold, and the groove depth extremum is less than the tire groove depth extremum threshold, the groove depth extremum falls within the tire wear threshold range, at which time the groove depth extremum has exceeded the wear threshold that the tire can withstand, and the wear of the tire is abnormal.
It will be appreciated that a user may adjust the average sipe depth threshold, the maximum sipe depth threshold, and the minimum sipe depth threshold by means of the tire wear monitoring software of a desktop or laptop computer.
According to another embodiment of the present application, the present application may further set a plurality of tire groove depth thresholds and corresponding tire wear conditions around the tire groove depth in the tire wear monitoring software of the desktop or notebook computer, so as to divide the tire wear data more finely according to different tire groove depth thresholds, so as to determine the tire wear conditions more accurately.
It should be noted that, when the abrasion condition is abnormal, the method for monitoring the abrasion of the tire further comprises the step of sending the early warning information corresponding to the abrasion condition and the abrasion condition to the terminal through the cloud server, wherein the early warning information comprises the current abrasion stage, the abrasion mode and the early warning time of the tire.
It can be understood that in the application, the desktop or notebook computer containing the tire monitoring system is in communication connection with the cloud server through the MQTT protocol or the TCP protocol, and the current abrasion condition is sent to the cloud server at regular time through the MQTT protocol or the TCP protocol, and the MQTT protocol occupies less memory and supports one-to-many transmission, so that the application has the advantages of lighter weight and easy realization. On the other hand, the cloud server is connected with the terminal through an internet of things server interface based on the MQTT protocol, when the terminal is in an on-line state, the cloud server can send historical data of the current abrasion condition and the tire abrasion condition to the terminal through the MQTT protocol or the TCP protocol, and a user can conveniently monitor the abrasion condition of the tire.
According to one embodiment of the application, a user can check the current wear state, early warning content and early warning time of the tire through tire wear monitoring software in a desktop or notebook computer, and when the tire is in normal wear, the wear state is displayed normally, and the early warning content and the early warning time are displayed nothing. Specifically, when the abrasion condition is abnormal, the tire abrasion monitoring software displays the abrasion state, the early warning content and the early warning time of the tire in real time, a user can manually check the abrasion stage and the abrasion mode of the current tire in the early warning content part, and the tire monitoring system can also give out corresponding abrasion reason analysis through the abrasion stage and the abrasion mode of the current tire, so that the user can conveniently further know the abrasion condition of the tire.
More specifically, the user can select the terminal for receiving the early warning information through the tire wear monitoring software in the desktop or notebook computer, and can also set the mailbox address for sending the early warning information and the mailbox address for receiving the early warning information, when the wear condition is abnormal, the cloud server can conveniently send the early warning information to the terminal after receiving the wear condition, and the user can conveniently receive the early warning information in real time and check the tire.
In a second aspect, referring to fig. 6, an embodiment of the present application provides a simulation system of tire wear, comprising:
at least one memory 200;
At least one processor 100;
At least one program;
the programs are stored in the memory 200, and the processor 100 executes at least one program to implement:
A method of monitoring tyre wear as in any one of the embodiments of the first aspect of the application.
The processor 100 and the memory 200 may be connected by a bus or other means.
Memory 200, as a non-transitory readable storage medium, may be used to store non-transitory software instructions as well as non-transitory directives. In addition, memory 200 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. It will be appreciated that the memory 200 may alternatively comprise memory 200 located remotely from the processor 100, such remote memory 200 being connectable to the processor 100 through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor 100 implements a tire wear monitoring method of the above-described first aspect embodiment by executing non-transitory software instructions, and signals stored in the memory 200, thereby various functional applications and data processing.
Non-transitory software instructions and instructions required to implement a tire wear simulation system of the above-described embodiments are stored in the memory 200, which when executed by the processor 100, perform a method of monitoring tire wear of the first aspect of the embodiment of the present application, for example, performing the method steps S110 to S130 in fig. 1, the method steps S210 to S230 in fig. 2, the method steps S310 to S330 in fig. 3, the method steps S410 to S420 in fig. 4, and the method steps S510 to S540 in fig. 5 described above.
In a third aspect, embodiments of the present application provide a computer-readable storage medium storing a computer-executable signal for performing:
a method of monitoring tire wear as in any of the embodiments of the first aspect of the application.
For example, the above-described method steps S110 to S130 in fig. 1, method steps S210 to S230 in fig. 2, method steps S310 to S330 in fig. 3, method steps S410 to S420 in fig. 4, and method steps S510 to S540 in fig. 5 are performed.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the description of the embodiments above, those skilled in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable signals, data structures, instruction modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable signals, data structures, instruction modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and may include any information delivery media.
The embodiments of the present application have been described in detail with reference to the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present application.
Claims (7)
1.A method for monitoring tire wear, applied to a tire monitoring system, comprising:
Acquiring image data of a tire;
performing extraction operation on the image data according to a tire tread projection extraction method to obtain a tire tread projection of the tire;
Calculating tire wear data of the tire according to the tire tread projection, wherein the tire wear data comprises groove depth, groove average depth and groove depth extremum, and the calculating of the tire wear data according to the tire tread projection comprises the steps of performing laser triangulation extraction operation on the tire tread projection to obtain tire wear two-dimensional data and tire wear three-dimensional data, calculating minimum circumscribed rectangle of the tire tread projection according to the tire wear two-dimensional data to obtain groove approximate pixel depth data and groove missing data of the tire according to the minimum circumscribed rectangle, performing splicing operation on the tire wear three-dimensional data, the groove approximate pixel depth data and the groove missing data according to a characteristic point cloud algorithm to obtain a characteristic point cloud model, performing optimization operation on the characteristic point cloud model according to a curvature sampling algorithm to obtain an optimized point cloud model, performing curved surface reconstruction operation on the optimized point cloud model, and performing calculation operation on the reconstructed optimized point cloud model to obtain the groove depth, the groove average depth and the groove missing data;
The method further includes combining the tire wear data with a preset tire wear threshold range to obtain a wear condition of the tire, including one of:
When the groove depth belongs to the tire abrasion threshold range, the abrasion condition is abnormal;
When the average depth of the groove belongs to the tire abrasion threshold range, the abrasion condition is abnormal;
when the groove depth extremum belongs to the tire abrasion threshold range, the abrasion condition is abnormal, wherein the tire abrasion threshold range comprises at least one of the following:
The groove depth being less than a tire groove depth threshold, or,
The average groove depth is less than the average tire groove depth threshold or,
Or the groove depth extreme value is smaller than the tire groove depth extreme value threshold value;
the method further comprises the steps of:
And when the abrasion condition is abnormal, sending early warning information corresponding to the abrasion condition and the abrasion condition to a terminal through a cloud server, wherein the early warning information comprises the current abrasion stage, the abrasion mode and the early warning time of the tire.
2. The method of claim 1, wherein the image data comprises first and second image data, wherein the tire monitoring system comprises a monitoring module comprising a first monitoring component, a second monitoring component, and a laser transmitter, wherein the first and second monitoring components are movably disposed in different orientations of the tire, respectively, and wherein the acquiring the image data of the tire comprises:
performing a laser irradiation operation on the tire by the laser emitter;
Acquiring first image data of the tire in real time through the first monitoring component;
And acquiring second image data of the tire in real time through the second monitoring component.
3. The method for monitoring tire wear according to claim 1, wherein the performing an extraction operation on the image data according to a tire tread projection extraction method to obtain a tread projection of the tire comprises:
performing mean filtering operation on the image data to obtain smooth image data;
performing preprocessing operation on the smooth image data to obtain preprocessed image data;
and executing feature extraction operation on the preprocessed image data to obtain the tire pattern projection of the tire.
4. A method of monitoring tyre wear as claimed in claim 3, wherein said performing a preprocessing operation on said smoothed image data results in preprocessed image data, comprising:
Performing image segmentation operation on the smooth image data according to a preset tire projection local threshold value to obtain segmented image data;
performing region decomposition operation on the segmented image data according to a preset region decomposition operator to obtain first decomposed image data, and performing deburring operation on the first decomposed image data to obtain second decomposed image data;
and performing interference filtering operation on the second decomposed image data according to a preset screening operator to obtain preprocessed image data.
5. A method of monitoring tyre wear as claimed in claim 3, wherein said performing a feature extraction operation on said preprocessed image data, resulting in a tread projection of said tyre, comprises:
Performing intersection operation on the smooth image data and the preprocessed image data according to a preset intersection operator to obtain intersection image data;
And executing region segmentation operation on the intersection image data according to a preset binarization threshold segmentation operator to obtain the tire tread projection of the tire.
6. A simulation system of tire wear, comprising:
At least one memory;
At least one processor;
At least one program;
the programs are stored in the memory, and the processor executes at least one of the programs to implement the tire wear monitoring method according to any one of claims 1 to 5.
7. A computer-readable storage medium storing a computer-executable signal for performing the method of monitoring tire wear as claimed in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210499591.6A CN115195357B (en) | 2022-05-09 | 2022-05-09 | Tire wear monitoring method, system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210499591.6A CN115195357B (en) | 2022-05-09 | 2022-05-09 | Tire wear monitoring method, system and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115195357A CN115195357A (en) | 2022-10-18 |
| CN115195357B true CN115195357B (en) | 2024-11-29 |
Family
ID=83575146
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210499591.6A Active CN115195357B (en) | 2022-05-09 | 2022-05-09 | Tire wear monitoring method, system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115195357B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117571341B (en) * | 2024-01-16 | 2024-05-14 | 山东中亚轮胎试验场有限公司 | System and method for detecting omnibearing wear of tire |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201637900A (en) * | 2015-04-23 | 2016-11-01 | Zhi-Ning Chen | Vehicle tire tread detecting method |
| JP2017198672A (en) * | 2016-04-19 | 2017-11-02 | バトラー エンジニアリング アンド マーケティング エス ピー エーＢｕｔｌｅｒ Ｅｎｇｉｎｅｅｒｉｎｇ ＆ Ｍａｒｋｅｔｉｎｇ Ｓ．Ｐ．Ａ． | Device and method for analyzing and detecting geometrical feature of object |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2970210B1 (en) * | 2011-01-10 | 2013-08-02 | Peugeot Citroen Automobiles Sa | SYSTEM FOR CONTROLLING THE SPEED OF A VEHICLE BASED ON THE CONDITION OF TIRES |
| JP6455181B2 (en) * | 2015-01-28 | 2019-01-23 | 横浜ゴム株式会社 | Tire wear evaluation method |
| CN113983949B (en) * | 2021-10-28 | 2024-07-19 | 康佳集团股份有限公司 | Tire wear detection method, device and detection equipment |
-
2022
- 2022-05-09 CN CN202210499591.6A patent/CN115195357B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201637900A (en) * | 2015-04-23 | 2016-11-01 | Zhi-Ning Chen | Vehicle tire tread detecting method |
| JP2017198672A (en) * | 2016-04-19 | 2017-11-02 | バトラー エンジニアリング アンド マーケティング エス ピー エーＢｕｔｌｅｒ Ｅｎｇｉｎｅｅｒｉｎｇ ＆ Ｍａｒｋｅｔｉｎｇ Ｓ．Ｐ．Ａ． | Device and method for analyzing and detecting geometrical feature of object |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115195357A (en) | 2022-10-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109300161B (en) | Positioning method and device based on binocular vision | |
| CN114754824B (en) | Monitoring and early warning method and system for wire drawing machine | |
| CN116109587B (en) | Sealing nail welding seam quality detection method in complex environment | |
| CN115195357B (en) | Tire wear monitoring method, system and storage medium | |
| CN110458144A (en) | Object area intrusion detection method, system, device and readable storage medium storing program for executing | |
| CN119204592A (en) | Shield machine operation and maintenance management system based on data analysis | |
| CN118095971A (en) | AD calcium milk beverage processing technology assessment method, system and medium | |
| CN116900449A (en) | Welding quality monitoring method and system based on signal fusion | |
| CN119206508A (en) | A real-time geographic information monitoring system and method thereof | |
| CN118936351A (en) | Real-time monitoring system for transmission tower deformation based on visual detection | |
| CN119444734B (en) | Power tool detection method based on machine vision | |
| CN112714285A (en) | 5G communication-based power transmission line high-definition video monitoring method and system | |
| CN118537339B (en) | Method and system for evaluating surface quality of part based on sander | |
| CN117745127A (en) | Industrial safety data association analysis system and method | |
| CN117456386A (en) | AI visual recognition system for unmanned equipment | |
| CN110888914A (en) | Agricultural real-time data anomaly detection method based on adaptive filtering | |
| CN119515801B (en) | AI vision-based power equipment robot inspection system | |
| CN118015737B (en) | Intelligent door lock joint control system based on Internet of Things | |
| CN116952973B (en) | Visual detection system and method for aluminum foil light diffuse reflection material | |
| CN119048416B (en) | A tire detection method, device, electronic equipment and system | |
| CN117451849B (en) | Intelligent ultrasonic flaw detection device for in-service axle | |
| CN116883948A (en) | Security monitoring method based on target image detection | |
| CN119850535A (en) | Method and device for realizing intelligent recognition of cutter fracture condition | |
| CN120312506A (en) | A wind power equipment health monitoring method based on image processing and support vector machine | |
| CN119068704A (en) | A method and system for batch detection of empty parking spaces based on deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |