Tire mark intelligent identification method, device, equipment and medium

Abstract

The invention provides a tire mark intelligent identification method, device and equipment and a medium, and belongs to the technical field of tire mark identification. A tire mark image is obtained, and the tire mark image is preprocessed; executing an image conversion process, and converting the tire impression image into a two-dimensional binary impression image matrix based on a preset conversion threshold value; identifying starting coordinates and stopping coordinates of a tire tread on the basis of the impression image matrix, dividing a plurality of tread areas in the impression image matrix, and constructing a tire contour line equation; fitting the tire contour line equation based on a preset algorithm, and judging whether the fitting precision meets a fitting precision threshold value or not; and if the fitting precision does not meet the fitting precision threshold value, adjusting the tire contour line equation parameters until the fitting precision meets the fitting precision threshold value. By identifying and evaluating the tire marks, the tire evaluation indexes can be quickly and accurately calculated, and data support is provided for tire performance analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of tire footprint recognition, and in particular relates to a tire footprint intelligent recognition method, device, equipment and medium. Background Art

[0002] As a component connecting the vehicle to the ground, tires play a role in shock absorption, driving, and supporting. Their performance plays a vital role in the vehicle's ride comfort, handling stability, and driving safety. As the current application scenarios of commercial vehicles become more and more refined, the requirements for various performance of tires are becoming more and more stringent. This requires the analysis of tire performance. In traditional tire performance analysis, it is usually based on manual measurement and observation of tire footprints to evaluate key indicators such as tire wear, contact area, and pressure distribution.

[0003] With the development of computer vision and image processing technology, related technologies include the use of computers to automatically perform detection, such as a method for automated tire footprint shape measurement and analysis, which converts the static load footprint image of the tire into a grayscale image, extracts and screens the contour, performs α-shape analysis on the footprint contour, obtains a suitable tire footprint envelope contour, and then analyzes the overall shape of the tire. This method only analyzes the tire as a whole, without partitioning the tread, resulting in low analysis accuracy. In addition, the α-shape analysis of the footprint contour has not been accurately measured, and the measurement results cannot guarantee the accuracy requirements, making it difficult to meet the high-precision and real-time requirements of tire performance analysis. Summary of the invention

[0004] The present invention provides a tire footprint intelligent recognition method, which can quickly and accurately calculate tire evaluation indicators through the recognition and evaluation of tire footprints, and provide data support for tire performance analysis.

[0005] Methods include: S101: Acquire a tire footprint image and pre-process the tire footprint image; S102: executing an image conversion process to convert the tire footprint image into a two-dimensional binary footprint image matrix based on a preset conversion threshold; S103: identifying the starting coordinates and the ending coordinates of the tire tread based on the imprint image matrix, dividing a plurality of tread areas in the imprint image matrix, and constructing a tire contour line equation; S104: Fitting the tire contour equation based on a preset algorithm, and determining whether the fitting accuracy meets a fitting accuracy threshold; S105: If not, adjust the tire profile equation parameters until the fitting accuracy meets the fitting accuracy threshold.

[0006] It should be further explained that, in step S103, the abscissa and ordinate of each tread area are obtained, and the tire contour curve is fitted according to the abscissa and ordinate.

[0007] It is further necessary to explain that the coordinates of several positions on the boundary of each tread area are extracted; Construct the nth-order equation of tire contour; The nth-order equation of the tire contour is: ; Where L is the vertical coordinate of the tire track boundary, P is the horizontal coordinate of the tire track boundary, and A n is the coefficient of the equation.

[0008] It should be further explained that, in the method, the nth-order equation of the tire contour line is fitted starting from the first-order equation; The tire contour line equation obtained by each fitting is calculated to calculate the fitting accuracy, and it is determined whether the fitting accuracy meets the fitting accuracy threshold.

[0009] It should be further explained that, S102: executing an image conversion process to convert the tire footprint image into a two-dimensional binary footprint image matrix; By setting the conversion threshold, the tire footprint gray matrix is ​​converted into a two-dimensional binary matrix of black and white; The element value at the tire tread is 1, and the element value at the tire groove is 0.

[0010] It should be further explained that step S103 also includes: defining the start and end columns and the end column of the tread; Extract each column vector of the impression image matrix, determine whether the column vector has tread contact, and determine whether the column vector has an element with a value of 1 in the column vectors before and after the current column vector, when a column vector itself has an element with a value of 1 and the previous column vector does not contain an element with a value of 1, determine that it is the tread starting column; When a column vector has an element with a value of 1, and the subsequent column vector does not contain an element with a value of 1, it is determined to be a tread termination column.

[0011] It should be further explained that, in step S101, the tire footprint image is preprocessed in a manner including: configuring the tire footprint image as an RGB matrix image, and converting it into a grayscale matrix.

[0012] The present application also provides a tire footprint intelligent recognition device, the device comprising: An image acquisition module, used for acquiring a tire print image and preprocessing the tire print image; An image conversion module, used for executing an image conversion process, and converting the tire footprint image into a two-dimensional binary footprint image matrix based on a preset conversion threshold; A tire profile construction module, which identifies the starting coordinates and the ending coordinates of the tire tread based on the imprint image matrix, divides a plurality of tread areas in the imprint image matrix, and constructs a tire profile equation; The tire profile fitting module fits the tire profile equation based on a preset algorithm and determines whether the fitting accuracy meets the fitting accuracy threshold; if not, the tire profile equation parameters are adjusted until the fitting accuracy meets the fitting accuracy threshold.

[0013] According to another embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the tire footprint intelligent recognition method when executing the program.

[0014] According to another embodiment of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the tire footprint intelligent recognition method are implemented.

[0015] It can be seen from the above technical solutions that the present invention has the following advantages: The tire footprint intelligent recognition method provided by the present application obtains a tire footprint image, pre-processes the tire footprint image, identifies the footprint image matrix, and then fits the tire contour equation. By continuously adjusting the parameters, the fitting accuracy of the tire contour equation meets the preset threshold requirements. Improve the accuracy and reliability of the tire contour equation. The present application extracts, processes and identifies tire footprint images, and by continuously adjusting the parameters, the fitting accuracy of the tire contour equation meets the preset threshold requirements. Improve the accuracy and reliability of the tire contour equation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a flow chart of the tire footprint intelligent recognition method; Figure 2 This is a flow chart of an embodiment of a tire footprint intelligent recognition method; Figure 3 Photos of tire footprints for loads of 3550Kg, 3000Kg, and 2750Kg; Figure 4 The flowchart of the tire footprint intelligent recognition method is shown as an example; Figure 5 is the tire footprint binary matrix transformation map; Figure 6 This is the fitting diagram of the footprint equation on a 3550Kg load tire; Figure 7 This is the fitting diagram of the footprint equation under a 3550Kg load tire; Figure 8 This is the fitting diagram of the footprint equation on a 3000Kg load tire; Fig. 9 This is the fitting diagram of the footprint equation under a 3000Kg load tire; Fig.10 This is the fitting diagram of the footprint equation on a 2750Kg load tire; Fig.11 This is the fitting diagram of the footprint equation under a 2750Kg load tire; Fig.12 Schematic diagram of an electronic device. DETAILED DESCRIPTION

[0018] The tire footprint intelligent recognition method provided by the present application first extracts the tire footprint image, transforms the tire footprint image to form a footprint image matrix, and obtains the tire quantitative evaluation index by fitting the tire contour equation. The present application can automatically identify and calculate the main evaluation index of the tire, providing data support for tire performance analysis.

[0019] The specific steps of the tire footprint intelligent recognition method will be described in detail below. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details.

[0020] The term "include" used in the present application to describe the tire footprint intelligent recognition method indicates the existence of the described features, entities, steps, operations, elements and / or components, but does not exclude the existence or addition of one or more other features, entities, steps, operations, elements, components and / or their collections. The terms "include", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0021] It should be understood that the "one or more" mentioned in this application refers to one, two or more than two, and the "multiple" mentioned in this application refers to two or more. In the description of this application, unless otherwise specified, " / " means or, for example, A / B can mean A or B. The "and / or" in this article is just a way to describe the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0022] The phrases such as "one embodiment" or "some embodiments" described in the present application mean that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of the present application. Therefore, the phrases such as "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments" etc. that appear in different places in the present application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 The figure is a flow chart of a tire footprint intelligent recognition method in a specific embodiment, the method comprising: S101: Acquire a tire footprint image and pre-process the tire footprint image.

[0025] In some embodiments, a pressurizer is used to obtain tire prints loaded by other devices. The tire prints are photographed or scanned by a camera, a scanner or other image acquisition device to obtain an image of the tire prints.

[0026] In this embodiment, MATLAB image processing software can be used to extract the image, and the extracted content is the complete image information of the tire footprint.

[0027] Optionally, the acquired tire footprint image is converted into an RGB matrix image by image processing software, that is, each pixel in the image is composed of values ​​of three color channels: red, green and blue.

[0028] In this embodiment, the color value of each pixel in the RGB matrix image is converted into a grayscale value through the grayscale conversion function in the MATLAB image processing software, thereby obtaining a grayscale matrix. The grayscale value usually represents the brightness information of the image.

[0029] It can be seen that this embodiment simplifies the image data based on the grayscale matrix, reduces the complexity of subsequent processing, and retains the contour and texture information of the image. It also converts the acquired tire footprint image into a grayscale matrix image suitable for subsequent processing, thereby improving the efficiency and accuracy of image processing.

[0030] S102: Execute an image conversion process to convert the tire footprint image into a two-dimensional binary footprint image matrix based on a preset conversion threshold.

[0031] In some embodiments, image processing software or corresponding programs may be used to perform the tire footprint image conversion process. According to the characteristics of the tire footprint image and the requirements of subsequent processing, a suitable threshold value may be determined through experiments or actual experience.

[0032] In this embodiment, the grayscale value of each pixel in the grayscale matrix can be compared with a preset conversion threshold, and the pixel values ​​greater than the threshold are set to 1, that is, the pixel values ​​are close to white. The pixel values ​​less than the threshold are set to 0, that is, the pixel values ​​are close to black, thereby obtaining a two-dimensional binary impression image matrix.

[0033] It can be seen that the conversion into a two-dimensional binary impression image matrix simplifies the image data, reduces the complexity of subsequent processing, and retains the key information of the image.

[0034] S103: Identify the starting coordinates and the ending coordinates of the tire tread based on the footprint image matrix, divide the tire tread area into multiple areas in the footprint image matrix, and construct a tire contour line equation.

[0035] This embodiment can use the edge detection or contour detection function in the image processing software to identify the starting coordinates and the ending coordinates of the tire tread, providing a basis for the subsequent division of the tread area and the construction of the tire contour line equation.

[0036] Optionally, a plurality of tread areas are divided in the tire footprint image matrix according to the identified starting coordinates and ending coordinates, as well as the shape and texture information of the tire footprint.

[0037] According to the divided tread area and the profile information of the tire footprint, the tire contour equation is constructed using polynomial fitting and curve fitting.

[0038] Based on identifying the starting coordinates and ending coordinates of the tire tread and dividing the tread area, the tire contour equation is constructed to provide accurate basic data for fitting the tire contour equation.

[0039] S104: Fitting the tire contour equation based on a preset algorithm, and determining whether the fitting accuracy meets a fitting accuracy threshold.

[0040] In some embodiments, the tire contour equation is fitted according to the least square method or the genetic algorithm. The fitting method may be polynomial fitting, curve fitting, etc. Based on the fitting algorithm, the fitting parameters may be set, the fitting process may be performed, and the fitting result may be obtained. The fitting accuracy may be evaluated by comparing the difference between the tire contour equation obtained by fitting and the actual tire contour. The smaller the difference, the higher the fitting accuracy.

[0041] This embodiment fits the tire contour equation and evaluates the fitting accuracy.

[0042] S105: If not, adjust the tire profile equation parameters until the fitting accuracy meets the fitting accuracy threshold.

[0043] This embodiment uses a gradient descent method to adjust the parameters of the tire profile equation according to the evaluation result of the fitting accuracy. Optionally, based on the evaluation of the fitting accuracy, it is determined whether a fitting accuracy threshold is met; if not, the parameters are adjusted, the fitting process is re-executed, the fitting accuracy is evaluated again, and the like.

[0044] In this way, by continuously adjusting the parameters, the fitting accuracy of the tire contour equation meets the preset threshold requirements. The accuracy and reliability of the tire contour equation are improved. It can be seen that the present application extracts, processes and recognizes tire footprint images, thereby obtaining an accurate tire contour equation and realizing intelligent recognition of tire footprints.

[0045] In one embodiment of the present invention, Figure 2 As shown, based on step S102, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.

[0046] S1021: Execute an image conversion process to convert the tire footprint image into a two-dimensional binary footprint image matrix.

[0047] S1022: The tire footprint grayscale matrix is ​​converted into a black-and-white two-dimensional binary footprint image matrix by setting a conversion threshold.

[0048] The element value at the tire tread is 1, and the element value at the tire groove is 0.

[0049] S1023: Define the start and end columns of the tread.

[0050] The specific method is: Extract each column vector of the imprint image matrix, determine whether the column vector has tread contact, and based on whether the previous and next column vectors of the current column vector have elements with a value of 1, when a column vector itself has an element with a value of 1 and the previous column vector does not contain an element with a value of 1, determine it as the tread starting column.

[0051] In some specific embodiments, by extracting each column vector of the two-dimensional binary footprint image matrix, it is determined whether the column vector has tread contact, which can be understood as whether there is an element with a value of 1.

[0052] This embodiment determines whether the current column vector is the start and end column of the tread based on whether there are elements with a value of 1 in the column vectors before and after the current column vector. When a column vector itself has an element with a value of 1 and the previous column vector does not contain an element with a value of 1, it is determined to be the start column of the tread.

[0053] When a column vector has an element with a value of 1, and the subsequent column vector does not contain an element with a value of 1, it is determined to be a tread termination column.

[0054] In this embodiment, in order to exclude data pollution (such as stains, etc.) caused by interference items in the column vector, a threshold is set. When the length between the start and end columns is less than the length threshold, the column vector is judged to be an interference item and is excluded, that is, the value of the interference column vector is converted to 0. Then, each tread area is divided according to the obtained start and end columns of each tread.

[0055] S1024: Divide each tread area of ​​the tire footprint image matrix by determining the starting coordinates and the ending coordinates of each tread.

[0056] In this embodiment, in the two-dimensional binary footprint image matrix, each row is traversed to find the first white pixel position of the tire tread as the starting coordinate, and the last white pixel position is found as the ending coordinate. According to the starting coordinate and the ending coordinate, the tire footprint image matrix is ​​divided into multiple tread areas. Each tread area contains a group of continuous white pixels, representing a part of the tire tread.

[0057] S1025: Extract the coordinates of several positions on the boundary of each tread area and construct the nth-order equation of the tire contour line: ; Where, L is the vertical coordinate of the tire track boundary, P is the horizontal coordinate of the tire track boundary, and A n is the coefficient of the equation; S1026: Using the extracted ordinate and abscissa, fitting the n-order equation of the tire contour line starting from the first-order equation, and judging whether the fitting accuracy meets a preset fitting accuracy threshold according to the fitted equation after each fitting.

[0058] If the fitting accuracy meets the preset fitting accuracy threshold, the next-order fitting of the tire contour line n-order equation is stopped, and the equation obtained by the current fitting is the curve equation of the tire contour line.

[0059] If the fitting accuracy does not meet the preset fitting accuracy threshold, the extracted coordinates are used to fit the tire contour line n-order equation to the next order until the fitting accuracy meets the preset fitting accuracy threshold. The equation obtained by the current fitting is the curve equation of the tire contour line.

[0060] In this embodiment, starting from the first-order equation, the tire contour line is fitted using the least square method or other fitting algorithms. The mean square error MSE between the fitted equation and the actual coordinate points is calculated. It is determined whether the error is less than a preset fitting accuracy threshold. If the error meets the threshold requirement, the fitting is stopped, and the current equation is the curve equation of the tire contour line. If the error does not meet the threshold requirement, the order of the equation is increased, a higher-order equation is used for fitting, and the above steps are repeated until the fitting accuracy threshold is met.

[0061] As an example, in the tire contour line equation fitting step, the horizontal coordinates and vertical coordinates of the 25% point, 50% point, and 75% point positions of the tread area are extracted, and the tire contour line curve is obtained by fitting based on the horizontal coordinates and the vertical coordinates.

[0062] As an example of the present application, the horizontal coordinates and vertical coordinates of the 25% point, 50% point, and 75% point positions of the tread area are extracted. The first-order equation of the tire contour is constructed using these coordinate points, and the fitting error is calculated. If the error does not meet the threshold requirement, a second-order equation is constructed and the fitting error is repeatedly calculated. And so on, until the equation order that meets the fitting accuracy threshold is found.

[0063] In this embodiment, when performing quantitative evaluation on a tire, the tire quantitative evaluation index may be substituted into the tire contour curve equation obtained by fitting to calculate the tire quantitative evaluation index.

[0064] Optionally, after obtaining the curve equation of the tire contour line, the tire width, tire diameter, and tire wear degree can be calculated as tire quantitative evaluation indicators. The tire is quantitatively evaluated based on the calculation results.

[0065] On the basis of the above embodiments, in order to further improve the reliability of the tire footprint intelligent recognition method provided in the above embodiments, the following is a specific implementation method for implementing the tire footprint intelligent recognition method. In one embodiment, Figure 3 As shown, photos of tire footprints of a certain specification tire under a tire pressure of 1000KPa and loads of 3550Kg, 3000Kg and 2750Kg respectively.

[0066] Figure 3 (a) is a photo of the tire footprint with a load of 3550 kg. Figure 3 (b) is a photo of the tire footprint with a load of 3000 kg. Figure 3(c) is a photo of the tire footprint with a load of 2750 kg.

[0067] Use MATLAB to extract and recognize images and implement the intelligent tire mark recognition method. The specific recognition process is as follows: Figure 4 shown.

[0068] In this embodiment, the tire rectangular ratio and the shoulder settlement are selected as the tire quantitative evaluation indicators for calculation, and the calculation steps of other evaluation indicators are similar.

[0069] The tire print photo was extracted through MATLAB to obtain the print gray matrix. The conversion threshold was set to 0.9, and the gray value below 0.9 was converted to black, and the tire print binary matrix image was obtained, as shown in Figure 5 shown.

[0070] Figure 5 (a) is the binary matrix transformation diagram of tire footprint with a load of 3550 kg. Figure 5 (b) is the binary matrix transformation diagram of tire footprint with a load of 3000Kg. Figure 5 (c) is the binary matrix transformation diagram of tire footprint with a load of 2750 kg.

[0071] This embodiment extracts each column vector of the tire footprint binary matrix to determine whether the column vector has tread contact, that is, whether there is an element with a value of 1, and determines whether the column vector is the start and end columns of the tread based on whether there are elements with a value of 1 in the column vectors before and after it. In order to eliminate data pollution caused by interference items (such as stains, etc.), the threshold is set to 5. When the number of elements between the start and end columns is less than 5, it is determined to be an interference item, and the value of the interference element is converted to 0.

[0072] In this embodiment, each tread area is divided according to the obtained start and end columns of each tread, and the tread area can be formed according to the 25% point, 50% point, and 75% point of the tire profile.

[0073] By extracting the horizontal and vertical coordinates of the start, middle and end of each tread area, and fitting the coordinates, the tire contour equation and curve are obtained. The coordinate points and fitting curve are shown in Figures 6 to 11 As shown. The fitting accuracy threshold is set to 96%. Define L i is the ordinate of the upper boundary of the tire footprint, H i is the ordinate of the lower boundary of the tire track, and P is the abscissa of the boundary of the tire track.

[0074] The fitting equation is as follows: 3550Kg load:

[0075] 3000Kg load:

[0076] 2750Kg load:

[0077] This embodiment takes two tire evaluation indicators, namely, tire rectangular ratio and tire shoulder settlement, as examples. The rectangular ratio is mainly used to evaluate the driving performance and steering performance of the tire; and the tire shoulder settlement is mainly used to determine whether the tire will be abnormally worn.

[0078] The calculation formula of the rectangular ratio of this embodiment is:

[0079] Among them, Q is the rectangular ratio, Y 1 Y is the longitudinal length of the tire footprint at 12.5% ​​of the transverse length of the tire footprint. 2 It is the longitudinal length of the tire footprint at 50% of the lateral length of the tire footprint.

[0080]

[0081] Among them, P1 is the abscissa at 12.5% ​​of the transverse length of the tire footprint, P2 is the abscissa at 50% of the transverse length of the tire footprint, and E is the longest longitudinal length of the tire footprint.

[0082] The calculation formula for tire shoulder settlement is:

[0083] Among them, S is the shoulder settlement rate, Y 3 Y is the longitudinal length of the tire footprint at 2.5% of the transverse length of the tire footprint, 4 It is the longitudinal length of the tire footprint at 12.5% ​​of the transverse length of the tire footprint.

[0084]

[0085] Among them, P3 is the horizontal coordinate of 2.5% of the horizontal length of the tire footprint, and P4 is the horizontal coordinate of 12.5% ​​of the horizontal length of the tire footprint.

[0086] Finally, the rectangular ratio of the tire under three loads is obtained, as shown in Table 1.

[0087] Table 1

[0088] The shoulder settlement rate of the tire is shown in Table 2.

[0089] Table 2

[0090] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0091] The following is an embodiment of a tire footprint intelligent recognition device provided in an embodiment of the present disclosure. The device and the tire footprint intelligent recognition method of the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the tire footprint intelligent recognition device, please refer to the embodiment of the above-mentioned tire footprint intelligent recognition method.

[0092] A mobile terminal implementing various embodiments of the present invention will now be described with reference to the accompanying drawings. In the subsequent description, suffixes such as "module", "component" or "unit" used to represent elements are used only to facilitate the description of the embodiments of the present invention and have no specific meaning in themselves. Therefore, "module" and "component" can be used interchangeably.

[0093] The device includes: The image acquisition module is used to acquire the tire print image and pre-process the tire print image.

[0094] The image conversion module is used to execute the image conversion process and convert the tire footprint image into a two-dimensional binary footprint image matrix based on a preset conversion threshold.

[0095] The tire profile construction module identifies the starting coordinates and the ending coordinates of the tire tread based on the imprint image matrix, divides a plurality of tread areas in the imprint image matrix, and constructs a tire contour line equation.

[0096] The tire profile fitting module fits the tire profile equation based on a preset algorithm and determines whether the fitting accuracy meets the fitting accuracy threshold; if not, the tire profile equation parameters are adjusted until the fitting accuracy meets the fitting accuracy threshold.

[0097] like Fig.12 As shown, the present application also provides an electronic device, including a memory 502, a processor 501, and a computer program stored in the memory and executable on the processor, wherein the processor 501 implements the steps of the tire footprint intelligent recognition method when executing the program.

[0098] Those skilled in the art will appreciate that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown, or combine certain components, or arrange different components. In the embodiments of the present invention, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0099] In the embodiment of the present application, the processor 501 can be implemented by using at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an implementation can be implemented in a controller. For software implementation, implementations such as processes or functions can be implemented with separate software modules that allow execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.

[0100] The memory 502 includes a program storage area and a data storage area, wherein the program storage area can store an operating system, etc. The memory 502 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0101] The display module 503 may also be involved as needed to display the operation process of the tire footprint intelligent recognition method, the footprint image matrix and the tire contour equation, etc. In addition, the electronic device includes some functional modules not shown, which will not be described in detail here.

[0102] The present application also provides a storage medium, in which a program product capable of implementing the tire footprint intelligent recognition method described above in this specification is stored. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the "Exemplary Method" section of this specification according to various exemplary implementations of the present disclosure.

[0103] The storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0104] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tire footprint intelligent recognition method, characterized in that: Methods include: S101: Acquire a tire footprint image and pre-process the tire footprint image; S102: executing an image conversion process to convert the tire footprint image into a two-dimensional binary footprint image matrix based on a preset conversion threshold; S103: identifying the starting coordinates and the ending coordinates of the tire tread based on the imprint image matrix, dividing a plurality of tread areas in the imprint image matrix, and constructing a tire contour line equation; S104: Fitting the tire contour equation based on a preset algorithm, and determining whether the fitting accuracy meets a fitting accuracy threshold; S105: If not, adjust the tire profile equation parameters until the fitting accuracy meets the fitting accuracy threshold.

2. The tire footprint intelligent recognition method according to claim 1, characterized in that: In step S103, the abscissa and ordinate of each tread area are obtained, and a tire contour curve is obtained by fitting according to the abscissa and ordinate.

3. The tire footprint intelligent recognition method according to claim 2, characterized in that: Extracting coordinates of several positions on the boundary of each tread area; Construct the nth-order equation of tire profile; The nth-order equation of the tire contour is: ； Where L is the vertical coordinate of the tire track boundary, P is the horizontal coordinate of the tire track boundary, and A n is the coefficient of the equation.

4. The tire footprint intelligent recognition method according to claim 3, characterized in that: In the method, the nth-order equation of the tire contour line is fitted starting from the first-order equation; The tire contour line equation obtained by each fitting is calculated to calculate the fitting accuracy, and it is determined whether the fitting accuracy meets the fitting accuracy threshold.

5. The tire footprint intelligent recognition method according to claim 1, characterized in that: S102: executing an image conversion process to convert the tire footprint image into a two-dimensional binary footprint image matrix; By setting the conversion threshold, the tire footprint gray matrix is ​​converted into a two-dimensional binary matrix of black and white; The element value at the tire tread is 1, and the element value at the tire groove is 0.

6. The tire footprint intelligent recognition method according to claim 5, characterized in that: Step S103 also includes: defining the start and end columns and the end column of the tread; Extract each column vector of the impression image matrix, determine whether the column vector has tread contact, and determine whether the column vector has an element with a value of 1 in the column vectors before and after the current column vector, when a column vector itself has an element with a value of 1 and the previous column vector does not contain an element with a value of 1, determine that it is the tread starting column; When a column vector has an element with a value of 1, and the subsequent column vector does not contain an element with a value of 1, it is determined to be a tread termination column.

7. The tire footprint intelligent recognition method according to claim 1, characterized in that: In step S101, the tire footprint image is preprocessed in a manner including: configuring the tire footprint image as an RGB matrix image, and converting it into a grayscale matrix.

8. A tire footprint intelligent recognition device, characterized in that: The device is used to implement the tire footprint intelligent recognition method as described in any one of claims 1 to 7; The device includes: An image acquisition module, used for acquiring a tire print image and preprocessing the tire print image; An image conversion module, used for executing an image conversion process, and converting the tire footprint image into a two-dimensional binary footprint image matrix based on a preset conversion threshold; A tire profile construction module, which identifies the starting coordinates and the ending coordinates of the tire tread based on the imprint image matrix, divides a plurality of tread areas in the imprint image matrix, and constructs a tire profile equation; The tire profile fitting module fits the tire profile equation based on a preset algorithm and determines whether the fitting accuracy meets the fitting accuracy threshold; if not, the tire profile equation parameters are adjusted until the fitting accuracy meets the fitting accuracy threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the tire footprint intelligent recognition method as described in any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the tire footprint intelligent recognition method as claimed in any one of claims 1 to 7 are implemented.

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