Tire damage evaluation device, tire damage evaluation method, and tire damage evaluation program

Abstract

To quantify and evaluate the damage condition in the surface region of a tire tread.SOLUTION: This tire damage evaluation device comprises a processing unit that acquires a tread image obtained by imaging a tread section of a tire, and quantifies the damage state of a surface region of the tread section on the basis of the tread image.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a tire damage assessment device, a tire damage assessment method, and a tire damage assessment program. [Background technology]

[0002] Conventionally, the appearance and damage of tires have been visually inspected by humans, and evaluation of the tire surface condition from tire images has been adopted in tire appearance inspections, for example (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-42988 Summary of the Invention [Problem to be solved by the invention]

[0004] However, inspection methods in which humans visually inspect the appearance of tires are heavily influenced by experience and know-how, lacking objectivity and limiting to qualitative evaluation. Furthermore, conventional image analysis methods can only detect whether a tire is good or bad on the tire production line, and cannot quantitatively evaluate the surface condition of the tread of a tire after it has started to be used.

[0005] Therefore, an object of the present disclosure, which has been made with these points in mind, is to provide a tire damage evaluation device, a tire damage evaluation method, and a tire damage evaluation program that are capable of quantifying and evaluating the surface condition of a tire tread portion. [Means for solving the problem]

[0006] (1) A tire damage assessment device according to one aspect of the present disclosure includes a processing unit that acquires a tread image of a tire tread portion and quantifies a damage state of a surface region of the tread portion based on the tread image. The tire damage assessment device having such a configuration makes it possible to quantify and assess the damage state of the surface region of the tire tread portion.

[0007] (2) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described in (1) above, wherein the tread portion is preferably configured to include a plurality of layers arranged in the radial direction of the tire, and the processing unit preferably generates a segmented image in which the surface region of the tread portion included in the tread image is segmented by the layers exposed in the surface region. A tire damage assessment device having such a configuration can visualize the state of damage caused by wear of the tread portion of the tire.

[0008] (3) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described in (2) above, wherein the multiple layers preferably include an outer layer rubber constituting the tread surface of the tread portion, a layer of metal cords disposed radially inward of the outer layer rubber, and an inner layer rubber located between the outer layer rubber and the layer of metal cords. A tire damage assessment device having such a configuration can be suitably applied to a tire whose tread portion is composed of an outer layer rubber, an inner layer rubber, and layers of metal cords.

[0009] (4) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described in (2) or (3) above, and the processing unit preferably executes a process of inputting the tread images of a plurality of sample tires to a first trained model trained using first teacher data that inputs a tread image of the tire and outputs a segment image in which the surface area of ​​the sample tire included in the tread image is segmented by layers exposed on the surface, and outputting the segment image of the tire. A tire damage assessment device having such a configuration can generate a segment image by machine learning. Furthermore, the tire damage assessment device can improve the accuracy of segmenting the surface area of ​​the tire by increasing the number of sample tires.

[0010] (5) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described in any one of (2) to (4) above, wherein the processing unit preferably divides the section image of the tire into a plurality of rectangular regions extending in the width direction of the tire, aligns the positions of the plurality of rectangular regions so that the positions of the tread portion in the rectangular regions in the width direction coincide, and generates a histogram by integrating the pixels of the plurality of rectangular regions for at least any of the layers exposed in the surface region for each position in the width direction. A tire damage assessment device having such a configuration makes it possible to grasp the positions of major damage in the tire width direction and the magnitude of the damage.

[0011] (6) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device according to any one of (1) to (5) above, and the processing unit is preferably configured to extract from the tread image at least any of a groove portion indicating a groove in the tread portion, a local damage portion indicating local damage in the surface region of the tread portion, and a repaired portion indicating a location where damage that occurred in the past in the tire has been repaired. A tire damage assessment device having such a configuration makes it possible to extract the groove portion, the local damage portion, and the repaired portion in the surface region of the tread portion.

[0012] (7) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described in any one of (1) to (6) above, and the processing unit preferably executes a process of inputting the tread image of the tire to a second trained model trained using second teacher data that inputs tread images of a plurality of sample tires and outputs an image of a local damage portion showing local damage in the tread portion of the sample tire included in the tread image, and outputting the image of the local damage portion of the tire. A tire damage assessment device having such a configuration can extract a local damage portion on a tire surface by machine learning. Furthermore, the tire damage assessment device can improve the accuracy of extracting a local damage portion by increasing the number of sample tires.

[0013] (8) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described above in (7), wherein the processing unit preferably calculates a ratio of an area of ​​the localized damage area to an area of ​​the surface area of ​​the tread portion. A tire damage assessment device having such a configuration can quantitatively assess the size of the localized damage area.

[0014] (9) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device according to (7) or (8) above, wherein the processing unit preferably calculates a center of gravity position of the area of ​​the localized damage portion. A tire damage assessment device having such a configuration can quantify and grasp the position of the localized damage portion.

[0015] (10) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device according to any one of (2) to (5) above, wherein the processing unit may quantify a damage state of the surface region of the tire in a portion where the outermost layer of the plurality of layers is exposed based on the tread image. A tire damage assessment device having such a configuration can quantitatively assess a damage state due to scratches or the like that is difficult to determine by visual inspection of the surface region of the tire, unlike wear and chipping of the surface region of the tread portion.

[0016] (11) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described above in (10), wherein the processing unit preferably extracts an image of a region including the surface region of the tread portion from the tread image, divides the image into images of a plurality of sub-regions, and quantifies a damage state of the surface region for each of the images of the sub-regions. A tire damage assessment device having such a configuration can assess the damage state of the surface region according to a position of the tire surface region in the tread portion.

[0017] (12) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described in (10) or (11) above, and the processing unit preferably executes a process of inputting the tread image of the tire to a third trained model trained using third teacher data that inputs tread images of a plurality of sample tires and outputs information quantifying the damage state of the surface region of a portion where the outermost layer of the tire is exposed among the plurality of layers included in the tread image, and outputting the information quantifying the damage state of the surface region. According to the tire damage assessment device having such a configuration, it is possible to assess the damage state due to scratches or the like in a relatively less damaged surface region of the tire by machine learning. Furthermore, the tire damage assessment device can improve the accuracy of assessing the damage state by increasing the number of sample tires.

[0018] (13) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device described above in (12), and the damage state of the surface region in the third teacher data is preferably quantified based on at least one of the number of scratches contained in the surface region and the depth of the scratches. A tire damage assessment device having such a configuration makes it possible to objectively assess the damage state of the surface region by setting a standard for at least one of the number of scratches and the depth of the scratches.

[0019] (14) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device according to any one of (1) to (13) above, wherein the damage state includes the state of chips and scratches on the tire, and the processing unit preferably estimates the wear characteristics of the tire based on information on the quantified state of the chips and scratches. A tire damage assessment device having such a configuration can estimate the wear characteristics of a tire by monitoring the progress of chips and scratches that occur in the tread portion of the tire. By accumulating such a relationship between chips and scratches on the tire and the progress of tire wear, it can be utilized in tire design.

[0020] (15) A tire damage assessment device according to an embodiment of the present disclosure is the tire damage assessment device according to any one of (1) to (14) above, and preferably includes at least one of a display unit that displays quantified information on the damage state of the surface region of the tread portion, and a communication unit that transmits the information to an external device. A tire damage assessment device having such a configuration can provide a user with the damage state of the surface region of the tread portion in a manner that is easy for the user to use.

[0021] (16) A tire damage evaluation method according to one aspect of the present disclosure is a tire damage evaluation method executed by an information processing device, and includes acquiring a tread image of a tire tread portion and quantifying a damage state of a surface region of the tread portion based on the tread image. The tire damage evaluation method having such a configuration makes it possible to quantify and evaluate the damage state of the surface region of the tire tread portion.

[0022] (17) A tire damage evaluation program according to an aspect of the present disclosure causes a computer to execute a process of acquiring a tread image of a tire tread portion and a process of quantifying a damage state of a surface region of the tread portion based on the tread image. The tire damage evaluation program having such a configuration makes it possible to quantify and evaluate the damage state of the surface region of the tire tread portion. Effect of the Invention

[0023] According to the present disclosure, it is possible to quantify and evaluate the damage state of the surface area of ​​the tread portion of a tire. [Brief description of the drawings]

[0024] [Figure 1] 1 is a block diagram showing a schematic configuration of a tire damage assessment system including a tire damage assessment device according to an embodiment of the present disclosure. [Diagram 2] 2 is a cross-sectional view showing an example of a tire to be evaluated by the tire damage evaluation system of FIG. 1. [Diagram 3] FIG. 1 is a flow diagram showing a method for generating a first trained model that segments the surface area of ​​a tread portion based on multiple sample tire images. [Figure 4] FIG. 2 is a diagram showing an example of an image of a tread portion of a sample tire. [Diagram 5] FIG. 13 is a diagram showing an example of a section image generated from an image of a tread portion. [Figure 6] FIG. 13 is a diagram illustrating a method for generating a histogram from a segmented image. [Figure 7] 1 is a diagram showing an example of a groove portion, a locally damaged portion, and a repaired portion of a tire. [Figure 8] FIG. 11 is a flow diagram showing a method for generating a third trained model that quantifies the damage level of the surface region of the tread portion based on multiple sample tire images. [Figure 9] FIG. 2 is a diagram showing an example of a tire tread image captured by an imaging unit. [Figure 10] 10 is a diagram showing an example in which a process for determining a rectangular area is executed on the tread image of FIG. 9. FIG. [Figure 11] FIG. 11 is a diagram showing an example of masking a damaged area in the rectangular image of FIG. 10 and dividing it into sub-areas. [Figure 12] FIG. 12 is a diagram showing an image in which a numerical value that quantifies the damage state is assigned to each sub-region of the tread image in FIG. 11. [Figure 13] FIG. 2 is a flow diagram illustrating a tire damage assessment method according to an embodiment of the present disclosure. [Figure 14] FIG. 2 is a block diagram showing a schematic configuration of a tire damage assessment system according to another embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] Tires used in vehicles operating in special environments such as mines are susceptible to damage, including chipping and scratches, on the surface of the tread when the vehicle travels on roads other than paved roads. There is concern that small damage to a tire may cause the tire to wear out rapidly. By quantitatively understanding the relationship between the damage that occurs to the tire and the progression of tire wear, it is expected that the relationship between tire damage and tread patterns can be clarified and used in tire design. It is also expected that the appropriate timing for tire rotation or replacement can be estimated from the state of tire damage, and information can be provided to tire users.

[0026] In this application, "damage" means that a normal state is at least partially lost. "Damage" to the tire surface includes wear, chips, scratches, and the like on the tire surface. Wear on the tire surface means that the tire surface has been worn down and scraped off due to friction with the ground. Chips on the tire surface mean that part of the tire surface is missing. Scratches on the tire surface mean that the tire has scratches, cuts, and the like. In this application, "localized damage" means chips, gouges, scratches, and the like that occur in a relatively small area, and whose location and range can be identified by an operator from an image of the tire surface.

[0027] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0028] (Tire Damage Assessment System) As shown in Fig. 1, a tire damage assessment system 1 according to an embodiment of the present disclosure includes a tire damage assessment device 10 and an imaging device 20. Fig. 2 shows a cross-sectional view of an example of a tire 30 to be assessed by the tire damage assessment system 1. In the tire damage assessment system 1, the tire damage assessment device 10 analyzes and quantifies a damage state of a surface region of the tread portion 35 based on an image of the tread portion 35 of the tire 30 captured by the imaging device 20. The tread portion 35 refers to a portion of the tire 30 that directly contacts the road. Hereinafter, an image of the tread portion 35 is referred to as a tread image.

[0029] The tire damage assessment device 10 is an information processing device, and is, for example, a computer such as a server computer, a workstation, or a PC (Personal Computer). The tire damage assessment device 10 may be installed at a user's site or may be located at a remote location via a network. The components and functions of the tire damage assessment device 10 described below may be distributed and located on multiple different hardware devices. Furthermore, the tire damage assessment device 10 may be a small information processing device such as a portable information terminal or a smartphone. In the embodiment described below, the tire damage assessment device 10 is described as being a stationary computer. The tire damage assessment device 10 acquires a tread image from an imaging device 20 and executes processing including image processing.

[0030] The imaging device 20 includes an optical system and an imaging element. The imaging element includes a CCD image sensor (Charge-Coupled Device Image Sensor) and a CMOS image sensor (Complementary MOS Image Sensor). The imaging element converts an image formed on a light receiving surface by the optical system into an electric signal. In the following embodiment, the imaging device 20 includes a set of optical systems and an imaging element, and images the tread portion 35 of the tire 30 from one direction in a state where the tire 30 is removed from the vehicle and laid down. However, the imaging device 20 may include multiple sets of optical systems and imaging elements, and may be configured to image the tread portion 35 from multiple directions. The imaging device 20 may be configured to image the tire 30 from all directions of 360 degrees along the circumferential direction of the tire 30. The imaging device 20 may be a digital camera or a portable information terminal equipped with a camera. The imaging device 20 may be a device provided exclusively for the tire damage evaluation system 1.

[0031] (Configuration of tire damage evaluation device) In one embodiment, the tire damage assessment device 10 includes a communication unit 11, an input unit 12, an output unit 13, a processing unit 14, and a memory unit 15.

[0032] The communication unit 11 provides a communication function for communicating with devices external to the tire damage assessment device 10. The communication unit 11 includes a communication module that connects to a communication network such as the Internet. The communication unit 11 may include a communication module that supports mobile communication standards such as 4G (4th Generation) and 5G (5th Generation). The communication unit 11 may include a communication module that supports a wired LAN standard (1000BASE-T as an example). The communication unit 11 may include a communication module that supports a wireless LAN standard (IEEE802.11 as an example). The communication unit 11 may include a communication module that supports short-range communication such as Bluetooth and BLE.

[0033] The communication unit 11 may be configured to be connected to the imaging device 20 via wired or wireless communication and to receive tread images captured by the imaging device 20. The communication unit 11 may be configured to be capable of transmitting and receiving information to and from an information processing device for machine learning (described later) via a network such as the Internet.

[0034] The input unit 12 includes one or more input interfaces for the tire damage assessment device 10. The input interfaces include, for example, a physical key, a capacitive key, a pointing device, a touch screen integrated with a display, a microphone, and a camera. The input unit 12 includes an input interface that acquires information from a portable storage medium such as a memory card. The tire damage assessment device 10 may acquire a tread image captured by the imaging device 20 via the storage medium.

[0035] The output unit 13 includes one or more output interfaces. Examples of the output interfaces include a display, a printer, and a speaker. Examples of the display that can be used include an LCD (Liquid Crystal Display) or an organic EL (Electro Luminescence) display. That is, the output unit 13 includes a display unit. The input unit 12 may include an output interface that writes information to a portable storage medium such as a memory card. The output unit 13 can output information obtained by the operation of the tire damage assessment device 10 to a user.

[0036] The processing unit 14 includes one or more processors. The processor may be a general-purpose processor such as a CPU (Central Processing Unit) that executes a program that defines a control procedure, or may be a dedicated processor specialized for a specific process. The processing unit 14 performs arithmetic processing for executing each function of the tire damage assessment device 10, and controls each component of the tire damage assessment device 10.

[0037] The processing unit 14 may be capable of performing various types of image processing on the tread image acquired from the imaging device 20. The processing unit 14 can evaluate the damage state of the surface region of the tread portion 35 by using at least one of first to third trained models stored in the storage unit 15 described later.

[0038] The storage unit 15 includes one or more memories. The memories are, for example, semiconductor memories, magnetic memories, optical memories, etc., but are not limited to these and may be any memories. The semiconductor memories may include volatile memories such as RAM (Random Access Memory), and non-volatile memories such as ROM (Read Only Memory) and flash memories. The magnetic memories may include, for example, hard disks and magnetic tapes. The optical memories may include, for example, CDs (Compact Discs), DVDs (Digital Versatile Discs), and BDs (Blu-ray (registered trademark) Discs).

[0039] The storage unit 15 stores information necessary for the processing unit 14 of the tire damage assessment device 10 to perform processing, information generated as a result of the processing by the processing unit 14, etc. For example, the storage unit 15 stores a tread image captured by the imaging device 20, an image obtained by processing the tread image, and information on the result of evaluating the damage state of the surface region of the tire 30 from the tread image. The storage unit 15 may store a tire damage assessment program executed by the processing unit 14 of the tire damage assessment device 10, and the first to third trained models. The tire damage assessment program may be provided in a form stored in a non-transitory storage medium such as a CD, a DVD, or a Universal Serial Bus (USB) memory.

[0040] (Example of tire configuration to be evaluated) An example of a tire 30 to be evaluated by the tire damage evaluation device 10 will be described with reference to the cross-sectional view of Fig. 2. In one embodiment, the tire 30 is a tire mounted on a vehicle used in special environments such as mines, and a special vehicle for work and construction. Being used in a harsh environment makes the tire 30 susceptible to various types of damage. Furthermore, the tire 30 is often used to the end of its life despite being damaged.

[0041] 2 has a pair of bead portions 31 and a pair of sidewall portions 32, a tread portion 35 connected to both sidewall portions 32 and having an outer layer rubber 33 and an inner layer rubber 34 in this order from the outside in the tire radial direction, a carcass 36 extending in a toroidal shape between the pair of bead portions 31 to reinforce the bead portions 31, the sidewall portions 32 and the tread portion 35, and a metal cord layer 37 arranged on the outside in the tire radial direction of the crown portion of the carcass 36 and on the inside in the tire radial direction of the inner layer rubber 34. That is, the outer layer rubber 33, the inner layer rubber 34 and the metal cord layer 37 are arranged in this order from the outside in the radial direction of the tire 30.

[0042] Here, the outer layer rubber 33 of the tire 30 shown in Fig. 2 is also generally called a "cap rubber". The inner layer rubber 34 is also generally called a "base rubber". Also, the metal cord layer 37 is also generally called a "belt".

[0043] The carcass 36 of the tire 30 shown in Fig. 2 is composed of one carcass ply made of a plurality of parallel-arranged cords covered with a coating rubber. The carcass 36 also comprises a main body portion extending in a toroidal shape between the bead cores 38 embedded in the bead portions 31, and turn-up portions wound up radially outward from the inner side toward the outer side in the tire width direction around each bead core 38. The number and structure of the plies of the carcass 36 are not limited to this.

[0044] Moreover, the metal cord layer (belt) 37 of the tire 30 shown in Fig. 2 is composed of two belt layers. The number of belt layers constituting the metal cord layer 37 is not limited to this, and the number of belt layers may be three or more. Here, the belt layer is usually composed of a rubber-coated layer of metal cords (preferably steel cords) extending at an angle to the tire equatorial plane. The two belt layers are laminated to constitute the metal cord layer 37 such that the metal cords constituting the belt layers cross each other with the tire equatorial plane in between.

[0045] The tire 30 includes, in a tread portion 35, an outer layer rubber 33 constituting the tread surface of the tread portion 35, a metal cord layer 37 disposed on the tire radially inner side of the outer layer rubber 33, and an inner layer rubber 34 positioned between the outer layer rubber 33 and the metal cord layer 37. A tread pattern is formed on the surface of the outer layer rubber 33. The rubber layer of the tread portion 35 of the tire 30 is not limited to a two-layer structure of the outer layer rubber 33 and the inner layer rubber 34, but may have a one-layer structure or a layer structure of three or more layers.

[0046] (Method of dividing the tire surface area) The processing unit 14 of the tire damage assessment device 10 acquires a tread image including an image of the tread portion 35 of the tire 30 captured by the imaging device 20 via the communication unit 11 or the input unit 12. The processing unit 14 generates a divided image in which the surface region of the tread portion 35 included in the acquired tread image is divided by layers exposed in the surface region. The processing unit 14 may divide the surface region into a plurality of regions including a first region 41 covered by the outer rubber layer 33, a second region 42 in which the inner rubber layer 34 is exposed, and a third region 43 in which the metal cord layer 37 is exposed (see FIGS. 4 and 5).

[0047] Various image processing methods can be used to generate the segment image. In one embodiment, machine learning can be used to generate the segment image. The processing unit 14 may be configured to input a tread image to a first trained model that has been machine-learned by an information processing device for machine learning, and output a segment image of the tire 30. The information processing device for machine learning may be the same computer as the tire damage assessment device 10, or may be a different computer. In the latter case, the information processing device for machine learning may be configured to be able to communicate with the tire damage assessment device 10.

[0048] Based on the flow diagram in Figure 3, we will explain how to generate the first trained model.

[0049] First, an operator takes tread images of multiple tires (step S101). The multiple tires that have been photographed are called sample tires. Sample tires may include tires that are still in use and tires that have reached the end of their life. Many images of sample tires under different conditions are taken and stored.

[0050] The surface area of ​​the tread portion 35 of the captured tread image of each sample tire is divided by the layer exposed on the surface area on the screen of a work computer by an operator (step S102). The operator may be a specialized engineer who can determine whether the exposed area is the outer rubber layer 33, the inner rubber layer 34, or the metal cord layer 37 by looking at the image of the sample tire.

[0051] Here, the fact that a layer other than the outermost layer of the tire 30 is exposed on the tire surface means that the upper layer has been worn away by the running of the vehicle. Therefore, for example, the area where the inner rubber layer 34 or the metal cord layer 37 is exposed is included in the damaged area. It can be said that the area where the metal cord layer 37 is exposed is more severely damaged than the area where the inner rubber layer 34 is exposed.

[0052] FIG. 4 shows an example of a tread image 40 of a sample tire and a division of a surface area on the tread image 40 by an operator. As shown in FIG. 4, the operator uses a GUI (Graphical User Interface) of a work computer to sequentially specify boundaries where different layers are exposed on an image of a tread portion 35 of a sample tire. The boundaries where different layers are exposed include, for example, a boundary between a first region 41 where the outer layer rubber 33 is located on the outermost surface and a second region 42 where the inner layer rubber 34 is exposed, and a boundary between the second region 42 and a third region 43 where the metal cord layer 37 is exposed. The operator specifies boundaries to surround areas where damage has occurred, for example, the second region 42 and the third region 43, respectively, to generate a division image in which the surface area of ​​the tread image 40 is divided. In this way, the first region 41, the second region 42, and the third region 43 are divided on the tread image 40 of the sample tire. The working computer may be any computer, and may be the same computer as the tire damage assessment device 10 and / or the information processing device for machine learning.

[0053] Next, first training data for machine learning is generated (step S103) by inputting the tread image 40 of the sample tire and outputting data of segmented images obtained by segmenting the surface area of ​​the sample tire included in the tread image 40. The first training data includes a large amount of data generated from a large number of sample tires.

[0054] The information processing device for machine learning performs machine learning using the first teacher data generated in step S103 (step S104). The information processing device for machine learning includes a function approximator capable of learning the input / output relationship, and generates a first trained model by learning the first teacher data (step S105). The first trained model is also called a region segmentation model. The function approximator can perform machine learning using a known machine learning algorithm such as a neural network or deep learning. When the tread image 40 of the tire 30 is input, the first trained model outputs a segmented image that segments the surface area of ​​the tread portion 35 of the tire 30. By learning data of many tread images 40, the accuracy of machine learning can be improved.

[0055] 3 is stored in the storage unit 15 of the tire damage assessment device 10. The processing unit 14 inputs the tread image 40 of the tire 30 captured by the imaging device 20 into the first trained model read from the storage unit 15, and generates a partition image by outputting a partition image that partitions the surface area of ​​the tread portion 35 of the tire 30. The partition image may be displayed on a display, which is the output unit 13.

[0056] FIG. 5 shows an example of a segmented image obtained by segmenting the surface area generated from the image of the tread portion 35. FIG. 5 shows a first region 41 in which the outer rubber layer 33 is exposed in the surface area, a second region 42 in which the inner rubber layer 34 is exposed, and a third region 43 in which the metal cord layer 37 is exposed. Each region may be displayed with a different color. From the size of each region, the user can visually grasp the degree of damage to the surface area of ​​the tire 30 quantitatively to some extent. The processing unit 14 may digitize the area ratio, which is the ratio of the area of ​​each of the first region 41, the second region 42, and the third region 43 to the entire surface area of ​​the tread portion 35, as the ratio of pixels in the image.

[0057] The processing unit 14 can further process the segmented image to grasp the positions in the width direction of the tire 30 where the inner rubber layer 34 and the metal cord layer 37 are largely exposed. For this reason, as shown in Fig. 6, the processing unit 14 divides the segmented image of the tire 30 into a plurality of rectangular regions 45 that are narrow in the radial direction of the tire 30 and extend long in the width direction of the tire 30. The term "rectangular" can be rephrased as "band-like" or "strip-like".

[0058] The processing unit 14 aligns the positions of the strip-shaped regions 45 so that the positions of the tread portions 35 in the strip-shaped regions 45 in the width direction of the tire 30 are consistent. FIG. 6 shows the strip-shaped regions 45 before alignment. The tread image 40 of the tire 30 may be shifted from the outer position of the image center depending on the angle at which it is captured. Also, the tire 30 may appear narrower on the outer side than in the center of the image. The processing unit 14 aligns the center positions of the strip-shaped regions 45 and aligns the lengths of the strip-shaped regions 45 in the width direction of the tire 30, i.e., the vertical direction in FIG. 6. This operation aligns the positions of the strip-shaped regions 45 in the width direction of the tire 30. This operation is called normalization.

[0059] Next, the processing unit 14 generates a histogram by integrating pixels corresponding to one or more of the first region 41, the second region 42, and the third region 43 for each position in the width direction of the tire 30 on the image formed by arranging the normalized strip-shaped regions 45. In the example of FIG. 6, the histograms for the second region 42 and the third region 43 are shown on the right side. This allows the user to grasp the positions and sizes of the inner layer rubber 34 and the metal cord layer 37 that are mostly exposed in the width direction of the tire 30. In other words, the user can grasp the positions and sizes of the regions that are extensively damaged due to wear and the like in the surface region of the tread portion 35 and the damage.

[0060] (Extraction of localized damage on tire surface) In the above explanation using Figures 3 to 6, the tire damage assessment device 10 generates segmented images that segment the exposed areas of the outer rubber layer 33, inner rubber layer 34, and metal cord layer 37 in the surface region of the tire 30. In addition to these, the tire damage assessment device 10 may be configured to extract groove portions 51 indicating tread grooves, local damaged portions 52 including local damage such as chips, gouges, and cuts, and repaired portions 53 indicating areas that have been repaired in the past, as shown in Figure 7. Figure 7 shows an enlarged view of the local damaged portion 52 included in a small region 54.

[0061] The groove portion 51, the locally damaged portion 52, and the repaired portion 53 may be extracted using a trained model generated by machine learning similar to that described using the flow diagram of Fig. 3. In particular, even if the locally damaged portion 52 of the tire 30 is small, it affects the progress of wear of the tire 30.

[0062] A procedure for extracting the local damage portion 52 will be described. As in the flow diagram of FIG. 3, an operator extracts the local damage portion 52 on the tread image 40 of a plurality of sample tires and records the area on the tread image 40. For example, the operator performs a process of filling in chips on the tread image 40. This makes it possible to generate second teacher data in which the tread image 40 is input and the area of ​​the local damage portion 52 on the tread image 40 is output. Using the second teacher data, a second trained model can be generated by having an information processing device for machine learning perform machine learning. The tire damage evaluation device 10 can store the second trained model in the storage unit 15. The processing unit 14 can execute a process of inputting the tread image 40 of the tire 30 into the second trained model read out from the storage unit 15 and outputting the local damage portion 52 of the tire 30.

[0063] When the processing unit 14 extracts the locally damaged areas 52, the processing unit 14 calculates the center of gravity (G x ,G y) may be calculated. This allows the processing unit 14 to present the position and area of ​​the local damaged area 52 to the user. The processing unit 14 may further calculate an area ratio, which is the ratio of the total area of ​​the local damaged areas 52 to the area of ​​the entire surface area of ​​the tread portion 35. This allows the processing unit 14 to quantify the size of the local damaged area 52.

[0064] In the above description, the second trained model inputs the tread image 40 and outputs information about the local damaged portion 52. The second trained model may be configured to output the groove portion 51 and / or the repaired portion 53 in addition to or instead of the local damaged portion 52. Information about the groove portion 51 and the repaired portion 53 may be used as appropriate for evaluating the damage state of the tire.

[0065] The first trained model and the second trained model may be generated as a single integrated trained model, rather than as different trained models. When a user inputs a tread image 40 into the tire damage assessment device 10, the processing unit 14 may be configured to generate a division image and extract a minute local damage portion 52 on the first region 41 on the division image. By continuously monitoring the local damage portion 52, the user can monitor the process in which the minute local damage portion 52 leads to significant wear of the tire 30. The user can observe the relationship between the shape of the tread pattern of the tread portion 35 and the progress of wear of the tire 30, and use the observation results in designing the tread pattern of the tire 30.

[0066] (Detection of minor damage levels) The surface of the tire 30 may have many small scratches that are difficult to identify individually, even in areas where the inner rubber layer 34 or the metal cord layer 37 is not exposed, i.e., in the area of ​​the outer rubber layer 33. In one embodiment, the tire damage evaluation device 10 can quantify relatively small damage such as scratches in the tread portion 35 of the tire 30. The damage state of the surface region of the tire 30 can be determined by analyzing the tread image 40 by image processing. In one embodiment, machine learning can be used in determining the damage state.

[0067] Fig. 8 is a flow diagram illustrating a method for generating a third trained model for evaluating the damage state of the tire 30 by machine learning based on the tread image 40. The processing of the flow diagram in Fig. 8 may be executed by an information processing device for machine learning. This information processing device for machine learning may be the same computer as or a different computer from the information processing device for machine learning used to generate the first trained model and / or the second trained model.

[0068] First, the information processing device for machine learning acquires tread images 40 of a plurality of sample tires captured by an operator (step S201). Fig. 9 is a diagram showing the acquired sample tire tread image 40. It is desirable that the tread images of the plurality of sample tires are captured at similar angles and magnifications so as to include the entire tire.

[0069] Next, a rectangular region 55 including the tread portion 35 is extracted from the tread image 40 of the sample tire (step S202). The rectangular region 55 may be extracted as the smallest rectangle that includes the tread portion 35 in the image of the sample tire. In one embodiment, in order to identify the tread portion 35 of the sample tire, the tread image 40 may be input to a first trained model, which may output a segmented image. By using the first trained model, as shown in FIG. 10, the tread portion 35 including a first region 41, a second region 42, and a third region 43 (not shown in FIG. 10) is extracted from the tread image 40 of the sample tire. In the example of FIG. 10, the surface region of the sample tire is mainly the first region 41 where the outer layer rubber 33 is exposed, and a part of the surface region is the second region 42 where the inner layer rubber 34 is exposed. The rectangular region 55 is determined to include the first region 41.

[0070] Next, the information processing device for machine learning divides the extracted rectangular region 55 vertically and horizontally into n×m subregions 57. For example, in the example of FIG. 11, the partitioned region is divided into eight subregions 57 of 2×4. n and m can be set to any natural number. In one embodiment, the information processing device for machine learning can apply a mask 56 to exclude a region having significant damage in the rectangular region 55 from subsequent processing. The region having significant damage includes, for example, the second region 42 and the third region 43 in which the inner rubber layer 34 or the metal cord layer 37 is determined to be exposed when analyzed using the first trained model. In another embodiment, the region having significant damage may include a region in which the local damaged portion 52 is extracted when analyzed using the second trained model.

[0071] In this way, for each divided sub-region 57, the worker determines the damage level according to a predetermined criterion and quantifies it (step S204). The damage level may be determined in five stages from 0 to 4, for example, as shown in FIG. 12. For example, damage level 0 indicates that the damage is slight, and damage level 4 indicates that the damage is severe. The predetermined criterion may include a criterion regarding the number of scratches in the sub-region 57 or per unit area, and the depth of the scratches. Since there are many minute scratches in the surface region of the tread portion 35 of the sample tire, unlike the method of generating the first trained model and the second trained model, the worker does not extract individual scratches. The information processing device for machine learning acquires data in which the damage level determined by the worker is quantified.

[0072] Third teacher data is generated by creating and accumulating a large amount of data in which the tread image 40 is used as input and the damage level of each lower region 57 of the tread image 40 is quantified as output (step S205).

[0073] The information processing device for machine learning performs machine learning using the third training data generated in step S205 (step S206). The information processing device for machine learning includes a function approximator capable of learning the relationship between input and output, and generates a third trained model by learning the third training data (step S207). The third trained model can also be called a damage prediction model. When the tread image 40 of the tire 30 is input, the third trained model outputs a numerical value indicating the damage state of each sub-region 57 obtained by dividing the surface region of the tread portion 35 of the tire 30.

[0074] The third trained model generated according to the flow diagram of FIG. 8 is stored in the storage unit 15 of the tire damage assessment device 10. The processing unit 14 can input the tread image 40 of the tire 30 captured by the imaging device 20 to the third trained model and output information quantifying the damage state of each sub-region 57 into which the surface region of the tread portion 35 is divided. The processing unit 14 may execute this process for the first region 41 portion in which the outer layer rubber 33, which is the layer arranged on the outermost side of the tread portion 35 of the tire 30, is exposed. The information quantifying the damage state output by the third trained model can be output as a numerical value finer than the damage level determined by the worker in step S204. For example, if the worker determines the damage state as a natural number from 0 to 4 in step S204, the information quantifying the damage state output by the third trained model may include a decimal between 0 and 4. The processing unit 14 may convert the information quantifying the damage state including a decimal into an integer value, for example, from 1 to 100.

[0075] (Tire damage evaluation process) Next, an example of a series of processes of a tire damage assessment method executed by the processing unit 14 of the tire damage assessment device 10 that has acquired an image of the tire 30 will be described with reference to the flowchart of Fig. 13. The tire damage assessment device 10 may execute all of the processes in the flowchart of Fig. 13, or may execute only a part of the processes.

[0076] First, the processing unit 14 acquires a tread image 40 obtained by capturing an image of the tread portion 35 of the tire 30 via the communication unit 11 or the input unit 12. The processing unit 14 performs pre-processing required for the following processing on the tread image 40 (step S301). The pre-processing includes processing for making the size of the acquired image uniform. For this reason, in the pre-processing, the processing unit 14 may compress or expand the image.

[0077] Next, the processing unit 14 inputs the tread image 40 into the first trained model and the second trained model, and divides the tread image 40 into segment images including the first region 41 to the third region 43 (step S302). The segmentation in step S302 includes extracting the local damaged portion 52. As a result, the processing unit 14 can generate a segment image as shown in FIG. 5 and extract the local damaged portion 52 as shown in FIG. 7. The processing unit 14 can color-code each region of the segment image and display it on a display, which is a display unit included in the output unit 13. The processing unit 14 stores information on each region and information on the local damaged portion 52 in the storage unit 15.

[0078] The processing unit 14 performs post-processing of the image of the second region 42, which is an exposed region of the inner layer rubber 34 (step S303). For example, the processing unit 14 can generate a histogram showing the distribution of pixels in the second region 42 in the width direction of the tire 30, as described with reference to FIG. 6. In addition, for example, the processing unit 14 calculates the size and / or area ratio of the second region 42 in the surface region of the tread portion 35 of the tire 30. The processing unit 14 outputs the position, size and / or area ratio of the second region 42 in the surface region of the tread portion 35 of the tire 30 via the output unit 13, or stores it in the storage unit 15.

[0079] The processing unit 14 performs post-processing on the image of the third region 43, which is an exposed region of the metal cord layer 17, in the same manner as the image of the second region 42 (step S304). The sizes of the second region 42 and the third region 43 quantitatively indicate the area where the surface of the tire 30 is damaged over a wide area.

[0080] The processing unit 14 performs post-processing of the image of the local damaged portion 52, such as a chip, output by inputting the tread image 40 to the second trained model (step S305). As described with reference to FIG. 7, the post-processing of the local damaged portion 52 may include calculating the position of the center of gravity of the local damaged portion 52 and calculating the area ratio of the area of ​​the local damaged portion 52 to the area of ​​the surface region of the tread portion 35 of the tire 30. The processing unit 14 stores information on the position of the center of gravity and the area ratio in the memory unit 15.

[0081] Next, the processing unit 14 generates a mask 56 that covers the second region 42, the third region 43, and the local damaged area 52 on the tread image 40 (step S306). This mask 56 is similar to the mask 56 that was applied to the tread image 40 of the sample tire when generating the third training data. The mask 56 is intended to exclude the second region 42, the third region 43, and the local damaged area 52, which are severely damaged, from the areas to be evaluated in the process of determining a relatively minor damaged state based on scratches or the like described below.

[0082] The processing unit 14 extracts a rectangular region 55 including the tread portion 35 of the tire 30 from the tread image 40, similar to that shown in Fig. 10 (step S307). The processing unit 14 can recognize the range of the tread portion 35 by generating the divided regions performed in step S302. The processing unit 14 may also apply the mask 56 generated in step S306 to the rectangular region 55. As a result, the regions other than the first region 41 where the outer layer rubber 33 is exposed on the surface are masked.

[0083] The processing unit 14 evaluates the damage state of the surface region of the tire 30 (step S308). Specifically, the processing unit 14 inputs the image of the rectangular region 55 extracted in step S307 to the third trained model, and obtains, as an output, a numerical value indicating the damage state of each sub-region 57. The processing unit 14 can display the output numerical value indicating the damage state of each sub-region 57 on a display unit included in the output unit 13, and store it in the storage unit 15.

[0084] As described above, the tire damage assessment device 10 can output the following assessment result information that quantifies the damage state of the surface region of the tread portion 35 according to the procedure in the flowchart of FIG. (1) An image showing an exposed state of each of the multiple layers of the tread portion 35 of the tire 30 due to wear of the tread portion 35, and quantitative data such as the area of ​​the region where each layer is exposed (2) An image showing localized damaged portions 52 such as chips occurring in the tread portion 35 of the tire 30, the number of localized damaged portions 52, the position of the center of gravity of each localized damaged portion 52, and the area ratio of the localized damaged portions 52 to the tread portion 35 (3) A numerical index of minor damage caused by scratches, etc. occurring on the surface area of ​​the outermost rubber layer 33 of the tread portion 35 In this manner, the tire damage assessment device 10 of the present disclosure can quantify and assess the surface condition of the tread portion 35 of the tire 30. The tire damage assessment device 10 may display information on the assessment results on the display unit of the output unit 13 and / or transmit the information to an external device via the communication unit 11.

[0085] The processing unit 14 of the tire damage assessment device 10 can further estimate the wear characteristics of the tire 30 based on the output information on localized damaged areas 52 such as chips and information on the state of damage due to scratches and the like. By using the tire damage assessment device 10, the user can analyze the relationship between the material of the tire 30 and / or the shape of the tread pattern and tire wear. In addition, by using the information output by the tire damage assessment device 10, the user can predict the lifespan of the tire 30.

[0086] (Another example of the tire damage evaluation system) The tire damage assessment system 1 shown in Fig. 1 is composed of a tire damage assessment device 10 and an imaging device 20. Fig. 14 shows another example of the configuration of the tire damage assessment system 1. In the embodiment of Fig. 14, the tire damage assessment device 10 is placed at a location away from the user, for example, on the cloud. The tire damage assessment device 10 is configured to be able to communicate with a user's portable information terminal 60 and an information processing device 70 via a network 80 such as the Internet.

[0087] The portable information terminal 60 includes, for example, a smartphone and a tablet terminal. The information processing device 70 is a computer such as a PC. The portable information terminal 60 and the information processing device 70 may include components such as a communication unit, an input unit, an output unit, a processing unit, and a storage unit, similar to the tire damage assessment device 10. The portable information terminal 60 may further include an imaging unit.

[0088] The portable information terminal 60 may be configured to transmit the tread image 40 of the tire 30 captured by the imaging unit to the tire damage assessment device 10. Thus, the portable information terminal 60 functions as the imaging device 20 of the tire damage assessment system 1 of FIG. 1. The portable information terminal 60 may also acquire and execute an application corresponding to the tire damage assessment system 1. A user of the portable information terminal 60 may capture an image of the tire 30 by following the guide of the application and transmit the image to the tire damage assessment device 10.

[0089] The information processing device 70 may acquire the evaluation results by the tire damage assessment device 10 and display them on a display which is an output unit. The information processing device 70 may store the evaluation results of the tire damage assessment device 10 in a memory unit. The information processing device 70 may perform further analysis using the evaluation results stored in the memory unit. The information processing device 70 may analyze and evaluate the damage of the tire 30 while transmitting and receiving information to and from the tire damage assessment device 10 using a dedicated application stored in the memory unit.

[0090] Although the embodiments of the present disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art can easily make various modifications or corrections based on the present disclosure. Therefore, it should be noted that these modifications or corrections are included in the scope of the present disclosure. For example, the functions included in each component or each step can be rearranged so as not to be logically inconsistent, and multiple components or steps can be combined into one or divided. The embodiments of the present disclosure can also be realized as a method, a program, or a storage medium on which a program is recorded, which is executed by a processor included in the device. It should be understood that these are also included in the scope of the present disclosure.

[0091] [Contribution to the United Nations-led Sustainable Development Goals (SDGs)] The SDGs have been proposed to realize a sustainable society. One embodiment of the present invention is expected to contribute to "No. 9 - Building a foundation for industry and technological innovation." [Explanation of symbols]

[0092] 10. Tire damage evaluation device 11 Communications Department 12 Input section 13 Output section (display section) 14 Processing section 15 Storage section 20 Imaging device 30 Tires 31 Bead section 32 Sidewall 33 Outer rubber 34 Inner layer rubber 35 Tread section 36 Carcass 37 Layers of Metal Cord 38 Bead core 40 Tread images 41 First area 42 Second area 43 Third area 45 Strip-shaped area 51 Groove 52 Localized injury 53 Repaired section 54 small area 55 Rectangular area 56 Mask 57 Sub-area 60 Mobile Information Terminals 70 Information processing equipment 80 Network

Claims

1. A tire damage evaluation device comprising: a processing unit that acquires a tread image by capturing an image of a tire tread portion, and quantifies a damage state of a surface region of the tread portion based on the tread image.

2. 2. The tire damage assessment device according to claim 1, wherein the tread portion is configured to include a plurality of layers arranged in a radial direction of the tire, and the processing unit generates a divided image in which the surface region of the tread portion included in the tread image is divided by the layers exposed in the surface region.

3. 3. The tire damage evaluation device according to claim 2, wherein the plurality of layers include an outer layer rubber that constitutes a tread surface of the tread portion, a layer of metal cords arranged radially inward of the outer layer rubber, and an inner layer rubber located between the outer layer rubber and the layer of metal cords.

4. 3. The tire damage assessment device according to claim 2, wherein the processing unit inputs tread images of a plurality of sample tires and executes a process of inputting the tread images of the tire into a first trained model trained using first teacher data that outputs a partition image in which a surface area of ​​the sample tires included in the tread images is partitioned by layers exposed on the surface, and outputting the partition image of the tire.

5. 3. The tire damage evaluation device according to claim 2, wherein the processing unit divides the section image of the tire into a plurality of rectangular regions extending in a width direction of the tire, aligns positions of the plurality of rectangular regions so that positions of the tread portion in the rectangular regions in the width direction coincide with each other, and generates a histogram by accumulating pixels of the plurality of rectangular regions for at least any of the layers exposed in the surface region for each position in the width direction.

6. 2. The tire damage assessment device according to claim 1, wherein the processing unit is configured to extract from the tread image at least any of a groove portion indicating a groove in the tread portion, a local damage portion indicating localized damage to the surface region of the tread portion, and a repaired portion indicating a location where damage that occurred in the past in the tire has been repaired.

7. 2. The tire damage evaluation device according to claim 1, wherein the processing unit executes a process of inputting the tread images of a plurality of sample tires into a second trained model trained using second teacher data that outputs an image of a local damage portion showing local damage in the tread portion of the sample tire contained in the tread image, and inputting the tread images of the tire into the second trained model, and outputting the image of the local damage portion of the tire.

8. The tire damage evaluation device according to claim 7 , wherein the processing unit calculates a ratio of an area of ​​the localized damaged portion to an area of ​​the surface region of the tread portion.

9. The tire damage assessment device according to claim 7 , wherein the processing unit calculates a center of gravity position of the area of ​​the localized damage portion.

10. 3. The tire damage assessment device according to claim 2, wherein the processing unit quantifies a damage state of the surface region of the tire in a portion where an outermost layer of the plurality of layers of the tire is exposed, based on the tread image.

11. 11. The tire damage evaluation device according to claim 10, wherein the processing unit extracts an image of a region including the surface region of the tread portion from the tread image, divides the image into images of a plurality of sub-regions, and quantifies a damage state of the surface region for each of the images of the sub-regions.

12. 11. The tire damage evaluation device according to claim 10, wherein the processing unit inputs tread images of a plurality of sample tires to a third trained model trained using third teacher data that outputs information that quantifies a damage state of the surface region of a portion where an outermost layer of the tire is exposed among the plurality of layers included in the tread image, and executes a process of inputting the tread image of the tire to the third trained model and outputting information that quantifies the damage state of the surface region.

13. 13. The tire damage assessment device according to claim 12, wherein the damage state of the surface region in the third teacher data is quantified based on at least one of the number of scratches contained in the surface region and the depth of the scratches.

14. 2. The tire damage assessment device according to claim 1, wherein the damage state includes a state of chips and scratches in the tire, and the processing unit estimates wear characteristics of the tire based on information on the quantified state of the chips and scratches.

15. 2. The tire damage assessment device according to claim 1, further comprising at least one of a display unit that displays quantified information on the damage state of the surface region of the tread portion, and a communication unit that transmits the information to an external device.

16. A tire damage assessment method executed by an information processing device, comprising: Obtaining a tread image by capturing an image of a tread portion of a tire; Quantifying a damage state of a surface region of the tread portion based on the tread image. A tire damage assessment method comprising:

17. A tire damage evaluation program that causes a computer to execute a process of acquiring a tread image by capturing an image of a tire tread portion, and a process of quantifying a damage state of a surface region of the tread portion based on the tread image.

Images