Deterioration estimation system, deterioration estimation method, and program

Abstract

A deterioration estimation system according to the present disclosure comprises: a detection means that detects road surface deterioration from a first road surface image obtained by photographing a road surface; a determination means that determines, within the first road surface image, a first region for which detection of the road surface deterioration is possible and a second region for which detection of the road surface deterioration is difficult; a first calculation means that calculates the degree of deterioration in the first region on the basis of the detection results from the first road surface image; an estimation means that estimates the degree of deterioration in the second region on the basis of the degree of road surface deterioration detected from a second road surface image obtained by photographing the road surface ahead of the first road surface image; and a second calculation means that calculates the degree of deterioration of the road surface on the basis of the calculated degree of deterioration in the first region and the estimated degree of deterioration in the second region.

Description

[Technical Field]

[0001] The present disclosure relates to a deterioration estimation system and the like. [Background technology]

[0002] Road surfaces deteriorate over time. The degree of road surface deterioration is measured to ensure appropriate management and repair of the road surface. There are various methods for measuring the degree of deterioration. Patent Document 1 discloses, as an example, a method for measuring the degree of deterioration by analyzing images captured by a camera.

[0003] Furthermore, future deterioration levels are also predicted. Patent Document 2 discloses a road management system that analyzes regression lines that approximate changes in road surface properties over time during a measurement period. In Patent Document 2, the regression lines for the near future after the measurement period have elapsed are predicted based on traffic volume and weather conditions. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2018-036076 [Patent Document 2] Japanese Patent Application Publication No. 2019-185443 Summary of the Invention [Problem to be solved by the invention]

[0005] Depending on the conditions, road surface deterioration may not be accurately measured from images. For example, when the road surface is covered with water or snow due to precipitation or snowfall, it becomes difficult to detect road surface deterioration.

[0006] The present disclosure aims to provide a deterioration estimation system and the like that can estimate the degree of deterioration of a road surface even if a road surface image captured of the road surface includes areas where it is difficult to detect road surface deterioration. [Means for solving the problem]

[0007] The deterioration estimation system of the present disclosure comprises a detection means for detecting road surface deterioration from a first road surface image photographed of the road surface, a determination means for determining a first area in the first road surface image where road surface deterioration can be detected and a second area where road surface deterioration is difficult to detect, a first calculation means for calculating the degree of deterioration of the first area based on the detection result from the first road surface image, an estimation means for estimating the degree of deterioration of the second area based on the degree of road surface deterioration detected from a second road surface image photographed of the road surface before the first road surface image, and a second calculation means for calculating the degree of deterioration of the road surface based on the calculated degree of deterioration of the first area and the estimated degree of deterioration of the second area.

[0008] The deterioration estimation method of the present disclosure detects road surface deterioration from a first road surface image photographed of the road surface, determines a first area of the first road surface image in which road surface deterioration can be detected and a second area in which road surface deterioration is difficult to detect, calculates the degree of deterioration of the first area based on the detection results from the first road surface image, estimates the degree of deterioration of the second area based on the degree of road surface deterioration detected from a second road surface image photographed of the road surface before the first road surface image, and calculates the degree of deterioration of the road surface based on the calculated degree of deterioration of the first area and the estimated degree of deterioration of the second area.

[0009] A program according to the present disclosure causes a computer to execute the following processes: detect road surface deterioration from a first road surface image obtained by photographing a road surface; determine a first area in the first road surface image where road surface deterioration can be detected and a second area in which road surface deterioration is difficult to detect; calculate a deterioration level of the first area based on the detection result from the first road surface image; estimate a deterioration level of the second area based on a deterioration level of road surface deterioration detected from a second road surface image obtained by photographing the road surface before the first road surface image; and calculate a deterioration level of the road surface based on the calculated deterioration level of the first area and the estimated deterioration level of the second area. The program may be stored in a computer-readable non-transitory recording medium. [Effects of the Invention]

[0010] According to the present disclosure, even if a road surface image captured of the road surface includes an area where it is difficult to detect road surface deterioration, the degree of road surface deterioration can be estimated. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a deterioration estimation system. [Figure 2] FIG. 2 is a diagram illustrating an example of a road surface image. [Figure 3] FIG. 10 is a diagram illustrating an example of devices communicably connected to the deterioration estimation system. [Figure 4] FIG. 10 is a diagram showing an example of a detection result of road surface deterioration. [Figure 5] FIG. 10 is a diagram showing an example of a first region and a second region that have been determined. [Figure 6] 10 is a flowchart illustrating an example of the operation of the deterioration estimation system. [Figure 7] FIG. 10 is a block diagram showing another example configuration of the deterioration estimation system. [Figure 8] 10 is an image showing an example of a first screen. [Figure 9] 10 is an image showing an example of a second screen. [Figure 10] 10 is an image showing an example of a third screen. [Figure 11] 10 is a flowchart showing another example of the operation of the deterioration estimation system. [Figure 12] FIG. 2 is a block diagram illustrating an example of a hardware configuration of a computer. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, with reference to the drawings, embodiments of a deterioration estimation system, a deterioration estimation method, a program, and a non-transitory recording medium for recording the program according to the present disclosure will be described in detail. The disclosed technology is not limited to these embodiments.

[0013] [First embodiment] Fig. 1 is a block diagram showing an example of the configuration of a deterioration estimation system 100 according to the first embodiment. The deterioration estimation system 100 according to the first embodiment includes a detection unit 101, a determination unit 102, a first calculation unit 103, an estimation unit 104, and a second calculation unit 105. In the first embodiment, Fig. 1 shows a case where the deterioration estimation system 100 further includes a display control unit 106. However, the deterioration estimation system 100 does not necessarily have to include the display control unit 106.

[0014] In this embodiment, the deterioration estimation system 100 is used to manage road surface deterioration using road surface images. Road surface deterioration includes, for example, cracks, potholes, rutting, and irregularities in flatness. Cracks may be classified into different types, such as linear cracks and hexagonal cracks, depending on their shape. A linear crack is a single linear crack. A hexagonal crack is a hexagonal crack that occurs, for example, when vertical and horizontal linear cracks are connected. Cracks progress from linear cracks to hexagonal cracks and potholes.

[0015] Various indices are used to represent the degree of road surface deterioration. In the present disclosure, the degree of road surface deterioration is represented by a deterioration degree. The value representing the deterioration degree can be set to increase as the road surface deterioration progresses. The deterioration degree may be any of indices including the degree of cracking, the number of potholes, the size of the potholes, the amount of rutting, or flatness.

[0016] The crack degree is expressed by any one of the shape, length, width, area, and number of cracks, or a combination of these. The crack rate is an example of the crack degree. The crack rate is expressed, for example, by 100 x (crack area / road section area). In this case, the deterioration degree value ranges from 0% to 100%. The crack area is calculated by any method. Note that there are no particular limitations on the method for calculating the crack rate, and other known calculation methods can be applied in addition to those mentioned above.

[0017] The size of a pothole can be expressed by, for example, the area, width, length, or depth of the pothole, or a combination of these. The amount of rutting is the depth of the rut where the vehicle's track is lower than the rest of the road surface due to the load of the vehicle and friction with the tires.

[0018] The deterioration level may also be determined based on a combination of multiple indicators that represent the degree of road surface deterioration. For example, the deterioration level may be the Maintenance Control Index (MCI). The MCI value is the minimum value calculated using four definition formulas that use the crack rate, rutting depth, and flatness. The MCI decreases as the road deteriorates.

[0019] The road surfaces that the deterioration estimation system 100 targets are not limited to ordinary roads on which vehicles and people pass, but also include vehicle test courses, airport runways and taxiways, etc. In other words, the deterioration estimation system 100 can target a wide range of paved road surfaces.

[0020] The detection unit 101 detects road surface deterioration from a road surface image obtained by capturing a road surface. The road surface image may include other objects than the road surface, such as the sky, road signs, and buildings, as long as it is a captured image of the road surface. Alternatively, the road surface image may be an image obtained by capturing only the road surface.

[0021] Fig. 2 is a diagram showing an example of a road surface image taken of a road on which cars are traveling. The road surface image in Fig. 2 includes cracks as an example of road surface deterioration.

[0022] The road surface images are captured by an in-vehicle camera such as a drive recorder. However, the type of camera is not limited to this, and various types of cameras may be used. For example, the road surface images may be captured by a camera mounted on another moving object such as a bicycle or a drone, a camera carried by a person, or a fixed camera installed on the road. The road surface images may be captured by a person or automatically.

[0023] FIG. 3 is a diagram showing an example of devices communicably connected to the deterioration estimation system 100 via a communication network 30 in a wired or wireless manner.

[0024] The display 20 is a display or tablet connected to a computer. An input device such as a mouse or keyboard may be connected to the display 20. Furthermore, if the display 20 is a touch panel display, the display 20 may be configured as an input device. A display control unit 106 included in the deterioration estimation system 100 causes various pieces of information to be displayed on the display 20. The information displayed by the display control unit 106 will be described later.

[0025] A road surface image captured by a camera mounted on the vehicle 10 is transmitted to the deterioration estimation system 100. The transmitted road surface image may be stored in the database 40. At this time, the detection unit 101 may acquire the road surface image from the database 40. Alternatively, if the deterioration estimation system 100 is communicably connected to an arbitrary camera, the detection unit 101 may acquire the road surface image from the camera.

[0026] The detection unit 101 may acquire location information of the point where the road surface image was captured together with the road surface image. The location information includes, for example, latitude and longitude, location information by a Global Navigation Satellite System (GNSS) or a Global Positioning System (GPS), or a location on a map.

[0027] The method for acquiring the position is not particularly limited. The device for receiving radio waves from GNSS satellites may be provided in a camera or a mobile object such as a car. Furthermore, the detection unit 101 may acquire the position information of the newly captured road surface image by, for example, comparing the road surface image stored in a database in association with the position information with the newly captured road surface image.

[0028] Furthermore, the detection unit 101 may acquire the date and time when the road surface image was captured together with the road surface image.

[0029] For example, the detection unit 101 detects road surface deterioration using a known image recognition technique for a road surface image. The detection unit 101 may detect road surface deterioration using a machine learning model. The detection unit 101 may determine whether or not each pixel in the road surface image is road surface deterioration.

[0030] FIG. 4 is a diagram showing an example of a detection result of road surface deterioration. The detection unit 101 may, for example, detect road surface deterioration included in a detection area F1 in the road surface image. The detection area F1 is an area targeted for detecting road surface deterioration. For example, an area of the road surface in the road surface image is set as the detection area F1. However, if the road surface image includes only the road surface, the entire road surface image may be set as the detection area F1. For privacy protection, areas other than the road surface may be excluded from the detection area F1. Furthermore, since it is difficult to detect road surface deterioration on a distant road surface, a nearby road surface may be set as the detection area F1, and a distant road surface may be excluded from the detection area F1. These ranges of the detection area F1 are merely examples, and the range may be set as appropriate.

[0031] The position of the detection area F1 in the road surface image captured by the drive recorder fixed to the vehicle is assumed to be fixed, so for example, a predetermined position in the road surface image is set as the detection area F1.

[0032] Alternatively, the detection area F1 may be set by the user. Alternatively, the detection unit 101 may recognize the road surface and set the area of the recognized road surface as the detection area F1.

[0033] For example, the detection unit 101 may divide the road surface image into predetermined units. Then, the detection unit 101 may detect road surface deterioration for each divided unit. The detection unit 101 may divide the road surface image into detection areas where road surface deterioration is detected, into predetermined units.

[0034] The determination unit 102 determines a first region and a second region from the road surface image. The first region is a region where road surface deterioration can be detected from the road surface image. The second region is a region where road surface deterioration is difficult to detect from the road surface image. Here, the road surface image from which the first region and the second region are determined is also referred to as the first road surface image.

[0035] The determining unit 102 may determine that no area in the first road surface image corresponds to the second area.

[0036] The determination unit 102 may determine whether each divided unit is a first region or a second region. For example, the determination unit 102 may determine the first region and the second region for each unit divided by the detection unit 101. Alternatively, regardless of whether the detection unit 101 divides the road surface image, the determination unit 102 may divide the road surface image into predetermined units separately from the detection unit 101. The determination unit 102 may determine the first region and the second region in units different from the units divided by the detection unit 101.

[0037] Fig. 5 is a diagram showing an example of the determined first and second regions, in which the first region is indicated by a solid line frame and the second region is indicated by a dotted line frame.

[0038] The following describes a case where the determination unit 102 determines the first region and the second region based on the detection result of the detection unit 101. For example, the determination unit 102 determines an area where road surface deterioration has been detected by the detection unit 101 as the first region. The determination unit 102 may also determine an area where the detection unit 101 has determined that there is no road surface deterioration as the first region. For example, an exposed road surface may be determined to be the first region.

[0039] The determination unit 102 may determine, as the second region, a region where road surface deterioration is difficult to detect, among regions where road surface deterioration has not been detected. Road surface deterioration may exist in a region where road surface deterioration is difficult to detect. The determination unit 102 may determine the second region using a machine-learned model of a region where road surface deterioration is difficult to detect. Note that the model for detecting road surface deterioration and the model for determining a region where road surface deterioration is difficult to detect may be the same. In other words, the detection of road surface deterioration and the determination of the second region may be performed by the same process.

[0040] For example, in an area where there is a puddle on the road surface, it is difficult to detect road surface deterioration underneath the puddle. Therefore, the determination unit 102 may recognize the puddle using image recognition technology. Then, the determination unit 102 determines the area where the puddle is located as the second area.

[0041] The second region is not limited to a region with a puddle. The determination unit 102 may determine a region in which other obstructions that block the road surface are recognized as the second region. For example, the second region may be a region with accumulated snow, a region covered with fallen leaves, a region hidden by other cars, or a region with debris. The determination unit 102 may determine a region with accumulated snow or fallen leaves, similar to a puddle, as the second region.

[0042] The second region is not limited to a region with an obstruction. For example, even if there is no obstruction on the road surface, it may be difficult to detect road surface deterioration due to bad weather or sunset. Furthermore, a road surface cast in the shadow of a tree or building may be dark, making it difficult to detect road surface deterioration. Therefore, the determination unit 102 may determine a shadowed region as the second region. In this case, a road surface that is illuminated and has no obstructions may be determined as the first region.

[0043] Furthermore, the determining unit 102 may determine, as the second region, an area in which road surface deterioration was detected in a road surface image captured before the road surface image, among areas in which road surface deterioration was not detected in the road surface image. Here, the road surface image captured later is also referred to as the first road surface image, and the road surface image captured before the first road surface image is also referred to as the second road surface image. An image in which road surface deterioration is easy to detect may be selected as the second road surface image. For example, an image that does not include an area in which road surface deterioration is difficult to detect may be selected as the second road surface image. Furthermore, an image captured on a sunny day may be selected as the second road surface image.

[0044] The determination unit 102, for example, refers to the database 40. Then, the determination unit 102 acquires a second road surface image captured at the same location as the first road surface image. The determination unit 102 then compares the road surface deterioration detected from the first road surface image with the road surface deterioration detected from the second road surface image. Of the areas in which road surface deterioration was not detected in the first road surface image, the determination unit 102 determines the area in which road surface deterioration was detected in the second road surface image as the second area.

[0045] The second road surface image may be selected by the user. For example, the determination unit 102 acquires, from the database 40, a plurality of images in which road surface deterioration can be easily detected, and passes them to the display control unit 106. The display control unit 106 then displays the acquired plurality of images to the user. The determination unit 102 may acquire, from the displayed images, an image selected by the user as the second road surface image.

[0046] The above describes a case where the determination unit 102 determines the first region and the second region based on the detection result of the detection unit 101. However, there is also a case where the determination unit 102 determines the first region and the second region before the detection unit 101 detects road surface deterioration. In this case, the determination unit 102 may determine the first region and the second region using a machine-learned model that identifies regions where road surface deterioration can be detected from a road surface image and regions where road surface deterioration is difficult to detect from a road surface image. Alternatively, the determination unit 102 may compare the first road surface image and the second road surface image described above, and use existing image processing technology to determine regions where the degree of match is equal to or greater than a predetermined threshold as the first region, and regions where the degree of match is lower than the threshold as the second region. The detection unit 101 then detects road surface deterioration in the region determined to be the first region.

[0047] The first calculation unit 103 calculates the deterioration level of the first region based on the detection result from the first road surface image. For example, the first calculation unit 103 calculates the deterioration level for each region divided by the detection unit 101. As an example, if the detection unit 101 detects cracks, the first calculation unit 103 calculates a crack rate for each divided region. The first calculation unit 103 may combine the deterioration levels for each region to calculate the overall deterioration level of the first region included in the first road surface image. As an example of combining the deterioration levels, the first calculation unit 103 may calculate the average of the deterioration levels of each region.

[0048] The estimation unit 104 estimates the deterioration level of the second area based on the past deterioration level. The past deterioration level is the deterioration level of the road surface detected from a second road surface image that was taken before the first road surface image. Here, as the second road surface image, for example, the second road surface image used to determine the second area is used to estimate the deterioration level. However, the second road surface image used to estimate the deterioration level may be different from the image used to determine the second area.

[0049] The estimation unit 104 estimates the degree of deterioration for each region separated by the detection unit 101, for example. As an example, when the detection unit 101 detects cracks, the estimation unit 104 estimates a crack rate for each separated region. The estimation unit 104 may combine the degrees of deterioration for each region to estimate the overall degree of deterioration of the second region included in the first road surface image. As an example of combining the degrees of deterioration, the estimation unit 104 may calculate the average of the estimated degrees of deterioration for each region.

[0050] The estimation unit 104 may estimate the degradation level acquired for the second road surface image as the degradation level of the second region of the first road surface image. Alternatively, the estimation unit 104 may estimate the degradation level of the second region by correcting the acquired degradation level using a parameter. A case in which the estimation unit 104 corrects the degradation level acquired for the second road surface image will be described later.

[0051] The estimation unit 104 acquires the past deterioration level from, for example, a database. The database stores the past deterioration levels calculated for road surface deterioration detected from road surface images. Furthermore, the database stores the shooting location and shooting date of the road surface image in association with the deterioration level. Then, the estimation unit 104 acquires the past deterioration level of the same location as the first road surface image from the database. If multiple deterioration levels are stored in the database, the estimation unit 104 may refer to the deterioration level calculated from the latest second road surface image.

[0052] To acquire the past deterioration level, the estimation unit 104 may acquire a second road surface image. In this case, the past road surface image is stored in the database. For example, the estimation unit 104 may calculate the deterioration level from the second road surface image. However, in the above case, when the estimation unit 104 acquires the deterioration level from the database, the process of the estimation unit 104 calculating the deterioration level from the second road surface image can be reduced.

[0053] The estimation unit 104 may identify an area in the second road surface image that corresponds to the second area in the first road surface image. In this case, the estimation unit 104 acquires the degradation level of the identified area from the second road surface image. However, the estimation unit 104 does not have to acquire the degradation level of the area that corresponds to the second area in the first road surface image. The estimation unit 104 may acquire the degradation level calculated from the entire second road surface image or the entire detection area of the second road surface image.

[0054] The estimation unit 104 may estimate the degree of deterioration acquired as described above for the second road surface image as the degree of deterioration of the second region of the first road surface image.

[0055] Next, an example will be described in which the estimation unit 104 corrects the degree of deterioration acquired for the second road surface image using parameters. Each parameter affects how much the road surface deterioration in the first road surface image has progressed from the deterioration level at the time the second road surface image was captured.

[0056] The estimation unit 104 estimates the degree of deterioration of the second region, for example, by adding the degree of deterioration acquired for the second road surface image and a value obtained by weighting any parameter. The estimation unit 104 may estimate the degree of deterioration of the second region by adding multiple values obtained by weighting multiple parameters, respectively, to the degree of deterioration acquired for the second road surface image. The weights assigned to the parameters represent the degree of influence that each parameter has on the progression of road surface deterioration.

[0057] For the first area, the degree of deterioration detected from the first road surface image is used, and for the second area, the degree of deterioration detected from the second road surface image is corrected using a parameter, thereby more accurately estimating the degree of deterioration of the road surface.

[0058] The type of parameter is not particularly limited, and for example, the parameter may be at least one of the amount of precipitation, the presence or absence of puddles, flatness, and traffic volume. For example, the deterioration level of the second area is expressed by the following formula.

[0059] Deterioration level of the second area = Deterioration level detected from the second road surface image + (precipitation amount × W1 + puddles × W2 + flatness × W3 + duration × W4 + ...) In the above formula, precipitation amount, puddle, flatness, and duration represent parameters, and W1, W2, W3, and W4 represent weights for each parameter.

[0060] The estimation unit 104 may automatically acquire the parameter values. A case in which the estimation unit 104 acquires parameters input from a user will be described later in the second embodiment. Each parameter will be described below.

[0061] The estimation unit 104 may estimate the deterioration level of the second area based on, as an example of a parameter, the total amount of precipitation from the time the second road surface image was captured to the time the first road surface image was captured. The greater the total amount of precipitation, the more advanced the road surface deterioration is predicted to be. Therefore, the greater the total amount of precipitation, the larger the value that the estimation unit 104 adds to the deterioration level obtained for the second road surface image.

[0062] The estimation unit 104 refers to, for example, a database that stores precipitation amounts. The database may store data on precipitation amounts that have actually been observed. Alternatively, the stored precipitation amounts may not be the amounts of precipitation that have actually been observed, but may be average precipitation amounts for a predetermined period of time in an average year. The estimation unit 104 refers to, for example, average precipitation amounts for each month.

[0063] Then, the estimation unit 104 acquires the total amount of precipitation from the time when the second road surface image was captured to the time when the first road surface image was captured. The acquired amount of precipitation does not need to be an accurate amount of precipitation. The estimation unit 104 may acquire the total amount of precipitation between the time when the two road surface images were captured, but the acquired amount of precipitation is not limited to this. For example, the estimation unit 104 may acquire the total amount of precipitation including the amount of precipitation for several hours or several days before and after the time when the road surface image was captured.

[0064] Furthermore, the estimation unit 104 may estimate the deterioration level of the second region based on whether or not a puddle is captured in the first road surface image, as an example of a parameter. For example, the parameter may be set to "1" when a puddle is present, and "0" when no puddle is present. This parameter may be applied to a second region included in a road surface image in which a puddle is captured, regardless of whether or not a puddle is present in each second region. A road surface with puddles may have poor drainage, and it is predicted that road surface deterioration is likely to progress. Therefore, if a puddle is present on the road surface, the estimation unit 104 adds a predetermined value to the deterioration level acquired for the second road surface image in estimating the deterioration level of the second region.

[0065] Alternatively, the estimation unit 104 may estimate the deterioration level of the second region based on whether or not there is a puddle in the second region, as an example of a parameter. For example, a parameter "1" is set for a second region with a puddle, and a parameter "0" is set for a second region without a puddle. A road surface with a puddle may be recessed compared to other road surfaces. Therefore, it is predicted that there is an irregularity in flatness, which is an example of road surface deterioration. It is also predicted that water accumulated on a recessed road surface will accelerate road surface deterioration. If there is a puddle in the second region, the estimation unit 104 adds a predetermined value to the deterioration level acquired for the second road surface image.

[0066] The estimation unit 104 may estimate the deterioration level of the second region based on, as an example of a parameter, the smoothness of the road surface at the time the first road surface image was captured. Even if it is difficult to detect road surface deterioration from a road surface image, the smoothness may be measurable. For example, the smoothness may be measured by a method other than image recognition even on a rainy day or when there are puddles on the road surface.

[0067] The flatness may be expressed by the International Roughness Index (IRI). The IRI is an index that relates the road surface to the driver's ride comfort, and is a numerical representation of the degree of unevenness. The IRI may be calculated based on measurement data obtained by measuring the road surface with a sensor. Alternatively, the IRI may be calculated based on the value of an acceleration sensor attached to the vehicle while it is traveling. Specifically, for example, the IRI is calculated based on the value of the vertical acceleration included in the acceleration obtained at the detection position. Note that the method of calculating the IRI is not limited to the above, and any known calculation method may be adopted.

[0068] The estimation unit 104 acquires a value representing the flatness from, for example, a vehicle that measures the flatness while capturing the first road surface image. It is predicted that a road surface with low flatness and unevenness will have a high degree of road surface deterioration detected from the road surface image. Therefore, the lower the flatness, the larger the value that the estimation unit 104 adds to the degree of deterioration acquired for the second road surface image.

[0069] Furthermore, the estimation unit 104 may estimate the deterioration level of the second region based on the length of the period from the capture date of the second road surface image to the capture date of the first road surface image, as an example of a parameter. The longer the period, the more advanced the road surface deterioration is predicted to be. The period may be expressed, for example, by the number of years, the number of months, the number of days, or the number of hours. The estimation unit 104, for example, obtains the capture dates and times of the first road surface image and the second road surface image, and calculates the length of the period. However, the length of the period used for estimation does not need to be an exact value.

[0070] Alternatively, the estimation unit 104 may estimate the degree of deterioration of the second area based on traffic volume as an example of a parameter. For example, the estimation unit 104 may estimate the degree of deterioration of the second area based on the total traffic volume from the time the second road surface image was captured to the time the first road surface image was captured. The greater the total traffic volume, the more advanced the road surface deterioration is predicted to be. The traffic volume represents, for example, the number of vehicles that have traveled on the road surface.

[0071] The estimation unit 104 refers to, for example, a database that stores traffic volume. The database may store data on annual traffic volume, data on traffic volume for one week, etc. Alternatively, the traffic volume to be stored may not be an actually counted traffic volume, but may be an average traffic volume for the point or area where the road surface image is captured.

[0072] Then, the estimation unit 104 acquires the total traffic volume from the time when the second road surface image was captured to the time when the first road surface image was captured. The acquired traffic volume does not need to be an accurate traffic volume. The estimation unit 104 may acquire the total traffic volume between the time when the two road surface images were captured, but the acquired traffic volume is not limited to this. For example, the estimation unit 104 may acquire the total traffic volume including the traffic volume for several hours or several days before and after the time when the road surface image was captured.

[0073] The estimation unit 104 adds a larger value to the deterioration level acquired for the second road surface image as the acquired total traffic volume increases.

[0074] The acquired values for the various parameters described above may be displayed by the display control unit 106. By displaying the values, the user can understand what parameters are used to estimate the deterioration level of the second region.

[0075] The estimation unit 104 may set the weight assigned to each parameter by any method. For example, the weight may be set by a machine-learned model. Furthermore, whether or not to use each parameter may also be set by the model.

[0076] In areas with heavy traffic, the weight of a parameter related to traffic volume may be set higher than that of other parameters. That is, in areas with heavy traffic volume, the weight may be set so that the influence of traffic volume on road surface deterioration is greater. The estimation unit 104, for example, acquires the traffic volume for a predetermined period at the point where the road surface image was captured. Then, the estimation unit 104 changes the weight of the parameter that is the total traffic volume from the time when the second road surface image was captured to the time when the first road surface image was captured, depending on the acquired traffic volume. Changing the weight of the parameter depending on the traffic volume by the estimation unit 104 is an example of estimating the deterioration level of the second area based on the traffic volume.

[0077] The estimation unit 104 may also estimate the deterioration level of the second region based on the amount of precipitation at the time the first road surface image was captured. For example, the estimation unit 104 may set a larger weight for a parameter related to the amount of precipitation than for other parameters during the rainy season or snowy season. In other words, the weight may be set so that the influence of precipitation on road surface deterioration is greater during periods of heavy precipitation.

[0078] The estimation unit 104 may acquire the amount of precipitation on the day the first road surface image was captured, or the amount of precipitation including the days before and after the capture date. Alternatively, the estimation unit 104 may acquire the amount of precipitation in an average year for the same period on the day the first road surface image was captured. Based on the acquired amount of precipitation, the estimation unit 104 assigns a weight to a parameter that is, for example, the total amount of precipitation from the time the second road surface image was captured to the time the first road surface image was captured.

[0079] As described above, by weighting the parameters, the degree of road surface deterioration can be estimated more accurately.

[0080] The second calculation unit 105 calculates the deterioration level of the photographed road surface based on the calculated deterioration level of the first region and the estimated deterioration level of the second region. Here, the deterioration level calculated by the second calculation unit 105 is estimated to be the deterioration level of the road surface at the time the first road surface image was photographed.

[0081] The second calculation unit 105 calculates the degree of deterioration of the photographed road surface, for example, by adding up the degrees of deterioration of the first and second areas, weighted by the areas of the first and second areas, respectively.

[0082] The deterioration level estimated by the second calculation unit 105 may be stored in a database as data on the road surface at the time the first road surface image was captured. Storing the estimated deterioration level in the database makes it possible to refer to the estimated deterioration level together with other deterioration level data for the same location at a later date. The database may also store the detection result by the detection unit 101, the calculation result by the first calculation unit 103, and the estimation result by the estimation unit 104 in association with the deterioration level.

[0083] The display control unit 106 displays the deterioration level of the road surface calculated by the second calculation unit 105. The deterioration level may be displayed in different colors depending on the range of the deterioration level. For example, a high deterioration level may be displayed in red, a low deterioration level in green, and an intermediate deterioration level in yellow.

[0084] The display control unit 106 may further display a first road surface image. The display control unit 106 may display the first area and the second area on the road surface image in different ways. For example, the first area and the second area may be displayed with frames of different colors. Alternatively, the first area may be displayed with a solid frame, and the second area may be displayed with a dotted frame.

[0085] The display control unit 106 may also display the detection area on the road surface image. For example, a frame indicating the detection area is displayed on the display using a frame of a different color or a frame of a different thickness for the first area and the second area. For privacy protection, the display control unit 106 may display the road surface image with a lower resolution for areas other than the detection area.

[0086] The display control unit 106 may reflect the calculated deterioration level on a map showing the deterioration level of the road surface. For example, the road surface on the map may be divided into predetermined ranges. Each divided area may be colored according to the range of the deterioration level. Furthermore, when an arrow indicating the vehicle's traveling direction is displayed for each area of the road divided into predetermined ranges, the arrow may be displayed in a color according to the range of the deterioration level.

[0087] FIG. 6 is a flowchart showing an example of the operation of the deterioration estimation system 100 according to the first embodiment.

[0088] The detection unit 101 detects road surface deterioration from a first road surface image obtained by photographing the road surface (step S1). The detection unit 101 passes the detected road surface deterioration to the first calculation unit 103. Next, the determination unit 102 determines, from the first road surface image, a first region in which road surface deterioration can be detected and a second region in which road surface deterioration is difficult to detect (step S2). Step S2 may be executed before step S1.

[0089] The first calculation unit 103 calculates the deterioration level of the first region based on the detection result from the first road surface image (step S3). The first calculation unit 103 passes the calculated deterioration level to the second calculation unit 105. The estimation unit 104 estimates the deterioration level of the second region based on the deterioration level of the road surface detected from the second road surface image, which was taken before the first road surface image (step S4). The estimation unit 104 passes the estimated deterioration level to the second calculation unit 105.

[0090] The second calculation unit 105 calculates the deterioration level of the photographed road surface based on the calculated deterioration level of the first region and the estimated deterioration level of the second region (step S5). After step S5, the display control unit 106 may display the calculated deterioration level on the display.

[0091] As described above, in the first embodiment, the deterioration estimation system 100 detects the degree of road surface deterioration from a road surface image. Then, the deterioration estimation system 100 calculates the degree of road surface deterioration based on the degree of deterioration calculated for areas where road surface deterioration is detectable and the degree of deterioration estimated for areas where road surface deterioration is difficult to detect. Therefore, even if a road surface image taken of the road surface includes areas where it is difficult to detect road surface deterioration, the user can estimate the degree of deterioration of the road surface.

[0092] It is difficult to clearly capture the road surface condition in road surface images taken on rainy or snowy days, making it difficult to accurately measure deterioration. Therefore, when measuring road surface deterioration using images, measurement may be suspended on rainy or snowy days. Furthermore, if bad weather such as rain or snow continues, measurement of road surface deterioration cannot be performed for a long period of time. According to the first embodiment, it is possible to measure the degree of deterioration even in areas where it is difficult to detect road surface deterioration in images taken on rainy or snowy days.

[0093] [Second embodiment] 7 is a diagram showing an example of the configuration of a deterioration estimation system 100 according to the second embodiment. The deterioration estimation system 100 according to the second embodiment differs from the deterioration estimation system 100 according to the first embodiment in that it includes a receiving unit 107. Regarding the configuration of the second embodiment, some explanations of the configuration similar to that of the first embodiment will be omitted.

[0094] In the second embodiment, the display control unit 106 displays a first road surface image. The first road surface image is an image from which road surface deterioration is detected and the deterioration level is estimated, similar to the first embodiment.

[0095] The receiving unit 107 receives information that the first road surface image includes an area where it is difficult to detect road surface deterioration. For example, the receiving unit 107 receives information from the user that the first road surface image includes an area where it is difficult to detect road surface deterioration. The receiving unit 107 receives information that the first road surface image includes an area where it is difficult to detect road surface deterioration. The receiving unit 107 receives information that the user has pressed a button. The button is displayed on a display by the display control unit 106, for example.

[0096] 8 is an image showing an example of a first screen that the display control unit 106 displays on the display. The first screen includes a button for accepting that an area where it is difficult to detect road surface deterioration is included. In FIG. 8, the first screen includes a first road surface image and the detection result of road surface deterioration by the detection unit 101.

[0097] The display control unit 106 may display the road surface deterioration detected from the first road surface image. For example, the display control unit 106 may display a frame surrounding the area where the road surface deterioration is detected, superimposed on the road surface image, as shown in Fig. 8. Alternatively, the display control unit 106 may display the road surface image in which the detected road surface deterioration is colored.

[0098] The display control unit 106 may display a deterioration degree calculated for road surface deterioration detected from the first road surface image. This deterioration degree may be calculated by the first calculation unit 103. In FIG. 8, a crack rate is displayed as an example of the deterioration degree. Furthermore, the display control unit 106 may display a deterioration degree previously measured at the same point. In FIG. 8, the past deterioration degree and the deterioration degree measured from the first road surface image are plotted on a graph.

[0099] In Fig. 8, an "Estimation in Rainy Weather" button is displayed as an example of a button that accepts that an area where it is difficult to detect road surface deterioration is included. However, the form of this embodiment is not limited to use in rainy weather. The display mode of the button can be changed in various ways.

[0100] The user may confirm, for example, that the displayed road image contains puddles or that the road image was taken in the rain. Alternatively, the user may confirm that the image contains areas where road deterioration may exist, among areas where no road deterioration has been detected. Based on the confirmation, the user presses a button such as the "rainy weather estimation" button.

[0101] The determining unit 102 determines the second region in response to reception by the receiving unit 107, for example, in the same manner as in the first embodiment. Alternatively, the determining unit 102 may determine the region designated by the user as the second region. In this case, the receiving unit 107 may receive a region designation from the user and an input specifying the designated region as the second region.

[0102] Once the second area is determined, the estimation unit 104 estimates the deterioration level of the second area, similar to the processing in the first embodiment. Furthermore, the second calculation unit 105 calculates the deterioration level of the road surface captured in the first road surface image based on the deterioration levels of the first area and the second area, similar to the processing in the first embodiment.

[0103] When the deterioration level is estimated based on the deterioration level acquired for the second road surface image and a weighted value of an arbitrary parameter, the accepting unit 107 may further accept an input of the parameter by the user. At this time, the display control unit 106 displays a second screen for accepting the input of the parameter.

[0104] 9 is an image showing an example of the second screen displayed on the display by the display control unit 106. The display control unit 106 may switch the first screen to the second screen in response to pressing of the "rainy weather estimation" button on the first screen.

[0105] 9 includes fields for receiving input of parameters including the amount of precipitation, the presence or absence of puddles, flatness, and the length of the period, although the input parameters are not limited to these.

[0106] The amount of precipitation may be, for example, the total amount of precipitation from the time the second road surface image was captured to the time the first road surface image was captured. The presence or absence of puddles may be whether or not puddles are captured in the first road surface image. The smoothness may be an IRI calculated based on the value of an acceleration sensor attached to the vehicle while it is traveling. The length of the period is, for example, the length of the period from the time the second road surface image was captured to the time the first road surface image was captured.

[0107] The second screen may include, for example, a radio button that is pressed if the image was taken in the rain. Depending on the input of the radio button, input may be possible in the field below the button.

[0108] Some or all of the parameters may be input automatically. For example, the display control unit 106 displays the parameters acquired by the estimation unit 104. The automatically input parameters may be corrected by the user via the reception unit 107.

[0109] As shown in Fig. 9, the second screen may display a first road surface image as "the image captured this time." The first area and the second area determined by the determination unit 102 may be displayed on the first road surface image. Also, the second screen may display a "road surface image captured previously" as the second road surface image.

[0110] The display of the second screen may be omitted. For example, if weighted values of various parameters are not added to estimate the deterioration level, the display of the second screen may be omitted.

[0111] After the second calculation unit 105 calculates the deterioration level, the display control unit 106 displays the third screen. The third screen displays the calculation result of the deterioration level at the time when the first road surface image was captured by the second calculation unit 105.

[0112] 10 is an image showing an example of a third screen that the display control unit 106 displays on the display. The display control unit 106 may switch the second screen to the third screen in response to pressing of an "Estimate" button on the second screen. Alternatively, if the display of the second screen is omitted, the display control unit 106 may switch the first screen to the third screen in response to pressing of a button on the first screen, such as a "Rainy Weather Estimation" button.

[0113] The third screen displays the deterioration degree calculated by the second calculation unit 105. As an example of the deterioration degree calculated by the second calculation unit 105, an "estimated crack rate" is displayed in FIG. 10. In FIG. 10, the deterioration degree calculated by the second calculation unit is plotted on a graph. The deterioration degree measured from the first road surface image and the estimated deterioration degree may be displayed in different ways.

[0114] FIG. 11 is a flowchart showing an example of the operation of the deterioration estimation system 100 according to the second embodiment.

[0115] The detection unit 101 detects road surface deterioration from a first road surface image obtained by photographing the road surface (step S21). The detection unit 101 passes the detected road surface deterioration to the first calculation unit 103 and the display control unit 106. After step S21, the display control unit 106 may display the detected road surface deterioration. For example, the display control unit 106 displays the first screen of FIG. 8.

[0116] Next, the accepting unit 107 accepts that the first road surface image includes an area where it is difficult to detect road surface deterioration (step S22). For example, the accepting unit 107 accepts from the user pressing the "rainy weather estimation" button in FIG.

[0117] In response to the reception by the reception unit 107, the determination unit 102 determines a second region in the first road surface image where it is difficult to detect road surface deterioration (step S23). The determination unit 102 transfers the determined second region to the estimation unit 104.

[0118] Furthermore, the determination unit 102 determines a first region where road surface deterioration can be detected (step S24). The determination unit 102 passes the determined first region to the first calculation unit 103. The determination of the first region may be performed by the same process as the determination of the second region in step S23. Alternatively, the determination of the first region may be performed at any timing between before step S21 and before step S25.

[0119] The first calculation unit 103 calculates the deterioration level of the first region based on the detection result from the first road surface image (step S25). The first calculation unit 103 passes the calculated deterioration level to the second calculation unit 105. Step S25 may be executed at any timing after road surface deterioration is detected and the first region is determined.

[0120] The estimation unit 104 estimates the deterioration level of the second region based on the past deterioration level (step S26). The past deterioration level is the deterioration level of the road surface detected from a second road surface image that was taken before the first road surface image. For example, in step S26, the estimation unit 104 acquires the deterioration level related to the second road surface image from a database. The estimation unit 104 passes the estimated deterioration level to the second calculation unit 105.

[0121] The second calculation unit 105 calculates the deterioration level of the photographed road surface based on the calculated deterioration level of the first region and the estimated deterioration level of the second region (step S27). After step S27, the display control unit 106 may display the calculated deterioration level on the display.

[0122] As described above, in the second embodiment, the deterioration estimation system 100 determines a second area and estimates the deterioration level of the second area in response to receiving information that the first road surface image includes an area where road surface deterioration is difficult to detect. Therefore, the determination of the second area can be executed only when necessary. Furthermore, even if a road surface image obtained by capturing the road surface includes an area where road surface deterioration is difficult to detect, the deterioration level of the road surface can be estimated.

[0123] [Variations] A modified example of the second embodiment will be described. The deterioration estimation system 100 according to the second embodiment may further include a determination unit. The determination unit determines whether or not there is a possibility that an area where it is difficult to detect road surface deterioration is included, based on weather information for the time when the first road surface image was taken. The determination unit acquires weather information for the location or area where the road surface image was taken.

[0124] The weather information is, for example, information about the weather, the amount of precipitation, or the amount of snowfall. The weather information indicates, for example, any of various weather conditions such as sunny, cloudy, rainy, snowy, foggy, etc. The weather information may also include wind speed, sunrise time, sunset time, etc.

[0125] For example, the determination unit acquires weather information on the date or time when the road surface image was captured. Alternatively, the determination unit may acquire weather information within a predetermined range before and after the date when the road surface image was captured. If the weather information indicates rain, puddles may form on the road surface. Therefore, the determination unit determines that there is a possibility that an area where it is difficult to detect road surface deterioration is included.

[0126] The determination method is not limited to the above and can be modified in various ways. For example, when the wind speed is strong, leaves may fall onto the road surface. Therefore, the determination unit may determine that a road surface image taken on a day with strong wind speed or the day after a day with strong wind speed may include an area where it is difficult to detect road surface deterioration. Furthermore, the determination unit may determine that a road surface image taken within a predetermined range before and after sunset may include an area where it is difficult to detect road surface deterioration due to a shadow.

[0127] The receiving unit 107 receives from the determining unit information that an area where it is difficult to detect road surface deterioration is included.

[0128] As described in the second embodiment, the determining unit 102 determines the second region in response to reception by the receiving unit 107. That is, the determining unit 102 may determine the second region in response to a determination that the second region may include an area where it is difficult to detect road surface deterioration, based on weather information at the time when the first road surface image was captured.

[0129] [Hardware configuration] In each of the above-described embodiments, each component of the deterioration estimation system 100 represents a functional block. Some or all of the components of the deterioration estimation system 100 may be realized by any combination of a computer 500 and a program.

[0130] Fig. 12 is a block diagram showing an example of the hardware configuration of a computer 500. Referring to Fig. 12, the computer 500 includes, for example, a CPU (Central Processing Unit) 501, a ROM (Read Only Memory) 502, a RAM (Random Access Memory) 503, a program 504, a storage device 505, a drive device 507, a communication interface 508, an input device 509, an input / output interface 511, and a bus 512.

[0131] The program 504 includes instructions for realizing each function of the deterioration estimation system 100. The program 504 is stored in advance in the ROM 502, the RAM 503, or the storage device 505. The CPU 501 executes the instructions included in the program 504 to realize each function of the deterioration estimation system 100. For example, the CPU 501 of the deterioration estimation system 100 executes the instructions included in the program 504 to realize the functions of the deterioration estimation system 100. The RAM 503 may also store data to be processed in each function of the deterioration estimation system 100. For example, a road surface image may be stored in the RAM 503 of the computer 500.

[0132] The drive device 507 reads and writes data from and to the recording medium 506. The communication interface 508 provides an interface with a communication network. The input device 509 is, for example, a mouse or keyboard, and accepts information input from a user. The output device 510 is, for example, a display, and outputs (displays) information to the user. The input / output interface 511 provides an interface with peripheral devices. The bus 512 connects these hardware components. The program 504 may be supplied to the CPU 501 via a communication network, or may be stored in advance on the recording medium 506, read by the drive device 507, and supplied to the CPU 501.

[0133] It should be noted that the hardware configuration shown in FIG. 12 is an example, and other components may be added, or some components may not be included.

[0134] There are various variations in the method of realizing the deterioration estimation system 100. For example, the deterioration estimation system 100 may be realized by any combination of a different computer and a program for each component. Furthermore, multiple components included in the deterioration estimation system 100 may be realized by any combination of a single computer and a program.

[0135] Furthermore, some or all of the components of the deterioration estimation system 100 may be realized by one or more processors. The processor may be configured by a single chip, or may be configured by multiple chips connected via a bus. Some or all of the components of the deterioration estimation system 100 may be realized by a combination of the above-mentioned processor and a program.

[0136] Furthermore, when some or all of the components of the deterioration estimation system 100 are realized by a plurality of computers, circuits, etc., the plurality of computers, circuits, etc. may be arranged in a centralized manner or in a distributed manner.

[0137] Furthermore, at least a part of the deterioration estimation system 100 may be provided in a SaaS (Software as a Service) format. That is, at least a part of the functions for realizing the deterioration estimation system 100 may be executed by software executed via a network.

[0138] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, the configurations in the respective embodiments can be combined with each other without departing from the scope of the present disclosure.

[0139] Some or all of the above embodiments may be described as, but are not limited to, the following supplementary notes.

[0140] [Appendix 1] a detection means for detecting road surface deterioration from a first road surface image obtained by photographing the road surface; a determining means for determining, from the first road surface image, a first area in which road surface deterioration can be detected and a second area in which road surface deterioration is difficult to detect; a first calculation means for calculating a deterioration level of the first area based on a detection result from the first road surface image; an estimation means for estimating a deterioration level of the second area based on a deterioration level of road surface deterioration detected from a second road surface image taken before the first road surface image; a second calculation means for calculating a deterioration level of the road surface based on the calculated deterioration level of the first area and the estimated deterioration level of the second area; A deterioration estimation system comprising:

[0141] [Appendix 2] The determining means determines an area of the first road surface image that has at least one of a puddle, snow, fallen leaves, or a shadow as the second area. 10. A deterioration estimation system as described in appendix 1.

[0142] [Appendix 3] The estimation means estimates the degree of deterioration of the second area by correcting the degree of deterioration detected from the second road surface image using a parameter. 3. A deterioration estimation system according to claim 1 or 2.

[0143] [Appendix 4] The estimation means estimates the deterioration level of the second area using, as the parameter, a total amount of precipitation from the time when the second road surface image was captured to the time when the first road surface image was captured. 3. A deterioration estimation system as described in appendix 3.

[0144] [Appendix 5] The estimation means further estimates the deterioration level of the second area using, as the parameter, whether or not a puddle is captured in the first road surface image. 4. A deterioration estimation system as described in appendix 4.

[0145] [Appendix 6] The estimation means further estimates the deterioration level of the second area using, as the parameter, whether or not there is a puddle in the second area. 6. A deterioration estimation system according to claim 4 or 5.

[0146] [Appendix 7] The estimation means further estimates the deterioration level of the second area using, as the parameter, the flatness of the road surface at the time when the first road surface image was captured. 7. A deterioration estimation system according to any one of appendixes 4 to 6.

[0147] [Appendix 8] The estimation means further estimates the deterioration level of the second area using, as the parameter, a length of a period from a time when the second road surface image was captured to a time when the first road surface image was captured. 8. A deterioration estimation system according to any one of appendices 4 to 7.

[0148] [Appendix 9] The estimation means further estimates the deterioration level of the second area using, as the parameter, a total traffic volume from the time when the second road surface image was captured to the time when the first road surface image was captured. 9. A deterioration estimation system according to any one of appendixes 4 to 8.

[0149] [Appendix 10] The estimation means estimates the deterioration degree of the second area by changing a weight of the total traffic volume among the plurality of parameters in accordance with the traffic volume of an area including the road surface. 10. A deterioration estimation system as described in Appendix 9.

[0150] [Appendix 11] The estimation means estimates the deterioration level of the second area by changing a weight of the total amount of precipitation among the plurality of parameters in accordance with the amount of precipitation at the time of capturing the first road surface image. 11. A deterioration estimation system according to any one of appendixes 4 to 10.

[0151] [Appendix 12] The estimation means estimates the deterioration degree of the second area based on the parameters input by a user. 11. A deterioration estimation system according to any one of appendices 3 to 10.

[0152] [Appendix 13] The estimation means estimates the deterioration level of the second area based on the deterioration level of the road surface detected from the second road surface image. 3. A deterioration estimation system according to claim 1 or 2.

[0153] [Appendix 14] a receiving means for receiving that the first road surface image includes an area where it is difficult to detect road surface deterioration, The determining means determines the second area in response to the reception. 14. A deterioration estimation system according to any one of appendices 1 to 13.

[0154] [Appendix 15] The determining means determines the second area in response to a determination that the first road surface image may include an area where it is difficult to detect road surface deterioration, based on weather information at the time the first road surface image was taken. 15. A deterioration estimation system according to any one of appendices 1 to 14.

[0155] [Appendix 16] Detecting road surface deterioration from a first road surface image obtained by photographing the road surface; determining a first area in which road surface deterioration can be detected and a second area in which road surface deterioration is difficult to detect from the first road surface image; calculating a deterioration level of the first area based on the detection result from the first road surface image; estimating a deterioration level of the second area based on a deterioration level of road surface deterioration detected from a second road surface image taken before the first road surface image; Calculating the deterioration level of the road surface based on the calculated deterioration level of the first area and the estimated deterioration level of the second area. Deterioration estimation method.

[0156] [Appendix 17] Detecting road surface deterioration from a first road surface image obtained by photographing the road surface; determining a first area in which road surface deterioration can be detected and a second area in which road surface deterioration is difficult to detect from the first road surface image; calculating a deterioration level of the first area based on the detection result from the first road surface image; estimating a deterioration level of the second area based on a deterioration level of road surface deterioration detected from a second road surface image taken before the first road surface image; Calculating the deterioration level of the road surface based on the calculated deterioration level of the first area and the estimated deterioration level of the second area. A recording medium that non-temporarily records a program that causes a computer to execute a process. [Explanation of symbols]

[0157] 100 Deterioration Estimation System 101 Detector 102 Decision Section 103 1st calculation section 104 Estimation part 105 2nd calculation section 106 Display control unit 107 Reception 10 vehicles 20 Display 30 Communication Network 40 databases

Claims

1. a detection means for detecting road surface deterioration from a first road surface image obtained by photographing the road surface; a determining means for determining, from the first road surface image, a first area in which road surface deterioration can be detected and a second area in which road surface deterioration is difficult to detect; a first calculation means for calculating a deterioration level of the first area based on a detection result from the first road surface image; an estimation means for estimating a deterioration level of the second area based on a deterioration level of road surface deterioration detected from a second road surface image taken before the first road surface image; a second calculation means for calculating a deterioration level of the road surface based on the calculated deterioration level of the first area and the estimated deterioration level of the second area; A deterioration estimation system comprising:

2. The determining means determines an area of the first road surface image that has at least one of a puddle, snow, fallen leaves, and a shadow as the second area. The deterioration estimation system according to claim 1 .

3. The estimation means estimates the degree of deterioration of the second area by correcting the degree of deterioration detected from the second road surface image using a parameter. The deterioration estimation system according to claim 1 or 2.

4. The estimation means estimates the deterioration level of the second area using, as the parameter, a total amount of precipitation from the time when the second road surface image was captured to the time when the first road surface image was captured. The deterioration estimation system according to claim 3 .

5. The estimation means further estimates the deterioration level of the second area using, as the parameter, whether or not a puddle is captured in the first road surface image. The deterioration estimation system according to claim 4 .

6. The estimation means further estimates the deterioration level of the second area using, as the parameter, the flatness of the road surface at the time when the first road surface image was captured. The deterioration estimation system according to claim 4 or 5.

7. The estimation means further estimates the deterioration level of the second area using, as the parameter, a length of a period from a time when the second road surface image was captured to a time when the first road surface image was captured. The deterioration estimation system according to any one of claims 4 to 6.

8. The estimation means estimates the deterioration degree of the second area based on the parameters input by a user. The deterioration estimation system according to any one of claims 3 to 7.

9. A computer comprising: detecting road surface deterioration from a first road surface image obtained by photographing the road surface; determining a first area in which road surface deterioration can be detected and a second area in which road surface deterioration is difficult to detect from the first road surface image; calculating a deterioration level of the first area based on the detection result from the first road surface image; estimating a deterioration level of the second area based on a deterioration level of road surface deterioration detected from a second road surface image captured before the first road surface image; Calculating the deterioration level of the road surface based on the calculated deterioration level of the first area and the estimated deterioration level of the second area. Deterioration estimation method.

10. detecting road surface deterioration from a first road surface image obtained by photographing the road surface; determining a first area in which road surface deterioration can be detected and a second area in which road surface deterioration is difficult to detect from the first road surface image; calculating a deterioration level of the first area based on the detection result from the first road surface image; estimating a deterioration level of the second area based on a deterioration level of road surface deterioration detected from a second road surface image captured before the first road surface image; Calculating the deterioration level of the road surface based on the calculated deterioration level of the first area and the estimated deterioration level of the second area. A program that causes a computer to perform a process.

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