Road surface data processing device, road surface data processing method and program

Abstract

To provide a road surface data processor, a road surface data processing method, and a program that can easily discriminate a pavement kind.SOLUTION: An information processor comprises: acquisition means for acquiring road surface data that measures a height of each point on a road surface; unevenness analysis means that analyzes the road surface data and calculates an irregularity index value that expresses the degree of irregularity of the road surface; and determination means that determines a pavement kind of the road according to the magnitude of the irregularity index value. The unevenness analysis means may calculate the irregularity index value based on a predetermined high-frequency component of the road surface data.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a road surface data processing device, a road surface data processing method, and a program. [Background technology]

[0002] As road networks continue to expand over vast distances, the number of instances of damage and deterioration increases, as does the need to efficiently detect such damage and deterioration so that they can be repaired.

[0003] Conventionally, there is a technology for detecting abnormalities on a road surface by photographing the road surface from a traveling vehicle. In Patent Document 1, cracks on the road surface are detected based on the height of the road surface measured from a traveling vehicle by the light cutting method, and a three-dimensional crack map showing the three-dimensional positions of the cracks is generated. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 7082227 Summary of the Invention [Problem to be solved by the invention]

[0005] However, there are multiple types of road pavement. If one were to try to detect anomalies using uniform criteria for all of these, there would be a high possibility of missed detections or false positives. Therefore, the criteria for detecting anomalies are changed according to the road pavement type. However, in order to change the detection criteria based on actual measurement results, the pavement type needs to be identified. Conventionally, the pavement type had to be manually input and set, which required a huge amount of work.

[0006] An object of the present invention is to provide a road surface data processing device, a road surface data processing method, and a program that can easily distinguish the pavement type. [Means for solving the problem]

[0007] In order to achieve the above object, the present disclosure provides: An acquisition means for acquiring road surface data in which the height of each point on the road surface is measured; A plurality of consecutive determination unit areas are set along the direction in which the road extends, and for each of the determination unit areas, an unevenness analysis means for analyzing the road surface data and calculating an unevenness index value representing the degree of unevenness of the road surface; A determination means for determining a pavement type of the road according to the magnitude of the unevenness index value; Equipped with 、 The determination means determines a pavement type of the determination unit area to be determined based on the unevenness index values ​​of a plurality of continuous determination unit areas including the determination unit area to be determined in an extension direction of the road. A road surface data processing device. Effect of the Invention

[0008] According to the present disclosure, there is an advantage that the pavement type can be easily identified. [Brief description of the drawings]

[0009] [Figure 1] FIG. 2 is a block diagram showing a functional configuration of the information processing device. [Diagram 2] 1 is a diagram illustrating identification of pavement types. [Diagram 3] FIG. 13 is a diagram illustrating setting of an evaluation region for the degree of unevenness. [Figure 4] FIG. 11 is a diagram showing a first example of determining a pavement type. [Diagram 5] FIG. 11 is a diagram showing a second example of determining the pavement type. [Figure 6] FIG. 11 is a diagram showing a third example of determination of pavement type. [Figure 7] 10 is a flowchart showing a control procedure for crack detection processing. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing the functional configuration of an information processing device 1 which is a road surface data processing device of this embodiment.

[0011] The information processing device 1 includes a control unit 11, a storage unit 12, an input / output interface (I / F) 13, a display unit 14, an operation reception unit 15, and the like.

[0012] The control unit 11 performs overall control of the operation of the information processing device 1. The control unit 11 has a processor that performs arithmetic processing. The processor may be a single general-purpose CPU (Central Processing Unit), or multiple CPUs that perform arithmetic processing in parallel or independently depending on the application. The processor may include a processor specialized for a specific arithmetic processing or image processing. The control unit 11 performs various control processing by reading and executing a program 121 from the storage unit 12.

[0013] The storage unit 12 has a RAM (Random Access Memory) and a non-volatile memory, and stores various data. The RAM provides a working memory space for the control unit 11 and stores temporary data. The non-volatile memory stores and holds the program 121, setting data, and the like. The non-volatile memory is, for example, a flash memory or an HDD (Hard Disk Drive), but is not limited to these. The storage unit 12 may have a ROM (Read Only Memory). An initial control program, and the like, can be stored in the ROM.

[0014] The input / output interface 13 inputs and outputs data between the information processing device 1 and the outside (including peripheral devices). The input / output interface 13 has a connection terminal 131 and a communication unit 132. The connection terminal 131 includes, for example, a Universal Serial Bus (USB) terminal and a Local Area Network (LAN) connector. The communication unit 132 controls communication in accordance with a communication rule (protocol) related to the LAN, such as TCP / IP.

[0015] The peripheral devices may include a database device 21, which is an auxiliary storage device, and an optical reader 22 that reads portable storage media (optical disks) such as CD-ROM, DVD, and Blu-ray (registered trademark). The portable storage media may include a magnetic tape, and the peripheral devices may include a reader that reads the magnetic tape.

[0016] Data that the information processing device 1 can obtain from the outside via the input / output interface 13 includes road height measurement data 201 (road surface data). The road height measurement data 201 is data on the road surface height of a road measured and calculated by a light cutting method based on the reflected light of a laser beam irradiated onto the road surface by a measuring device mounted on a traveling vehicle, and is integrated with vehicle position data and vehicle attitude data to generate distribution data of the three-dimensional height of the road surface. The database device 21 or the portable storage medium may store information such as the name, width, and number of lanes of the road to be measured in association with the road height measurement data 201.

[0017] The display unit 14 performs display on the display screen based on the control of the control unit 11. The display screen is, for example, a liquid crystal display or an organic EL (Electro-Luminescent) display, but is not limited to these.

[0018] The operation reception unit 15 receives an input operation from the outside, and outputs an operation signal corresponding to the input operation to the control unit 11. The operation reception unit 15 includes, for example, a keyboard and a pointing device. The pointing device may be a mouse. The display unit 14 and / or the operation reception unit 15 may be peripheral devices of the information processing device 1, and may be attached to the main body (computer) of the information processing device 1 including the control unit 11, the storage unit 12, and the input / output interface 13.

[0019] Next, the road height measurement data 201 will be described. As described above, the road surface measurement unit of the measurement device obtains the height distribution of the road surface along the measurement line by irradiating a linear laser beam extending in the width direction of the road perpendicular to the traveling direction of the vehicle, photographing the reflected light, and identifying the reflected position by the light section method. Since the geographical position of the measurement line is determined based on the position and attitude of the traveling vehicle, the position on the road where the height distribution is obtained is identified. By identifying such height distribution at appropriate time intervals while the vehicle is traveling, the height distribution of each point on the road surface, i.e., the distribution of three-dimensional positions, can be obtained with high accuracy. In particular, since such measurement by the light section method is performed by irradiating laser light, it is less susceptible to the influence of shadows generated on the road surface due to disturbances such as dirt and shadows, and allows for highly accurate analysis, compared to measurement by normal photography without irradiating laser light. The road height measurement data 201 is distribution data of the three-dimensional positions of this road surface.

[0020] The position of a moving vehicle is determined by, for example, satellite positioning, which receives radio waves from positioning satellites related to the Global Navigation Satellite System (GNSS) and calculates the absolute position on the Earth's surface. The attitude of a moving vehicle is determined by measuring the rotation of the moving vehicle (measurement device) in a plane parallel to the road surface, i.e., the vehicle's orientation, and the rotation in a plane including a direction perpendicular to the road surface, i.e., the vehicle's inclination. These measurements may be performed, for example, using an acceleration sensor that measures acceleration in three orthogonal axial directions and a gyro sensor that measures angular velocity in three orthogonal axial directions.

[0021] According to such a light-cutting method using laser light, a height distribution can be obtained with a height resolution of 1 mm or less on a scale of several mm in the horizontal direction, for example, every 4 mm square. Therefore, this light-cutting method can also detect minute cracks and depressions. Furthermore, in this embodiment, as described later, since it is necessary to appropriately quantitatively evaluate the degree of unevenness in the porous asphalt pavement, the height resolution needs to be higher than the accuracy required for detecting cracks. For example, the height resolution of the measuring device is sufficiently higher than the size of the aggregate in the porous asphalt pavement (for example, the maximum diameter is about 5 to 20 mm), and is 0.5 mm or less.

[0022] The properties of the road surface vary depending on the pavement material used, and the size of the cracks also varies depending on these properties. Concrete and asphalt are widely used as pavement materials. Asphalt pavement can be broadly divided into dense-grained asphalt pavement (dense-grained pavement) and porous asphalt pavement (porous pavement) depending on the size of the aggregate. Porous asphalt pavement is also called permeable pavement, drainage pavement, or high-performance pavement because of its good drainage properties.

[0023] In asphalt pavement, the unevenness of the surface varies greatly depending on the size of the aggregate. In porous asphalt pavement, the unevenness is large even in a normal state without cracks, depending on the size of the aggregate. In addition, even if the cracks are minor, they become wide and deep along the gaps between the aggregates. On the other hand, in dense asphalt pavement, the aggregates are small, so the unevenness is small in a normal state without cracks. In addition, even if cracks occur, the cracks are narrow and shallow along the gaps between the aggregates when they are minor. As a result, if cracks are detected using a uniform standard, it is easy to miss cracks in dense asphalt pavement, and it is easy to mistake normal parts for cracks in porous asphalt pavement. In concrete pavement, the size of the unevenness is small, about halfway between dense asphalt pavement and porous asphalt pavement, or about the same as dense asphalt pavement. Therefore, when detecting cracks, it is necessary to change the detection standard value for each type of pavement.

[0024] In order to change the reference value according to the pavement type, the type of pavement at each position where the height distribution of the road surface is measured must be specified. In this embodiment, the type of pavement of each road surface is specified by focusing on the magnitude of unevenness of the road surface, and a reference value according to the type of pavement is set, and then crack detection is performed.

[0025] FIG. 2 is a diagram explaining the identification of pavement types. The brightness of reflected light differs greatly between asphalt pavement and concrete pavement. Therefore, the control unit 11, as a determination means, distinguishes between asphalt pavement and concrete pavement based on the difference in brightness in the captured image. In this embodiment, since a laser light of a constant intensity is irradiated, the reflected light intensity is approximately constant for each material. The brightness can be expressed as luminance. The brightness is expressed in 256 gradations, for example, with the minimum value (black) being 0 and the maximum value (white) being 255, and is included in the above-mentioned road height measurement data 201. The control unit 11, as a brightness analysis means, analyzes the road height measurement data 201 and obtains (calculates) a representative value of the luminance value for each appropriate range (a determination unit area described later) as a brightness index value. The control unit 11, as a determination means, determines that the pavement type is concrete pavement if the brightness index value is greater than a reference value, and determines that the pavement type is either asphalt pavement if the brightness index value is equal to or less than the reference value.

[0026] As described above, the degree of surface unevenness differs between porous asphalt pavement and dense asphalt pavement. Therefore, when the pavement type is determined to be either asphalt pavement based on the brightness index value, the control unit 11, as a determination means, quantitatively evaluates the degree of unevenness to identify which type of pavement it is. As described above, the measurement by the light section method is less susceptible to the influence of shadows that occur on the road surface due to disturbances such as dirt and shadows, compared to measurement by normal photography without irradiation of laser light, and enables highly accurate analysis of unevenness. Therefore, the measurement data by the light section method is also preferably used for determining the type of pavement. The degree of unevenness may be divided into three or more types. For example, porous asphalt pavement may be further divided into a plurality of types according to the difference in aggregate size. In addition, in the case of concrete pavement, porous concrete pavement may be distinguished from normal concrete pavement. After determining whether the concrete pavement is a porous concrete pavement based on the brightness value as described above, if the degree of unevenness in the concrete pavement is equal to or greater than a certain reference value, the concrete pavement is determined to be a porous concrete pavement. If the degree of unevenness is less than the reference value, the concrete pavement is determined to be a normal concrete pavement.

[0027] Crack detection thresholds (minimum values) are set according to each of these pavement types. The detection thresholds include depth detection thresholds D1 to D3 and length detection thresholds L1 to L3. In addition, the detection thresholds may include width detection thresholds W1 to W3. As described above, the crack detection thresholds D1, L1, and W1 for porous asphalt pavement are greater than the crack detection thresholds D2, L2, and W2 for dense-grained asphalt pavement, respectively.

[0028] FIG. 3 is a diagram for explaining the setting of the evaluation region for the degree of unevenness. The degree of unevenness is obtained, for example, by analyzing the height (unevenness) of the road surface near the center of the lane where the influence of ruts and the like is small, that is, here, near the middle between the roadway center line Ec and the roadside demarcation line Ee, from the measurement data (road height measurement data 201) of the road surface of the road W. Here, for example, for the road height measurement data 201 acquired by the control unit 11 as an acquisition means, a determination unit area group As is determined by continuously arranging 1 meter square determination unit areas along the extension direction of the road. The determination unit area group As is specified as determination unit areas As0, As1, As2, ... Asi in order from an appropriate reference position (pavement type determination reference position). The control unit 11, as an unevenness analysis means, specifies the variation in the height of the road surface for each determination unit area as the degree of unevenness. The determination unit area is not limited to 1 meter square, but is preferably larger than the width of a sign painted on the road surface or the size of a structure such as a manhole. In addition, when the resolution of the original measurement data differs between the road width direction and the road extension direction, data averaged to match the low resolution side may be used. When the measurement points are arranged in 4 mm units in the horizontal direction as described above, each determination unit area Asi may include 256 x 256 measurement points, assuming a square of 1.024 meters.

[0029] For example, the variation in height (standard deviation) in a unit section can be used to evaluate the degree of unevenness. Alternatively, the mean profile depth (MPD) can be used to evaluate the degree of unevenness. Here, the unevenness analysis means calculates the standard deviation while excluding as much as possible the influence of ruts, garbage, fallen leaves, and the like. These ruts, garbage, fallen leaves, and the like have longer wavelengths than the unevenness of the paved road surface. Therefore, the unevenness analysis means analyzes the unevenness based on the predetermined high frequency components corresponding to the width of the unevenness of the road surface, that is, excluding the components (predetermined low frequency components) having longer wavelengths than the unevenness of the road surface. Filtering (high pass filter) to remove the low frequency components is performed by converting from the spatial domain to the frequency domain by Fourier transform. As described above, the number of measurement points in each judgment unit area Asi is made a power of two, so that it is efficiently converted to the frequency domain. Thereafter, the calculation of the standard deviation is performed directly in the frequency domain by convolution integral, thereby reducing the load of calculation. That is, in a two-dimensional frequency space, where k is the index corresponding to the cutoff frequency, the standard deviation σ can be calculated in the frequency domain of M rows by N columns only in the range of indices “k to Mk” and “k to Nk”, that is, in the range equal to or greater than the cutoff frequency, as shown in Equation 1 below. σ = ((NM-1) -1 Σ u=k~N-k Σ v=k~M-k As(u,v) As(Nu,Mv) 1 / 2 … (Formula 1)

[0030] By appropriately setting the reference value (irregularity reference value) of the unevenness index value, the control unit 11, as a judgment means, judges the pavement type. When the unevenness index value is equal to or greater than this reference value, the judgment means judges the pavement type as porous asphalt pavement, and when the unevenness index value is smaller than the reference value, the judgment means judges the pavement type as dense-grained asphalt pavement. The change in pavement type basically occurs along the extension direction of the road, except for temporary construction sites. Therefore, the type of asphalt pavement judged according to the unevenness index value for each judgment unit area is applied to the entire width direction including the evaluated judgment unit area. Note that in a portion where two corresponding lanes are completely separated by a median strip or the like, the two lanes may be treated as separate roads and the pavement type may be judged separately for each.

[0031] However, as described above, if there are obstacles such as signs and manholes on the road surface, the degree of unevenness may be significantly affected, leading to a misjudgment. On the other hand, the type of pavement usually changes on a scale sufficiently large compared to the judgment unit area, for example, at intervals of tens to tens of meters or more, so it is expected that the same judgment will continue to a certain extent in consecutive judgment unit areas Asi. In other words, if only a single or small number of judgment unit areas Asi consecutive in the extension direction are judged to be of a different pavement type from those before and after them, it can be judged that the misjudgment is due to the influence of the obstacle.

[0032] In order to avoid such erroneous determination, in this embodiment, the type of pavement in the judgment unit area Asj to be determined is determined based on a representative value of the unevenness index value in the judgment area Aj, which is a collection of a plurality of consecutive judgment unit areas Asi including the judgment unit area Asj to be determined. The judgment unit area Asj to be determined may be treated as a representative position of the judgment area Aj. For example, a set of 11 judgment unit areas Asi including five judgment unit areas Asi (j-5≦i≦j+5) before and after the judgment unit area Asj to be determined is defined as the judgment area Aj. The type of pavement in the judgment unit area Asj (representative position of the judgment area Aj) is determined using a representative value of the unevenness index value of each judgment unit area Asi included in this judgment area Aj, for example, an average value or a median value. In this way, a section of porous asphalt pavement including a plurality of judgment unit areas Asi in which the same judgment (porous asphalt pavement) is consecutively obtained is divided into a section of dense asphalt pavement including a plurality of judgment unit areas Asi in which the same judgment (dense asphalt pavement) is consecutively obtained. Since the judgment unit areas Asi to be judged are shifted one by one, the unevenness index values ​​of the judgment unit areas Asi included in the judgment area Aj (j=0, 1, 2...) can be easily and appropriately retained and a representative value can be obtained by adding and deleting them one by one by, for example, FIFO (First-in First-Out). Similarly, for the brightness index value, a determination can be made as to whether the judgment unit area Asj to be judged is asphalt pavement or concrete pavement based on the representative value of the brightness index values ​​of the multiple (11) judgment unit areas Asi included in the judgment area Aj.

[0033] Fig. 4 is a diagram showing a first example of pavement type determination. In Fig. 4(a), a group of determination unit areas As extends along the vicinity of the middle between the center line Ec of the roadway and a division line Ee of one of the roadside strips. For a determination unit area Aso to be determined, a set of eleven consecutive determination unit areas Asi, each of which is ±5 in the extension direction of the road, is determined as a determination area Aо to be associated with the determination unit area Aso.

[0034] Figure 4(b) is a diagram showing the brightness index value (representative value of luminance value) and unevenness index value of each judgment unit area Asо and the judgment area Aо corresponding to the judgment unit area Asо versus position (distance) in the extension direction of the road. For example, when the judgment unit area Asо is located on the boundary line B between the porous asphalt pavement area Pa and the dense asphalt pavement area Pb, the brightness index value of each judgment unit area Asо shown by the short dashed line does not change on either side of the boundary line B and is lower than the reference value Lth shown by the long dashed line. Accordingly, the brightness index value (representative value) of each judgment area Aо shown by the solid line also remains lower than the reference value Lth. Therefore, it is determined that none of the judgment unit areas Asо to which each judgment area Aо corresponds is a concrete pavement area. The reference value Lth may depend on the intensity of the irradiated light and the sensitivity of the captured image, but is set to, for example, 200 for the brightness value of the above 256 gradations. On the other hand, the unevenness index value of each judgment unit area Asо shown by the short dashed line is relatively higher in the porous asphalt pavement area Pa across the boundary line B than in the dense asphalt pavement area Pb. In other words, the unevenness index value is clearly different on either side of the boundary line B. Accordingly, the unevenness index value (representative value) of each judgment area Aо shown by the solid line gradually rises from the average value of the dense asphalt pavement area Pb to the reference value Fth on one side of the boundary line B, and further rises from the reference value Fth to the average value of the porous asphalt pavement area Pa on the other side of the boundary line B. Therefore, it can be seen that the index values ​​on the left and right of the boundary line B represent each area. It is possible to identify which asphalt pavement it is based on the magnitude relationship between the unevenness index value and an appropriate reference value Fth shown by the long dashed line. The reference value Fth is, for example, 0.8 mm.

[0035] The pavement type identified for the determination unit area Asо is determined as the pavement type for the entire range C (full width) of the road in the width direction, including the determination unit area Asо. In other words, it is not assumed here that multiple pavement types are mixed in the width direction.

[0036] FIG. 5 shows a second example of pavement type determination. The determination unit area group As is set in the same way as in the first example. As shown in FIG. 5(a), in this second example, there are two boundary lines B1 and B2, and the area between the boundary lines B1 and B2 is a concrete pavement area Pc. The left side of the boundary line B1 and the right side of the boundary line B2 are dense asphalt pavement areas Pb.

[0037] As shown in FIG. 5(b), in the concrete pavement area Pc, the brightness index value (short dashed line) in each judgment unit area Asо continues to be significantly higher than in the dense asphalt pavement area Pb. The distance between the boundaries B1 and B2 is about 8 m, and the brightness index value of the judgment area A shown by the solid line is also higher than the reference value Lth shown by the long dashed line. Therefore, it is determined that the increase in the brightness index value is not due to a local obstacle, but is due to a continuous change in the pavement type.

[0038] On the other hand, the unevenness index value does not show any significant change across the boundaries B1, B2 and remains lower than the reference value Fth. The representative value of the unevenness index value in each judgment area Ao also continues to be lower than the reference value Fth. Since the difference between the unevenness index value of the concrete pavement area Pc and the unevenness index value of the dense asphalt pavement area Pb is not systematically determined, when the brightness index value is larger than the reference value Lth as described above, the unevenness index value does not need to be taken into consideration when determining the pavement type.

[0039] Fig. 6 shows a third example of pavement type determination. In the third example, the determination unit area group As is set on the opposite lane to the first and second examples, but the setting method is the same. As shown in Fig. 6(a), in the third example, the pavement type is dense asphalt pavement overall, but road signs (road markings) such as a crosswalk E1 and a stop line E2 are shown on the road surface.

[0040] Road signs formed on the road surface are white or yellow and have high light reflectance, and the brightness index value in the judgment unit area Asi that includes this road sign is likely to rise. As shown in FIG. 6(b), the brightness index value (short dashed line) of the judgment unit area Asо that includes the crosswalk E1 or the stop line E2 is locally high and exceeds the reference value Lth. However, there is only one judgment unit area Asо that includes the stop line E2, and there are also four judgment unit areas Asо that include the crosswalk E1 (two of which are only partial). Therefore, the brightness index value (solid line) in this area, for example, in the judgment unit area Aо corresponding to the judgment unit area Aso in FIG. 6(a) does not exceed the reference value Lth. That is, these judgment unit areas Asо are not erroneously determined to be concrete pavement based on the index value of the corresponding judgment area Aо.

[0041] As described above, when the type of pavement is determined based on the brightness (brightness value) of the reflected light and the degree of unevenness (unevenness index value), a crack detection threshold (parameter) that is predetermined according to the determined type of pavement is acquired. The control unit 11, as a crack detection means, detects points that are lower in height than other points by the detection threshold or more in the linear measurement point group (measurement line) by each scan as crack points using the acquired detection threshold. Note that points with relatively low road surface heights may also include those due to unevenness caused by the inclination of the road surface itself or ruts. Therefore, the control unit 11 subtracts the average height within a predetermined range around the crack determination point (moving average value within a range set wide enough to remove unevenness caused by ruts) from the height of the determination point, and then compares it with the detection threshold. As a result, gradual changes in height such as the inclination of the road surface and ruts are excluded from detection.

[0042] Cracks are often continuous across multiple measurement points. Such continuous crack points are identified and classified as being in the same group (crack group). Classification is determined, for example, by whether or not there are other crack points within a reference distance from a certain crack point. The reference distance is, for example, preset to 15 mm. A crack point that has other crack points within the reference distance is placed in the same group as the other crack points (all of them, if there are multiple crack points). Of the cracks grouped in this way, crack points that belong to those whose length or number of crack points is smaller than the reference may be excluded from the crack detection points.

[0043] The crack detection results are displayed by the display unit 14 or the like. The three-dimensional positions of each measurement point on the road surface are projected onto a two-dimensional surface seen from an arbitrary viewpoint, a three-dimensional crack diagram is generated, and this three-dimensional crack diagram is displayed on the display screen. Alternatively, only a plan view (planar rectangular coordinate system) seen from above may be displayed. In addition, if visible light images of the road itself and its surroundings are stored, these images may be projected and displayed in an overlapping manner. At this time, each of the crack groups identified above may be highlighted in an appropriate color. In addition, the characteristic quantities related to the cracks of each crack group, such as the maximum crack depth and width and length, may be displayed by a balloon display or the like.

[0044] FIG. 7 is a flowchart showing a control procedure of the crack detection process executed by the control unit 11 (acquisition means, unevenness analysis means, determination means, crack detection means) in the information processing device 1 of this embodiment. The control contents of the crack detection processing, including the road surface data processing method of this embodiment, are included in the program 121, and are executed by specifying the file of the entire road height measurement data 201 to be subjected to crack detection or specifying a partial area within the road height measurement data 201, and accepting an input operation of an execution command to the operation receiving unit 15 by the user.

[0045] The acquisition means acquires road height measurement data 201 of a specified file or area (step S1; acquisition step). The unevenness analysis means sets a pavement type determination reference position (step S2). Specifically, the unevenness analysis means determines the start end of the road extension direction in the acquired road height measurement data 201 as the pavement type determination reference position.

[0046] The unevenness analysis means sets a judgment unit area and a judgment area (step S3). Specifically, the unevenness analysis means determines a strip area of ​​about 1 m (e.g., 1.024 m) width centered on the data of the center of the width direction of the road in the acquired road height measurement data 201, and divides the strip area at intervals of about 1 m (e.g., 1.024 m) in the extension direction of the road starting from the pavement type judgment reference position to set judgment unit areas Asi (i = 0, 1, 2, ...). Then, the unevenness analysis means determines judgment unit areas Asj (i = 5, 6, 7 ...) to be judged, and for each judgment unit area Asj, determines a set of the judgment unit area Asj and five judgment unit areas Asi before and after it, totaling 11 judgment unit areas Asi, as a judgment area Aj. As a result, a judgment unit area and a judgment area of ​​about 1 m width are set near the center of the lane.

[0047] The unevenness analysis means calculates a brightness index value of the judgment unit area, and calculates a representative value of the brightness index values ​​in the judgment area corresponding to the judgment unit area (step S4). As described above, the brightness index value is a luminance value, and is a representative value of the luminance values ​​at each measurement point in the judgment unit area. The brightness index value of the judgment area is a further representative value of the representative values ​​of each judgment unit area. Note that, before calculating the representative value, obvious error data, etc. may be excluded. In this case, the data of the excluded points may be interpolated using the data of surrounding points.

[0048] The unevenness analysis means calculates an unevenness index value for the judgment unit area, and calculates a representative value of the unevenness index values ​​in the judgment unit area corresponding to the judgment unit area (step S5). The unevenness index value for the judgment area is a representative value of each unevenness index value in the judgment unit area within the judgment area. Note that, before calculating the unevenness index value, if an error value indicating an abnormality in the height of the road surface is included in the judgment unit area, this error value may be removed in advance. Data of the excluded points may be interpolated using data of surrounding points.

[0049] The determination means determines the pavement type according to the brightness index value and the roughness index value obtained for the determination area (step S6). The determination means associates the pavement type of the determination result with the position information of the determination unit area (specified by satellite positioning) and stores them in the memory unit 12 or an external database device 21 (step S7). When the same pavement type continues in multiple determination unit areas, the determination means may store the coordinates of the first and last determination unit areas in association with each other to obtain section information for more easily identifying sections where the same pavement type continues.

[0050] The crack detection means sets a detection threshold for crack detection according to the determined pavement type (step S8). The detection threshold is any one of the combinations (D1, L1, W1), (D2, L2, W2), and (D3, L3, W3) as described above. The crack detection means detects cracks based on the height distribution along the measurement line including the determination unit area by the measurement device (step S9). As described above, the control unit 11 may detect cracks based on the height distribution along multiple measurement lines in both the front and rear. The crack detection means stores the detected three-dimensional crack position in the memory unit 12 or the database device 21 (step S10).

[0051] The control unit 11 determines whether crack detection has been completed throughout the entire target range of crack detection (step S11). If it is determined that crack detection has not been completed (step S11; NO), the processing of the control unit 11 returns to step S3. If it is determined that crack detection has been completed (step S11; YES), the control unit 11 causes the display unit 14 to display the crack detection results (step S12). Alternatively, the control unit 11 may generate display control data for displaying the detection results on an external display device and output the data to the external display device. Then, the control unit 11 ends the crack detection processing. The processes in steps S2 to S5 are included in the unevenness analysis step in the road surface data processing method of this embodiment. The processes in steps S6 to S7 are included in the determination step in the road surface data processing method of this embodiment. The processes in steps S8 to S10 are included in the crack detection step in the road surface data processing method of this embodiment.

[0052] The present invention is not limited to the above-described embodiment, but may be modified in various ways. For example, in the above embodiment, high-pass filtering is performed in the frequency domain when calculating the unevenness index value, but this is not limited to this. For example, data of high frequency components may be extracted by subtracting data obtained by smoothing spatial domain data from the original spatial domain data. The data may be smoothed by applying a low-pass filter in the frequency domain, or by calculating a moving average value of a predetermined range around each target point.

[0053] Furthermore, when calculating the standard deviation of height, it is not necessary to perform convolution integration in the frequency domain. The data after high-pass filtering may simply be subjected to an inverse Fourier transform to return it to the spatial domain, and the standard deviation of height may be calculated for each determination unit region.

[0054] Moreover, instead of the two-dimensional Fourier transform, a wavelet transform may be used to perform transformation into the frequency domain, frequency filtering, and the like.

[0055] In addition, in the above crack detection, the local drop in road surface height is simply compared with the crack detection threshold, but this is not limited to the above. For example, a machine learning model may be used to detect cracks. The machine learning model may be generated and used according to the pavement type as described above, or may be a combination of multiple models weighted appropriately according to the pavement type. Alternatively, the input variables to the machine learning model may include the pavement type or the brightness index value and the unevenness index value. Also, instead of the unevenness index value, an identification value representing the pavement type may be included in the input variables. In this case, preprocessing such as calculating a separate local average and subtracting it when detecting cracks may not be performed.

[0056] In the above, the determination unit areas are arranged in a line in the direction of the road, but two or more lines may be arranged in parallel. For each range in the direction of the road, the unevenness index values ​​of a plurality of determination unit areas may be used in combination to determine the pavement type.

[0057] In the above description, the representative value, for example, the average value or the median value of the unevenness index values ​​of the 11 judgment unit areas Asi is used as the unevenness index value in the judgment area Ai formed by combining the 11 judgment unit areas Asi, but the present invention is not limited to this. For example, the 11 unevenness index values ​​may be weighted according to the distance from the central judgment unit area Asi to calculate a weighted average.

[0058] Also, instead of using the unevenness index value (representative value) in the judgment area, the boundary between the pavement types may be specified based on the continuity of the pavement types judged in each judgment unit area. In other words, a section judged to be the same pavement type for a predetermined number of consecutive times or more may be judged to be a section of that pavement type.

[0059] In the above description, the storage unit 12 consisting of a non-volatile memory such as an HDD or a flash memory is used as an example of a computer-readable medium for storing the program 121 relating to the pavement type determination and crack detection control of the present invention, but is not limited to this. As other computer-readable media, other non-volatile memories such as MRAM and portable storage media such as CD-ROMs and DVD disks can be applied. In addition, a carrier wave is also applied to the present invention as a medium for providing the data of the program according to the present invention via a communication line. In addition, the specific configurations, contents and procedures of the processing operations, etc. shown in the above embodiments can be modified as appropriate without departing from the spirit of the present invention. The scope of the present invention includes the scope of the invention described in the claims and its equivalents.

[0060] As described above, the information processing device 1, which is a road surface data processing device of this embodiment, includes the control unit 11. The control unit 11, as an acquisition means, acquires road height measurement data 201 in which the height of each point on the road surface is measured. The control unit 11, as an unevenness analysis means, analyzes the road height measurement data 201 and calculates an unevenness index value that indicates the degree of unevenness of the road surface. The control unit 11, as a determination means, determines the pavement type of the road depending on the magnitude of the unevenness index value. This information processing device 1 can easily determine the pavement type of a road using the road height measurement data 201. Therefore, there is no need to manually input and set the pavement type of the road. In addition, there is no need to add additional data for determining the pavement type, which saves time and effort. Furthermore, as a result, the information processing device 1 can determine crack detection standards according to the pavement type based on the determination result of the pavement type, and detect cracks with greater accuracy.

[0061] The unevenness analysis means may calculate the unevenness index value based on a predetermined high frequency component in the road height measurement data 201. This allows the information processing device 1 to reduce the influence of ruts and fallen leaves on the road surface in determining the pavement type.

[0062] The unevenness analysis means may also calculate the unevenness index value by converting the distribution of road surface height in the road height measurement data 201 into a frequency domain, convoluting high frequency components equal to or higher than a predetermined cutoff frequency in the frequency domain, and calculating the standard deviation of the height distribution. In this way, the information processing device 1 does not need to perform an inverse Fourier transform on the frequency domain data after high-pass filtering to return it to spatial domain data, and can therefore obtain the unevenness index value with less effort.

[0063] In addition, the determination means determines that the road is a dense-grained asphalt pavement when the unevenness index value is less than a predetermined unevenness reference value, and determines that the road is a porous asphalt pavement when the unevenness index value is equal to or greater than the unevenness reference value. In other words, the information processing device 1 can easily classify dense-grained asphalt pavement and porous asphalt pavement based on the unevenness index value. Therefore, the determination is very easy.

[0064] The unevenness analysis means sets a number of consecutive determination unit areas along the extension direction of the road, and the determination means determines the pavement type for each of the multiple determination unit areas, and can divide the road height measurement data 201 into sections with the same pavement type. In this way, the road can be divided into areas with the same pavement type, so the information processing device 1 can easily identify the distribution of the pavement condition. This allows the information processing device 1 to easily determine the detection of cracks collectively for each pavement type classification.

[0065] Furthermore, the control unit 11 may detect crack points on the road surface by analyzing each of the above sections using a detection threshold (parameter) that is predetermined according to the pavement type of the section as a crack detection means. The information processing device 1 can easily classify the pavement types in this way and detect cracks in each type. Therefore, the information processing device 1 can reduce false detections and missed detections according to the crack size corresponding to the pavement type.

[0066] Moreover, the acquisition means acquires road height measurement data 201 including the brightness of each point. The control unit 11, as brightness analysis means, analyzes the brightness indicated by the road height measurement data 201 to calculate a brightness index value of the road surface. The determination means performs a determination when the brightness index value is equal to or less than a predetermined brightness reference value. In this way, the information processing device 1 can determine whether the pavement type is asphalt pavement or concrete pavement according to the reflected light intensity of the road surface. By such a determination, asphalt pavement is extracted, and then it is determined whether it is dense asphalt pavement or porous asphalt pavement as described above, so that it is possible to avoid erroneously identifying sections as concrete pavement.

[0067] The road surface data processing method of this embodiment also includes the following steps: (1) an acquisition step of acquiring road height measurement data 201 in which the height of each point on the road surface is measured, (2) an unevenness analysis step of analyzing the road height measurement data 201 and calculating an unevenness index value that represents the degree of unevenness of the road surface, and (2) a determination step of determining the pavement type of the road depending on the magnitude of the unevenness index value. According to this road surface data processing method, the pavement type of the road can be easily determined using the road height measurement data 201. Therefore, the pavement type corresponding to each measurement point of the road can be easily obtained. In particular, since the pavement type can be determined only using the data used for crack detection, there is no need to prepare and process other data, and the effort is reduced.

[0068] Furthermore, by installing and executing the program 121 relating to the above-mentioned road surface data processing method on a computer (including the control unit 11, memory unit 12, and input / output interface 13), the pavement type can be easily determined without the need for any special configuration and without any hassle. [Explanation of symbols]

[0069] 1. Information processing device 11 Control section 12 Storage section 121 Programs 13 Input / Output Interface 131 Connection terminal 132 Communications Department 14 Display section 15 Operation reception section 21 Database equipment 22 Optical reading device 201 Measurement Data

Claims

1. An acquisition means for acquiring road surface data in which the height of each point on the road surface is measured; an unevenness analysis means for setting a plurality of consecutive determination unit areas along an extension direction of the road, analyzing the road surface data for each of the determination unit areas, and calculating an unevenness index value that represents the degree of unevenness of the road surface; A determination means for determining a pavement type of the road according to the magnitude of the unevenness index value; Equipped with The determination means determines a pavement type of the determination unit area to be determined based on the unevenness index values ​​of a plurality of continuous determination unit areas including the determination unit area to be determined in an extension direction of the road. Road surface data processing device.

2. A road surface data processing device as described in claim 1, wherein the judgment means calculates a representative value of the unevenness index value in a plurality of consecutive judgment unit areas including the judgment unit area of ​​the judgment target, and judges the pavement type of the judgment unit area of ​​the judgment target based on the representative value.

3. The road surface data processing device of claim 2, wherein the judgment means judges that the judgment unit area of ​​the object to be judged is dense-grained pavement if the representative value is less than a predetermined unevenness standard value, and judges that the judgment unit area of ​​the object to be judged is porous pavement if the representative value is equal to or greater than the unevenness standard value.

4. A road surface data processing device as described in claim 1, wherein the judgment means judges the judgment unit area to be the target of judgment to be of the same pavement type when a predetermined number or more of consecutive individual judgment unit areas including the judgment unit area to be judged are of the same pavement type based on the unevenness index values ​​of those multiple consecutive judgment unit areas.

5. The road surface data processing device according to any one of claims 1 to 4, wherein the unevenness analysis means converts the height distribution in the road surface data into a frequency domain, performs a convolution integral on high frequency components equal to or higher than a predetermined cutoff frequency in the frequency domain, and calculates the standard deviation of the height distribution to calculate the unevenness index value.

6. The determination means determines the pavement type by setting each of the plurality of consecutive determination unit areas as the determination target, and divides the road surface data into sections in which the same pavement type is consecutive, The road surface data processing device includes a crack detection means for detecting a crack point where a crack has occurred on the road surface by analyzing each of the sections using parameters that are predetermined according to the pavement type of the section.

2. The road surface data processing device according to claim 1.

7. The acquisition means acquires the road surface data including the brightness of each of the points, The road surface data processing device includes a brightness analysis means for analyzing the luminance indicated by the road surface data and calculating a brightness index value of the road surface, The determination means performs the determination when the brightness index value is equal to or less than a predetermined brightness reference value.

2. The road surface data processing device according to claim 1.

8. an acquisition step of acquiring road surface data in which the height of each point on the road surface is measured; an unevenness analysis step of setting a plurality of consecutive determination unit areas along an extension direction of the road, analyzing the road surface data for each of the determination unit areas, and calculating an unevenness index value representing the degree of unevenness of the road surface; a determining step of determining a pavement type of the road according to the magnitude of the unevenness index value; Including, In the determination step, a pavement type of the determination unit area to be determined is determined based on the unevenness index values ​​of a plurality of continuous determination unit areas including the determination unit area to be determined in an extension direction of the road. Road surface data processing method.

9. Computer, An acquisition means for acquiring road surface data in which the height of each point on the road surface is measured; an unevenness analysis means for setting a plurality of consecutive determination unit areas along an extension direction of the road, analyzing the road surface data for each of the determination unit areas, and calculating an unevenness index value representing the degree of unevenness of the road surface; A determination means for determining a pavement type of the road in accordance with the magnitude of the unevenness index value; Functioning as a The determination means determines a pavement type of the determination unit area to be determined based on the unevenness index values ​​of a plurality of continuous determination unit areas including the determination unit area to be determined in an extension direction of the road. program.

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