KR20200009726A - Pavement condition index (pci) detection system using hyper-spectral sensor mounted on unmanned air vehicle, and method for the same - Google Patents

Abstract

Provided are a pavement condition index (PCI) measurement system using a hyper-spectral sensor mounted on an unmanned aerial vehicle and a method thereof. According to the present invention, the hyper-spectral sensor is mounted on a drone, the unmanned aerial vehicle, and therefore hyper-spectral data of the pavement surface is obtained. The distribution characteristics of the hyper-spectral data are analyzed and the PCI of the pavement surface is measured, so that a deterioration state of the pavement surface can be investigated in a short time. In addition, by using the hyper-spectral sensor mounted on the unmanned aerial vehicle, an aging state of a plurality of road pavement surfaces can be examined, and therefore overlay priority of the road pavement surface can be determined. Accordingly, the efficiency of pavement investigation can be improved.

Description

Pavement condition index measurement system using hyperspectral sensor mounted on unmanned aerial vehicle and method thereof {PAVEMENT CONDITION INDEX (PCI) DETECTION SYSTEM USING HYPER-SPECTRAL SENSOR MOUNTED ON UNMANNED AIR VEHICLE, AND METHOD FOR THE SAME}

The present invention relates to a pavement index measurement system, and more particularly, after the hyperspectral sensor (Hyper-Spectral Sensor) is mounted on the drone, which is an unmanned aerial vehicle, to obtain hyperspectral data on the pavement surface, The present invention relates to a pavement state index measurement system using a hyperspectral sensor mounted on an unmanned aerial vehicle, and a method for measuring a paving condition index (PCI) of a road pavement by analyzing distribution characteristics of spectral data.

Recently, the aging of existing pavement is progressing rapidly due to the progress of pavement work for pavement, and as a result, the cracks generated on the road surface of highways and national roads are also at an alarming level. In particular, recently, various new materials and new construction methods for highway and national road pavement have been actively applied, and it is necessary to verify site safety.

Considering the aging trend of existing highway and national road pavement and the increase of new pavement, the road crack analysis device should be developed to improve road safety. However, in the case of the road crack analysis device used in Korea, it was difficult to maintain the device because it was not able to accurately measure the crack condition of the road and was not a domestically produced device.

In addition, road repair costs such as overlapping increased sharply in the 1990s and gradually increased in the 2000s, but the percentage of pavement repairs used for road pavement was decreasing. Since then, the trend has been increasing.

In addition, road pavement of industrial facilities such as highways, airport runways, and the like may be cracked depending on time and environment, so maintenance and management of existing pavement facilities is an important concern.

Conventionally, a method for reliably measuring cracks in a paving facility has not been devised, and a specific investigator with experience in crack inspection visually checks the paving facility, and then checks the paved road in 'good condition' and 'normal state'. , 'Broken' and so on. However, this method has a lack of objectivity of crack detection, which makes it difficult to systematically manage the packaging facilities.

As a prior art for solving this problem, Korean Patent No. 10-298601 discloses a invention named "packaging crack measurement and analysis system", which will be described with reference to FIG.

1 is a view for explaining a pavement crack measurement and analysis system according to the prior art.

As shown in a) and b) of FIG. 1, in the case of a pavement crack measurement and analysis system according to the related art, a support 12 protruding rearward by a predetermined length L1 is provided on an upper portion of the vehicle 11. The digital camera 14 is attached to the end of the support 12 so as to face downward so that the rear road surface 13 of the traveling vehicle 11 can be photographed.

Here, the length L1 of the support 12 from the rear upper part of the vehicle 11 to the place where the digital camera 14 is installed is approximately 100 cm, and is the lowest end of the digital camera 14 installed on the upper end of the support 12. The length L4 from the road surface 13 to the road surface 13 may be 200 cm, and the photographing area L3 of the road surface 13 photographed by the digital camera 14 may be 400 cm.

An image of the road surface 13 photographed by the digital camera 14 is connected to be transmitted to the MPEG encoder 17 and the image board 15, and the MPEG encoder 17 is compressed by the MPEG encoder 17. Connected image is supplied to the recording mechanism to be stored in the storage medium (18).

In addition, the image board 15 that receives the image of the road surface from the digital camera 14 is connected to the image processor 16 so as to segment and transmit only the frame of the image of the crack generated image of the road surface, the crack The monitor 19 for displaying the analyzed result is connected to the image processor 16 for analyzing the generated image.

Specifically, according to the conventional pavement crack measurement and analysis system, the crack is scanned from the road surface through a digital camera installed at the rear of the vehicle, and only the image having the crack in the scanned road image is segmented by the frame unit, the crack state As a method of analyzing, the pavement management system can provide analysis data analyzing the crack state generated on the road surface to accurately maintain the highway or the national road according to the crack formed on the road surface.

According to the prior art, the survey of the same point is carried out every three years by sequentially executing national roads using road pavement survey vehicles, and use the road pavement survey vehicles to cover all lanes one by one. The aging state of the road surface, for example, cracks, flatness, and the like are investigated.

However, the road paving survey vehicle according to the prior art is carried out by collecting field data for each lane and analyzing the collected data by manpower, resulting in an inefficiency of excessive research time.

In addition, the current pavement survey is conducted every three years by road using a survey vehicle in the case of a general national road. There is a disadvantage that an unnecessary investigation period takes too long.

Republic of Korea Patent No. 10-1636650 (application date: May 4, 2015), the title of the invention: "Airport concrete pavement index analysis device and concrete pavement index analysis method using the same" Republic of Korea Patent No. 10-1812566 (Application Date: August 26, 2016), the title of the invention: "Prevention Method and Prediction System for Road Pavement Damage Risk" Republic of Korea Patent No. 10-1394244 (filed December 12, 2013), the title of the invention: "Multiple image acquisition device and mobile road surface automatic detection system using the same" Republic of Korea Patent No. 10-1339399 (application date: November 28, 2011), the title of the invention: "Airport pavement status information providing apparatus and method" Republic of Korea Patent No. 10-298601 (filed February 25, 1999), the title of the invention: "Packaging crack measurement and analysis system" Republic of Korea Patent No. 10-1619836 (filed February 5, 2016), the title of the invention: "Superspectroscopic remote monitoring system using a drone" Republic of Korea Patent No. 10-333781 (application date: January 3, 2000), the title of the invention: "Automatic measurement system for longitudinal longitudinal and transverse irregularities"

The technical problem to be solved by the present invention for solving the above problems is to mount the ultra-spectral sensor on the drone, which is an unmanned aerial vehicle, to obtain the hyperspectral data of the pavement surface, and to analyze the distribution characteristics of the hyperspectral data to pave the road To provide a pavement condition index measurement system and method using a hyperspectral sensor mounted on an unmanned aerial vehicle that can investigate the aging state of the pavement surface by measuring the pavement condition index (PCI) of the cotton will be.

Another technical problem to be achieved by the present invention is to use the hyperspectral sensor mounted on the unmanned aerial vehicle to investigate the aging state of the pavement surface, the super-spectral mounted on the unmanned aerial vehicle, which can determine the priority of the overlay of the pavement surface The present invention provides a pavement index measurement system using a sensor and a method thereof.

As a means for achieving the above technical problem, the pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the present invention, pavement road along the flight trajectory so that the hyperspectral sensor can photograph the pavement surface A drone flying to access a plane; A hyperspectral sensor mounted on the unmanned aerial vehicle so as to acquire hyperspectral data on the pavement surface and photographing the pavement surface; A hyperspectral data collector configured to collect hyperspectral data photographed by a hyperspectral sensor mounted on the unmanned aerial vehicle; A spectral distribution characteristic representative value selecting unit for selecting a spectral distribution characteristic representative value of the pavement surface according to a ratio of the hyperspectral wavelength band reflectance from the hyperspectral data collected by the hyperspectral data collector; A correlation model construction unit for preparing a scatter diagram between the selected spectral distribution characteristic representative value and the pavement state index reference value and deriving a regression equation that is a correlation model; And a pavement state index estimator configured to estimate a pavement state index using a regression equation derived from the correlation model construction unit and a hyperspectral distribution characteristic representative value collected using the unmanned aerial vehicle, wherein the hyperspectral sensor mounted on the unmanned aerial vehicle is included. By examining the aging state of the road pavement surface for the entire lane at a time, and to determine the aging state of the road pavement surface in accordance with the estimated pavement state index.

Here, the hyperspectral data acquired by the hyperspectral sensor may be an image having tens to hundreds of spectral bands in a continuous narrow wavelength region.

Here, the spectral distribution characteristic representative value selecting unit selects the spectral distribution characteristic representative value of the pavement surface by using the ratio of the ultra-spectral wavelength band reflectance of 400 to 500 nm and 800 to 900 nm and / or 2100 to 2200 nm and 2300 to 2400 nm. Can be selected.

The correlation model construction unit may include: a spectral distribution scatter plot generation unit for preparing a scatter diagram of the spectral distribution characteristic representative value selected by the spectral distribution characteristic representative value selecting unit and the pavement state index reference value; A database for storing the pavement state index measurement value and the spectral distribution characteristic representative value; And a correlation model for deriving a regression equation that is a correlation model through correlation analysis using a pavement state index as a dependent variable according to the scatter plot prepared by the spectral distribution scatter plot creator and an independent variable of the spectral distribution characteristic representative value measured using an unmanned aerial vehicle. It may include a derivation unit.

Here, the pavement state index reference value in the spectral distribution scatter plot creation unit may be a value obtained through a road survey vehicle and a manpower survey.

Pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the present invention, the pavement overlay priority for a plurality of road pavement surface according to the pavement state index of the road pavement surface The package may further include a package state index visualization unit for visualizing and displaying the state index.

The pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the present invention is an unmanned aerial vehicle for remotely instructing dispatch and return of the unmanned aerial vehicle, flight, charging, road pavement recognition and road pavement imaging. It may further include a flight and control terminal.

Here, the unmanned aerial vehicle includes a wireless communication module for receiving a remote control signal from the unmanned aerial vehicle flight and control terminal and transmitting hyperspectral data photographed by the hyperspectral sensor; Controls a flight unit according to a remote control signal received through the wireless communication module, controls the operation of the hyperspectral sensor, and controls the transmission of the hyperspectral data captured by the hyperspectral sensor through the wireless communication module. A control unit; A memory for storing hyperspectral data on the pavement surface photographed by the hyperspectral sensor; A flight unit driven under the control of the controller to fly the unmanned aerial vehicle according to the remote control signal transmitted to the unmanned aerial vehicle flight and the control terminal; And a battery for supplying power to the wireless communication module, the controller, the memory, the flight unit, and the hyperspectral sensor.

On the other hand, as another means for achieving the above technical problem, the pavement state index measurement method using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the present invention, a) mounting the hyperspectral sensor on the unmanned aerial vehicle; b) acquiring hyperspectral data by photographing a pavement surface of the hyperspectral sensor along a flight trajectory of the unmanned aerial vehicle; c) collecting, by a manager terminal, the hyperspectral data from the unmanned aerial vehicle; d) selecting a representative value of the spectral distribution characteristic of the hyperspectral data; e) deriving a regression equation that is a correlation model for pavement state index and spectral distribution characteristic representative value; And f) estimating the pavement state index from the hyperspectral distribution characteristic representative value collected by using the derived regression equation and the unmanned aerial vehicle, wherein the pavement index is mounted on the unmanned aerial vehicle through the superspectral sensor mounted on the unmanned aerial vehicle. The aging state of the cotton is examined at a time, and the aging state of the pavement surface is characterized in accordance with the estimated pavement state index.

According to the present invention, a superspectral sensor is mounted on an unmanned aerial drone to acquire hyperspectral data on a pavement surface, and the distribution characteristics of the hyperspectral data are analyzed to determine a pavement condition index on a pavement surface. ), The aging of the pavement surface can be investigated in a short time.

According to the present invention, it is possible to determine the overriding priority of the pavement surface by investigating the deterioration state of the pavement surface by using the hyperspectral sensor mounted on the unmanned aerial vehicle, thereby improving the efficiency of the pavement survey. .

1 is a view for explaining a pavement crack measurement and analysis system according to the prior art.
2 is a configuration diagram of a pavement state index measurement system using a hyperspectral sensor mounted on an unmanned aerial vehicle according to an embodiment of the present invention.
3A and 3B are diagrams illustrating a spectral distribution and a correlation model in a pavement index measurement system using a hyperspectral sensor mounted on an unmanned aerial vehicle according to an embodiment of the present invention, respectively.
4 is a view for explaining in detail the unmanned aerial vehicle equipped with the hyperspectral sensor in the pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention.
5 is a view for explaining in detail the construction of the correlation model in the pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention.
6 is a diagram illustrating a hyperspectral image measured by a hyperspectral sensor in a pavement index measurement system using a hyperspectral sensor mounted on an unmanned aerial vehicle according to an exemplary embodiment of the present invention.
7A and 7B are diagrams illustrating the spectral distribution and correlation model for the hyperspectral image shown in FIG. 6, respectively.
8 is a diagram illustrating a pavement state index measurement result in a pavement state index measurement system using a hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention.
FIG. 9 is a diagram illustrating a pavement overlay priority selected by visualizing a pavement state index in a pavement state index measurement system using a hyperspectral sensor mounted on an unmanned aerial vehicle according to an exemplary embodiment of the present invention.
10 is a flowchart illustrating a method of measuring a pavement state index using a hyperspectral sensor mounted on an unmanned aerial vehicle according to an exemplary embodiment of the present invention.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and like reference numerals designate like parts throughout the specification.

Throughout the specification, when a part is said to "include" a certain component, it means that it can further include other components, except to exclude other components unless specifically stated otherwise. In addition, terms such as "... unit" described in the specification means a unit for processing at least one function or operation, which may be implemented in hardware or software or a combination of hardware and software.

[Pavement State Index Measurement System Using Hyperspectral Sensor mounted on Unmanned Aerial Vehicle]

2 is a configuration diagram of a pavement index measurement system using a hyperspectral sensor mounted on an unmanned aerial vehicle according to an embodiment of the present invention, Figures 3a and 3b are respectively mounted on the unmanned aerial vehicle according to an embodiment of the present invention A diagram illustrating spectral distribution and correlation model in a pavement index measurement system using a hyperspectral sensor.

Referring to FIG. 2, the pavement index measurement system 100 using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the embodiment of the present invention includes an unmanned aerial vehicle 110 and a hyperspectral sensor 120. , Hyperspectral data collection unit 130, spectral distribution characteristic representative value selection unit 140, correlation model construction unit 150, pavement state index (PCI) estimation unit 160, pavement state index (PCI) visualization unit 170 And a drone flight and control terminal 180. At this time, the hyperspectral data collection unit 130, the spectral distribution characteristic representative value selection unit 140, the correlation model construction unit 150, the packaging state index (PCI) estimation unit 160 and the packaging state index (PCI) visualization unit 170 may be implemented as one manager terminal.

The unmanned aerial vehicle 110, for example, a drone may fly to approach the pavement surface 200 along a flight trajectory so that the hyperspectral sensor 120 may photograph the pavement surface 200.

The unmanned aerial vehicle flight and control terminal 180 remotely instructs the dispatch and return of the unmanned aerial vehicle 110, flight, charging, road pavement recognition and road pavement imaging. At this time, the unmanned aerial vehicle flight and control terminal 180 is equipped with each algorithm and processor capable of remotely instructing the dispatch and return of the unmanned aerial vehicle 110, flight, charging, road pavement recognition, road pavement image shooting It is. Therefore, the unmanned aerial vehicle 110 dispatched by the command of the unmanned aerial vehicle flight and the steering terminal 180 flies to approach the road pavement surface 200.

The hyperspectral sensor 120 is mounted on the unmanned aerial vehicle 110 so as to acquire a hyperspectral image of the pavement surface 200 to photograph the pavement surface 200. Specifically, the hyperspectral sensor 120 is a device for acquiring continuous and complete spectral information of an indicator corresponding to each pixel constituting an image. In general digital aerial photography, red, green, and blue ( Although only a wavelength region corresponding to blue) is acquired and visualized as a color image, the hyperspectral data of the hyperspectral sensor 120 may be an image having tens to hundreds of spectral bands in a continuous narrow wavelength region.

The hyperspectral data collection unit 130 collects the hyperspectral data captured by the hyperspectral sensor 120 mounted on the unmanned aerial vehicle 110.

The spectral distribution characteristic representative value selecting unit 140 selects the spectral distribution characteristic representative value of the pavement surface 200 according to the ratio of the hyperspectral wavelength band reflectance from the hyperspectral data collected by the hyperspectral data collection unit 130. . At this time, the spectral distribution characteristic representative value selection unit 140, as shown in Figure 3a, of the ultra-high wavelength wavelength reflectance of 400 to 500 nm and 800 to 900 nm and / or 2100 to 2200 nm and 2300 to 2400 nm A representative value of the spectral distribution characteristic of the road pavement surface 200 may be selected using the ratio.

The correlation model construction unit 150 creates a plot of the selected spectral distribution characteristic representative value and the pavement state index reference value, and derives a regression equation that is a correlation model. Specifically, as shown in FIG. 3b, the pavement state index (PCI) is set as the dependent variable (y) according to the prepared spectral distribution characteristic scatter diagram, and the spectral distribution characteristic representative value measured using the unmanned aerial vehicle 110 is independent. Correlation analysis using the variable (x) can lead to a regression equation that is a correlation model.

The PCI estimator 160 uses the regression equation derived from the correlation model construction unit 150 and the hyperspectral distribution characteristic representative value collected by using the unmanned aerial vehicle 110. Estimate

The pavement state index (PCI) visualization unit 170 is the pavement state to determine the priority of pavement over the plurality of road pavement surface 200 according to the pavement state index (PCI) of the road pavement surface 200 The exponent (PCI) is visualized and displayed.

Accordingly, the pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention, the road for the entire lane through the hyperspectral sensor 120 mounted on the unmanned aerial vehicle 110. The aging state of the pavement surface 200 may be examined at a time, and the aging state of the road pavement surface 200 may be determined according to the estimated pavement state index (PCI).

On the other hand, Figure 4 is a view for explaining in detail the unmanned aerial vehicle equipped with the hyperspectral sensor in the pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention, a ) Is a configuration diagram of the drone, and b) of FIG. 4 is a photograph illustrating the drone.

Referring to Figure 4a), the unmanned aerial vehicle 110 is a wireless communication module 111, the control unit 112 in the pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention. And a memory 113, a flight unit 114, and a battery 115, and as shown in b) of FIG. 4, the unmanned aerial vehicle 110 includes an unmanned aerial vehicle body, a propeller motor, a propeller, and a landing support. It may include.

The hyperspectral sensor 120 is rotatably mounted in the unmanned aerial vehicle 110 to acquire hyperspectral data by photographing the road pavement surface 200.

The wireless communication module 111 receives a remote control signal from the unmanned aerial vehicle flight and control terminal 180 and transmits hyperspectral data captured by the hyperspectral sensor 120.

The controller 112 controls the flight unit 114 according to the remote control signal received through the wireless communication module 111, controls the driving of the hyperspectral sensor 120, and the hyperspectral sensor 120. It is implemented by the MCU to control the transmission of the hyperspectral data photographed from the wireless communication module 111, the memory 113 stores the hyperspectral data photographed by the hyperspectral sensor 120.

The flight unit 114 is driven under the control of the controller 112 to fly the unmanned aerial vehicle 110 according to the remote control signal transmitted from the unmanned aerial vehicle flight and the steering terminal 180.

The battery 115 supplies power to the wireless communication module 111, the control unit 112, the memory 113, the flight unit 114, and the hyperspectral sensor 120.

The unmanned aerial vehicle flight and control terminal 180 remotely instructs the dispatch and return of the unmanned aerial vehicle 110, flight, charging, road pavement recognition and image recording. At this time, the unmanned aerial vehicle flight and control terminal 180 is equipped with an algorithm and a processor for instructing the dispatch and return of the unmanned aerial vehicle 110, flight, charging, road pavement recognition, road pavement image shooting.

Therefore, the unmanned aerial vehicle 110 driven by the command of the unmanned aerial vehicle flight and the control terminal 180 is flying close to the road pavement surface 200 along the flight trajectory, the hyperspectral sensor 120 is the road pavement surface The hyperspectral image is taken to check for an abnormality of the 200.

On the other hand, Figure 5 is a view for explaining in detail the construction of the correlation model in the pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention.

As shown in FIG. 5, in the pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention, the correlation model construction unit 150 includes a spectral distribution scatter plotter 151 and a database. (DB: 152) and the correlation model derivation unit 153 may be included.

The spectral distribution scatter plot generator 151 creates a plot of the spectral distribution characteristic representative value selected by the spectral distribution characteristic representative value selecting unit 140 and the pavement state index reference value.

A database (DB) 152 stores the PSI measurement value and the spectral distribution characteristic representative value.

The correlation model derivation unit 153 uses the unmanned aerial vehicle 110 to determine the pavement state index (PCI) according to the scatter plot prepared by the spectral distribution scatter plot generator 151 and to measure the spectral distribution characteristics. A regression equation, which is a correlation model, is derived through a correlation analysis using the representative value as an independent variable (x), wherein the reference value of the pavement state index in the spectral distribution scatter plotter 151 is a value obtained through a road survey vehicle and a manpower survey. Can be.

On the other hand, Figure 6 is a diagram illustrating the hyperspectral image measured by the hyperspectral sensor in the pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention, Figures 7a and 7b Are diagrams illustrating the spectral distribution and correlation model for the hyperspectral image shown in FIG. 6, respectively.

As shown in FIG. 6, the hyperspectral image measured by the hyperspectral sensor in the pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the embodiment of the present invention is the first to fourth surface. It may be a spectra, specifically the Good Pavement shown in FIG. 6 a), the old crack seal shown in FIG. 6 b), the new crack seal shown in FIG. 6 c). (New Crack Seal) and the crack seal (Crack Seal) shown in FIG. 6 d), and may be poor in the order of the first to fourth surface spectra.

In addition, corresponding to the first to fourth surface spectra, as shown in FIG. 7A, the ultra-spectral wavelength band reflectances of 400 to 500 nm and 800 to 900 nm and / or 2100 to 2200 nm and 2300 to 2400 nm are shown. A representative value of the spectral distribution characteristic of the road pavement surface 200 may be selected using the ratio. Here, the wavelength range of 400 to 500 nm means that any value of the corresponding band can be utilized.

In addition, a plot of plotting the selected spectral distribution characteristic representative value and the pavement state index reference value is prepared, and as shown in FIG. 7B, the pavement state index (PCI) is determined as a dependent variable ( y), and the regression equation, which is a correlation model, can be derived through a correlation analysis using the spectral distribution characteristic representative value measured using the unmanned aerial vehicle 110 as an independent variable (x).

On the other hand, Figure 8 is a diagram illustrating a pavement state index measurement results in the pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention.

In the pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the embodiment of the present invention, the pavement state index (PCI) estimator 160, as shown in FIG. The packing state index (PCI) may be estimated using the regression equation derived from the unit 150 and the hyperspectral distribution characteristic representative value collected using the unmanned aerial vehicle 110.

On the other hand, Figure 9 is a diagram showing the selection of the packaging overlay index by visualizing the packaging state index in the packaging state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention.

In the pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the embodiment of the present invention, the pavement index (PCI) visualization unit 170, as shown in Figure 9, the road pavement surface The paved state index (PCI) is visualized and displayed to determine the paving priority of the plurality of road pavement surfaces 200 according to the paved state index (PCI) of the 200.

As a result, in the case of the pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle according to the embodiment of the present invention, by using the unmanned aerial vehicle, the road pavement surface is roughly 1/15 compared with the irradiation time of the conventional irradiation method. It takes a degree, and can also be diversified and advanced using an unmanned aerial vehicle.

[Method of Measuring Pavement State Index Using Hyperspectral Sensor 120 Mounted on Unmanned Aerial Vehicle]

10 is a flowchart illustrating a method of measuring a pavement state index using a hyperspectral sensor mounted on an unmanned aerial vehicle according to an exemplary embodiment of the present invention.

Referring to FIG. 10, a pavement state index measurement method using a hyperspectral sensor mounted on an unmanned aerial vehicle according to an embodiment of the present invention, first, the hyperspectral sensor 120 is mounted on the unmanned aerial vehicle 110 ( S110).

Next, the hyperspectral sensor 120 photographs the pavement surface 200 along the flight trajectory of the unmanned aerial vehicle 110 to obtain hyperspectral data (S120). In this case, the hyperspectral data acquired by the hyperspectral sensor 120 may be an image having tens to hundreds of spectral bands in a continuous narrow wavelength region.

Next, a manager terminal collects the hyperspectral data from the unmanned aerial vehicle 110 (S130).

Next, the spectral distribution characteristic representative value of the hyperspectral data is selected (S140). For example, the spectral distribution characteristic representative value of the road pavement 200 can be selected using the ratio of the ultra-spectral wavelength band reflectance of 400 to 500 nm and 800 to 900 nm and / or 2100 to 2200 nm and 2300 to 2400 nm. Can be.

Next, a regression equation which is a correlation model for the pavement state index (PCI) and the spectral distribution characteristic representative value is derived (S150). Specifically, plotting a plot between the selected spectral distribution characteristic representative value and the pavement state index reference value is made, and the pavement state index (PCI) is set as the dependent variable (y) according to the scatter plot, and the unmanned aerial vehicle 110 is used. The regression equation, which is a correlation model, is derived through a correlation analysis using the spectral distribution characteristic representative value measured as an independent variable (x), wherein the pavement index reference value may be a value obtained through a road survey vehicle and a manpower survey. .

Next, the pavement state index (PCI) is estimated from the hyperspectral distribution characteristic representative value collected by using the derived regression equation and the unmanned aerial vehicle 110 (S160).

Next, the paved state index (PCI) is visualized and displayed to determine the priority of the pavement of the road pavement surface 200 (S170).

Accordingly, the aging state of the pavement surface 200 for the entire lane is examined at one time through the hyperspectral sensor 120 mounted on the unmanned aerial vehicle 110, and the estimated pavement state index (PCI) is examined. Accordingly, it is possible to grasp the aging state of the road pavement surface 200.

As a result, according to an embodiment of the present invention, a hyperspectral sensor is mounted on a drone that is an unmanned aerial vehicle to obtain hyperspectral data on a pavement surface, and the distribution characteristics of the hyperspectral data are analyzed to make a pavement. By measuring the Pavement Condition Index (PCI) of the surface, it is possible to investigate the aging condition of the road pavement surface in a short time, and also use the ultra-spectral sensor mounted on the unmanned aerial vehicle. We can determine the priority of overlaying road pavement by examining.

On the other hand, pavement state index measurement system and method using the hyperspectral sensor mounted on the unmanned aerial vehicle according to an embodiment of the present invention after performing the first irradiation (wide / short term) using the drone of the unmanned aerial vehicle (110) Subsequently, only the sections requiring 20-40% of detailed surveys can be carried out using road pavement survey vehicles to maximize the efficiency of pavement surveys. It can also be applied in a way that replaces or complements the existing porthole occurrence status identification system.

The above description of the present invention is intended for illustration, and it will be understood by those skilled in the art that the present invention may be easily modified in other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is shown by the following claims rather than the above description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present invention. do.

100: pavement index measurement system 200: pavement surface
110: drone
120: Hyper Spectral Sensor
130: hyperspectral data collection unit
140: spectral distribution characteristic representative value selection unit
150: correlation model construction unit
160: PIC estimation unit
170: Packaging State Index (PCI) Visualization
180: drone flight and control terminal
111: wireless communication module 112: control unit
113: memory 114: flight unit
115: battery
151: Spectral distribution scatter plot creation unit 152: Database (DB)
153: correlation model derivation unit

Claims (14)

An unmanned aerial vehicle 110 that flies to approach the pavement surface 200 along the flight trajectory so that the hyperspectral sensor 120 may photograph the pavement surface 200;
A hyperspectral sensor (120) mounted on the unmanned aerial vehicle 110 to acquire hyperspectral data on the pavement surface 200 and photographing the pavement surface 200;
A hyperspectral data collector 130 for collecting hyperspectral data captured by the hyperspectral sensor 120 mounted on the unmanned aerial vehicle 110;
A spectral distribution characteristic representative value selecting unit (140) for selecting a spectral distribution characteristic representative value of the pavement surface (200) according to a ratio of hyperspectral wavelength band reflectivity from the hyperspectral data collected by the hyperspectral data collection unit (130);
A correlation model construction unit 150 for preparing a scatter plot between the selected spectral distribution characteristic representative value and the pavement state index reference value and deriving a regression equation that is a correlation model; And
The pavement state index estimator 160 estimates the pavement state index (PCI) using the regression equation derived from the correlation model construction unit 150 and the hyperspectral distribution characteristic representative value collected using the unmanned aerial vehicle 110. Including,
The super-spectral sensor 120 mounted on the unmanned aerial vehicle 110 examines the aging state of the road pavement surface 200 for the entire lane at one time, and paves the road according to the estimated pavement state index (PCI). Pavement state index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle, characterized in that to determine the aging state of the surface (200). The method of claim 1,
The hyperspectral data acquired by the hyperspectral sensor 120 is a continuous and narrow wavelength region, and the pavement state index measurement system using the hyperspectral sensor mounted on an unmanned aerial vehicle, characterized in that the image having tens to hundreds of spectral bands. . The method of claim 1,
The spectral distribution characteristic representative value selecting unit 140 utilizes a ratio of the ultra-spectral wavelength band reflectances of 400 to 500 nm and 800 to 900 nm and / or 2100 to 2200 nm and 2300 to 2400 nm. Pavement condition index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle, characterized in that the representative value of the spectral distribution characteristic is selected. The method of claim 1, wherein the correlation model construction unit 150,
A spectral distribution scatter plot preparation unit 151 for preparing a scatter plot between the spectral distribution characteristic representative value selected by the spectral distribution characteristic representative value selecting unit 140 and the pavement state index reference value;
A database (DB) 152 for storing the PCI value and the spectral distribution characteristic representative value; And
According to the scatter plot prepared by the spectral distribution scatter plotter 151, the pavement state index (PCI) is a dependent variable (y), and the spectral distribution characteristic representative value measured by using the unmanned aerial vehicle (110) as an independent variable (x). Pavement condition index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle including a correlation model derivation unit 153 for deriving a regression equation that is a correlation model through a correlation analysis. The method of claim 4, wherein
The pavement state index reference value in the spectral distribution scatter plot creation unit 151 is a pavement state index measurement system using a super-spectral sensor mounted on an unmanned aerial vehicle, characterized in that the value obtained through a road survey vehicle and manpower survey. The method of claim 1,
Pavement state index to visualize and display the pavement state index (PCI) to determine the priority of pavement over the plurality of road pavement surface 200 according to the pavement state index (PCI) of the road pavement surface (200) (PCI) Pavement index measurement system using the hyperspectral sensor mounted on the unmanned aerial vehicle further including a visualization unit 170. The method of claim 1,
Ultra-spectral mounted on an unmanned aerial vehicle further including an unmanned aerial vehicle flying and steering terminal 180 for remotely instructing the return and flight of the unmanned aerial vehicle, flight, charging, road pavement recognition and road pavement image recording. Pavement index measurement system using sensors. The method of claim 7, wherein the unmanned aerial vehicle 110,
A wireless communication module 111 for receiving a remote control signal from the unmanned aerial vehicle flight and control terminal 180 and transmitting hyperspectral data captured by the hyperspectral sensor 120;
Controls the flight unit 114 according to the remote control signal received through the wireless communication module 111, controls the driving of the hyperspectral sensor 120, and hyperspectral photographed from the hyperspectral sensor 120 A control unit 112 for controlling data transmission through the wireless communication module 111;
A memory 113 for storing hyperspectral data on the pavement surface 200 photographed by the hyperspectral sensor 120;
A flight unit 114 driven under the control of the control unit 112 to fly the unmanned aerial vehicle 110 according to the remote control signal transmitted to the unmanned aerial vehicle flight and the steering terminal 180; And
A hyperspectral sensor mounted on an unmanned aerial vehicle including a battery 145 for supplying power to the wireless communication module 111, the controller 112, the memory 113, the flight unit 114, and the hyperspectral sensor 120. Pavement Index Index System a) mounting the hyperspectral sensor 120 on the unmanned aerial vehicle 110;
b) acquiring hyperspectral data by photographing the pavement surface 200 by the hyperspectral sensor 120 along the flight trajectory of the unmanned aerial vehicle 110;
c) collecting, by a manager terminal, the hyperspectral data from the unmanned aerial vehicle (110);
d) selecting a representative value of the spectral distribution characteristic of the hyperspectral data;
e) deriving a regression equation that is a correlation model for the PIC and spectral distribution characteristic representative value; And
f) estimating a pavement state index (PCI) from the hyperspectral distribution characteristic representative value collected using the derived regression equation and the unmanned aerial vehicle 110,
The super-spectral sensor 120 mounted on the unmanned aerial vehicle 110 examines the aging state of the road pavement surface 200 for the entire lane at one time, and paves the road according to the estimated pavement state index (PCI). Pavement state index measurement method using the hyperspectral sensor mounted on the unmanned aerial vehicle, characterized in that to grasp the aging state of the surface (200). The method of claim 9,
The hyperspectral data acquired by the hyperspectral sensor 120 in step b) is an image having a spectral band of several tens to hundreds of spectral bands in a continuous and narrow wavelength range. State index measurement method. The method of claim 9,
In step d), the spectral distribution characteristic representative value of the pavement surface 200 is selected by using the ratio of the ultra-spectral wavelength band reflectance of 400 to 500 nm and 800 to 900 nm and / or 2100 to 2200 nm and 2300 to 2400 nm. Pavement condition index measurement method using a hyperspectral sensor mounted on an unmanned aerial vehicle. The method of claim 9,
In step e), a plot of the selected spectral distribution characteristic representative value and the pavement state index reference value is prepared, and the pavement state index (PCI) is defined as the dependent variable (y) according to the scattering plot, and the unmanned aerial vehicle ( Pavement condition index measurement using hyperspectral sensor mounted on an unmanned aerial vehicle, characterized by deriving a regression equation, which is a correlation model, through correlation analysis using the spectral distribution characteristic representative value measured using 110) as an independent variable (x). Way. The method of claim 12,
The pavement state index reference value is a pavement state index measurement method using a hyperspectral sensor mounted on the unmanned aerial vehicle, characterized in that the value obtained through the road survey vehicle and manpower survey. The method of claim 9,
g) pavement state index using the hyperspectral sensor mounted on the unmanned aerial vehicle further comprising the step of visualizing and displaying the pavement state index (PCI) to determine the priority of the pavement of the road pavement surface 200; How to measure.

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