Information processing method and information processing apparatus

Abstract

To improve a board thickness estimation technique.SOLUTION: Provided is an information processing method for estimating the board thickness of a portion to be inspected in a facility structure, and the method includes: calculating a correction coefficient on the basis of surface roughness, lightness information, and the curvature of the portion to be inspected that are specified on the basis of a photographed image of the portion to be inspected; estimating the board thickness of the portion to be inspected on the basis of the correction coefficient and a board thickness measured value measured by an ultrasonic sensor; and outputting the board thickness of the portion to be inspected.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an information processing method and an information processing device related to a plate thickness estimation technique. [Background technology]

[0002] Conventionally, the invention described in Patent Document 1 is known as a method for inspecting piping for abnormalities. Patent Document 1 describes a method for identifying the three-dimensional shape of a piping using a visible light camera and a thermal camera, and detecting abnormalities caused by dust adhesion inside the piping, wear of the piping, and thin-walled areas caused by corrosion based on the temperature distribution. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-148732 Summary of the Invention [Problem to be solved by the invention]

[0004] The invention in Patent Document 1 detects piping abnormalities based on the temperature distribution captured by a thermal camera, but it is known that the thermal image depends on the illuminance of the measurement environment, making the evaluation of the corrosion condition relative, and there is a problem in that it is not possible to accurately determine the degree of corrosion progress inside the pipe. In other words, there is room for improvement in the plate thickness estimation technology.

[0005] In view of the above circumstances, an object of the present disclosure is to improve plate thickness estimation technology. [Means for solving the problem]

[0006] (1) An information processing method according to an embodiment of the present disclosure includes: An information processing method for estimating a plate thickness of an inspection target portion in a facility structure, comprising: Calculating a correction coefficient based on a surface roughness and brightness information identified based on a photographed image of the inspection target area, and a curvature of the inspection target area; Estimating a plate thickness of the inspection target portion based on the correction coefficient and a plate thickness measurement value measured by an ultrasonic sensor; Outputting the plate thickness of the inspection target portion; Includes.

[0007] (2) An information processing method according to an embodiment of the present disclosure is the information processing method according to (1), The method is characterized in that a plate thickness reduction rate is calculated based on the plate thickness and the original thickness of the inspected portion, and when the plate thickness reduction rate exceeds a threshold value, it is estimated whether the cause of the plate thickness reduction is internal corrosion or external corrosion based on the surface roughness.

[0008] (3) An information processing method according to an embodiment of the present disclosure is an information processing method according to (1) or (2), The present invention is characterized in that information indicating the plate thickness is superimposed on a two-dimensional or three-dimensional image of the facility structure and displayed on a display unit.

[0009] (4) An information processing method according to an embodiment of the present disclosure is the information processing method according to (2), The present invention is characterized in that information indicating the plate thickness reduction rate is superimposed on a two-dimensional or three-dimensional image of the facility structure and displayed on a display unit.

[0010] (5) An information processing method according to an embodiment of the present disclosure is an information processing method according to (4), The present invention is characterized in that, at locations in the two-dimensional or three-dimensional image of the facility structure where the plate thickness reduction rate exceeds the threshold value, information indicating whether the cause of the plate thickness reduction is internal corrosion or external corrosion is superimposed.

[0011] (6) An information processing device according to an embodiment of the present disclosure, An information processing device that includes a control unit and estimates a plate thickness of an inspection target portion in a facility structure, the control unit comprising: Calculating a correction coefficient based on the surface roughness and brightness information identified based on the photographed image of the inspection target area, and the curvature of the inspection target area; The plate thickness of the inspection target portion is estimated based on the correction coefficient and the plate thickness measurement value measured by the ultrasonic sensor, The plate thickness of the inspected portion is output.

[0012] (7) An information processing device according to an embodiment of the present disclosure is the information processing device according to (6), The control unit is further characterized in that it calculates a plate thickness reduction rate based on the plate thickness and the original thickness of the inspected portion, and when the plate thickness reduction rate exceeds a threshold value, it estimates whether the cause of the plate thickness reduction is internal corrosion or external corrosion based on the surface roughness.

[0013] (8) An information processing device according to an embodiment of the present disclosure is an information processing device according to (6) or (7), The control unit is further characterized in that the information indicating the plate thickness is superimposed on a two-dimensional or three-dimensional image of the facility structure and displayed on the display unit.

[0014] (9) An information processing device according to an embodiment of the present disclosure is the information processing device according to (7), The control unit is further characterized in that information indicating the plate thickness reduction rate is superimposed on a two-dimensional or three-dimensional image of the facility structure and displayed on a display unit.

[0015] (10) An information processing device according to an embodiment of the present disclosure is the information processing device according to (9), The control unit is further characterized in that, at locations in the two-dimensional or three-dimensional image of the equipment structure where the plate thickness reduction rate exceeds the threshold value, information indicating whether the cause of the plate thickness reduction is internal corrosion or external corrosion is superimposed and displayed on the display unit. Effect of the Invention

[0016] According to one embodiment of the present disclosure, thickness estimation techniques can be improved. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 illustrates a structure inspection system according to an embodiment of the present disclosure. [Diagram 2] 1 is a flowchart illustrating an operation of an information processing device according to an embodiment of the present disclosure. [Diagram 3] FIG. 11 is a diagram showing an example of a display output mode. [Figure 4] 10 is a flowchart showing a process for estimating whether the cause of the reduction in plate thickness is internal corrosion or external corrosion. [Diagram 5] FIG. 11 is a diagram showing an example of a display output mode. [Figure 6] FIG. 11 is a diagram showing an example of a display output mode. [Figure 7] FIG. 1 is a diagram showing a configuration of an embodiment according to the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] A structure inspection system 1 according to an embodiment of the present disclosure will be described below with reference to the drawings. Note that, although a pipe installed outdoors will be described as an example of an object to be inspected by the structure inspection system 1 of the present disclosure, the present invention can be suitably used not only for pipes but also for ducts, chimneys, and plate thickness measurements of facilities and equipment such as frames of steel structures. The structure inspection system 1 of the present disclosure can be suitably used both indoors and outdoors.

[0019] In each drawing, the same or corresponding parts are denoted by the same reference numerals. In the description of this embodiment, the description of the same or corresponding parts will be omitted or simplified as appropriate.

[0020] 1 shows an example of the configuration of a structure inspection system 1 according to this embodiment. The structure inspection system 1 includes an unmanned photography device 10, a remote controller 20, and an information processing device 30. The unmanned photography device 10, the remote controller 20, and the information processing device 30 are connected to each other so as to be able to communicate with each other.

[0021] The unmanned photography device 10 may be, for example, an unmanned aerial vehicle. The unmanned aerial vehicle includes a drone. In the following, in this embodiment, a case where the unmanned photography device 10 is a drone will be described, but the unmanned photography device 10 is not limited to this.

[0022] The remote controller 20 is a communications terminal equipped with functions for operating and monitoring the unmanned photography device 10 and used by operators (hereinafter simply referred to as operators) such as supervisors of the unmanned photography device 10 and other related parties.

[0023] The information processing device 30 is a communication terminal used by an operator, such as a tablet terminal. The functions of the remote controller 20 and the information processing device 30 may be configured by a single communication terminal. For example, the information processing device 30 may have a function of operating and monitoring the unmanned photography device 10.

[0024] First, an overview of this embodiment will be described, and details will be described later. The unmanned photography device 10 photographs an inspection target portion in a facility structure and generates a photographed image. The unmanned photography device 10 transmits the photographed image to the information processing device 30. The unmanned photography device 10 also measures the plate thickness of the inspection target portion by an ultrasonic sensor and transmits the measured plate thickness measurement value to the information processing device 30. The unmanned photography device 10 also measures brightness information of the measurement environment by an illuminance sensor and transmits the measured brightness information to the information processing device 30. The information processing device 30 calculates a correction coefficient based on the surface roughness identified based on the photographed image of the inspection target portion, the brightness information of the measurement environment, and the curvature of the inspection target portion. The information processing device 30 estimates the plate thickness of the inspection target portion based on the correction coefficient and the plate thickness measurement value measured by the ultrasonic sensor. The information processing device 30 then outputs the plate thickness of the inspection target portion.

[0025] In this way, according to the structure inspection system 1 of this embodiment, the plate thickness of the inspected portion is estimated by correcting the plate thickness measurement value of the inspected portion measured by the ultrasonic sensor with a correction coefficient calculated from the surface roughness, brightness information, and curvature of the inspected portion. Since the plate thickness measurement value of the inspected portion measured by the ultrasonic sensor is affected by the surface roughness, brightness information, and curvature of the inspected portion, the plate thickness estimation technology can be improved by using the correction coefficient determined by these factors to improve the accuracy of the plate thickness estimation.

[0026] Next, each component of the structure inspection system 1 will be described in detail.

[0027] (Configuration of the unmanned photography device 10) As shown in FIG. 1, the unmanned photography device 10 includes a control unit 11, a storage unit 12, a communication unit 13, a positioning unit 14, an imaging unit 15, an ultrasonic sensor 16, and an illuminance sensor 17.

[0028] The control unit 11 includes at least one processor, at least one dedicated circuit, or a combination of these. The processor is a general-purpose processor such as a central processing unit (CPU) or a graphics processing unit (GPU), or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The control unit 11 executes processes related to the operation of the unmanned photography device 10 while controlling each part of the unmanned photography device 10.

[0029] The storage unit 12 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or a combination of at least two of these. The semiconductor memory is, for example, a random access memory (RAM) or a read only memory (ROM). The RAM is, for example, a static random access memory (SRAM) or a dynamic random access memory (DRAM). The ROM is, for example, an electrically erasable programmable read only memory (EEPROM). The storage unit 12 functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 12 stores data used in the operation of the unmanned photography device 10 and data obtained by the operation of the unmanned photography device 10.

[0030] The communication unit 13 includes at least one wireless communication interface. The communication unit 13 receives data used in the operation of the unmanned photography apparatus 10, and transmits data obtained by the operation of the unmanned photography apparatus 10.

[0031] The positioning unit 14 includes a receiving device compatible with a satellite positioning system. The receiving device is compatible with, for example, a GPS (Global Positioning System), but is not limited thereto, and may be compatible with any satellite positioning system. The positioning unit 14 also includes, for example, a gyro sensor, a geomagnetic sensor, and an air pressure sensor. In this embodiment, the unmanned photography device 10 can obtain its own photography position information, the direction in which the device is facing, and the inclination of the device using the positioning unit 14. The position information may include two-dimensional coordinate data including latitude and longitude, or may include three-dimensional coordinate data including altitude in addition to latitude and longitude.

[0032] The imaging unit 15 includes an imaging device such as a camera that captures an image of an object. The imaging device may be configured with, for example, a Complementary Metal Oxide Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD).

[0033] The ultrasonic sensor 16 is a sensor that uses ultrasonic waves to measure the distance and position of an object. The ultrasonic sensor 16 includes an ultrasonic transmitting device and a receiving device. The transmitting device transmits ultrasonic waves toward the object. The receiving device receives the reflected waves from the object.

[0034] The illuminance sensor 17 is a sensor that measures the brightness and illuminance of the surrounding light. The illuminance sensor 17 includes a light receiving device. The light receiving device includes a photodiode and a phototransistor.

[0035] (Configuration of the remote controller 20) As shown in FIG. 1, the remote controller 20 includes a control unit 21, a storage unit 22, a communication unit 23, and an input unit 24.

[0036] The control unit 21 includes at least one processor, at least one dedicated circuit, or a combination of these. The processor is a general-purpose processor such as a central processing unit (CPU) or a graphics processing unit (GPU), or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The control unit 21 executes processes related to the operation of the remote controller 20 while controlling each part of the remote controller 20.

[0037] The storage unit 22 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or a combination of at least two of these. The semiconductor memory is, for example, a random access memory (RAM) or a read only memory (ROM). The RAM is, for example, a static random access memory (SRAM) or a dynamic random access memory (DRAM). The ROM is, for example, an electrically erasable programmable read only memory (EEPROM). The storage unit 22 functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 22 stores data used in the operation of the remote controller 20 and data obtained by the operation of the remote controller 20.

[0038] The communication unit 23 includes at least one wireless communication interface. The communication unit 23 receives data used in the operation of the remote controller 20, and transmits data obtained by the operation of the remote controller 20.

[0039] The input unit 24 includes at least one input interface. The input interface is, for example, a physical key, a capacitive key, a pointing device, or a touch screen that is integrated with a display. The input unit 24 accepts an operation for inputting data used for the operation of the remote controller 20.

[0040] (Configuration of information processing device 30) As shown in FIG. 1, the information processing device 30 includes a control unit 31, a storage unit 32, a communication unit 33, an input unit , and a display unit .

[0041] The control unit 31 includes at least one processor, at least one dedicated circuit, or a combination of these. The processor is a general-purpose processor such as a central processing unit (CPU) or a graphics processing unit (GPU), or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The control unit 31 executes processes related to the operation of the information processing device 30 while controlling each unit of the information processing device 30.

[0042] The storage unit 32 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or a combination of at least two of these. The semiconductor memory is, for example, a random access memory (RAM) or a read only memory (ROM). The RAM is, for example, a static random access memory (SRAM) or a dynamic random access memory (DRAM). The ROM is, for example, an electrically erasable programmable read only memory (EEPROM). The storage unit 32 functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 32 stores data used in the operation of the information processing device 30 and data obtained by the operation of the information processing device 30.

[0043] The communication unit 33 includes at least one wireless communication interface. The communication unit 33 receives data used in the operation of the information processing device 30, and transmits data obtained by the operation of the information processing device 30.

[0044] The input unit 34 includes at least one input interface. The input interface is, for example, a physical key, a capacitive key, a pointing device, or a touch screen integrally provided with a display. The input interface may also be, for example, a microphone that accepts voice input, or a camera that accepts gesture input. The input unit 34 accepts an operation to input data used for the operation of the information processing device 30. The input unit 34 may be connected to the information processing device 30 as an external input device instead of being provided in the information processing device 30. As a connection method, any method such as USB (Universal Serial Bus), HDMI (registered trademark) (High-Definition Multimedia Interface), or Bluetooth (registered trademark) can be used.

[0045] The display unit 35 includes at least one display output interface. The display output interface is, for example, a display that outputs information as a video. The display is, for example, a liquid crystal display (LCD) or an organic EL (electro luminescence) display. The display unit 35 displays and outputs data obtained by the operation of the information processing device 30. The display unit 35 may be connected to the information processing device 30 as an external output device instead of being provided in the information processing device 30. As a connection method, any method such as USB, HDMI (registered trademark), or Bluetooth (registered trademark) can be used.

[0046] The functions of the information processing device 30 are realized by executing a program according to this embodiment on a processor equivalent to the information processing device 30. That is, the functions of the information processing device 30 are realized by software. The program causes a computer to execute the operations of the information processing device 30, thereby causing the computer to function as the information processing device 30. That is, the computer functions as the information processing device 30 by executing the operations of the information processing device 30 in accordance with the program.

[0047] In this embodiment, the program can be recorded on a computer-readable recording medium. The computer-readable recording medium includes a non-transitory computer-readable medium, such as a magnetic recording device, an optical disc, a magneto-optical recording medium, or a semiconductor memory. The program is distributed, for example, by selling, transferring, or lending a portable recording medium such as a DVD (digital versatile disc) or a CD-ROM (compact disc read only memory) on which the program is recorded. The program may also be distributed by storing the program in the storage of an external server and transmitting the program from the external server to another computer. The program may also be provided as a program product.

[0048] (Operation of information processing device 30) The operation of the information processing device 30 according to this embodiment, that is, the plate thickness estimation process performed by the information processing device 30, will be described with reference to FIG.

[0049] Step S10: The control unit 31 of the information processing device 30 acquires information related to the correction coefficient. Specifically, the control unit 31 acquires an image of the inspection target location captured by the unmanned photography device 10 via the communication unit 33. The information processing device 30 also acquires a plate thickness measurement value measured by the unmanned photography device 10 using the ultrasonic sensor 16 via the communication unit 33. The information processing device 30 also acquires brightness information of the measurement environment measured by the unmanned photography device 10 using the illuminance sensor 17 via the communication unit 33.

[0050] Furthermore, the information processing device 30 acquires the curvature of the inspection target location based on the position information corresponding to the captured image of the inspection target location. The captured image contains information on the position information identified by the positioning unit 14 when the unmanned photography device 10 captures the image. The information processing device 30 acquires the curvature of the inspection target location based on the position information. Any method can be used to acquire the curvature of the inspection target location. For example, a table that associates the position information of the inspection target location with the curvature may be stored in the storage unit 32. In this case, the control unit 31 acquires the curvature of the inspection target location by referring to the table in the storage unit 32.

[0051] Step S11: The control unit 31 of the information processing device 30 calculates a correction coefficient based on the surface roughness identified based on the captured image of the inspection target location, brightness information of the measurement environment, and the curvature of the inspection target location.

[0052] The correction coefficient is calculated using the following formula (1). α= f(Ra, lx, r) (1) Here, α is a correction coefficient, Ra is the surface roughness determined based on the photographed image of the inspected area, lx is the brightness information of the measurement environment, r is the curvature of the inspected area, and f is a function that indicates the correspondence between the correction coefficient and these three variables.

[0053] The function f can be set arbitrarily. For example, the function f may be determined based on a learning model created by a machine learning algorithm. In other words, the correction coefficient may be calculated based on a learning model. Hereinafter, in this embodiment, a case where the correction coefficient is calculated based on a learning model will be described, but this is not limiting. For example, the function f may be determined by a theoretical, statistical, or other method.

[0054] The learning model may be, for example, a machine learning model constructed based on a decision tree. Examples of machine learning models constructed based on a decision tree include, but are not limited to, Light GBM and XGBoost. Alternatively, the learning model may be a model generated based on a machine learning algorithm such as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or other deep learning.

[0055] The learning model is trained based on teacher data in which variables that may be related to the correction coefficient are used as explanatory variables, and the correction coefficient of the ground truth data based on the actual measurement data is used as the objective variable. In this embodiment, the explanatory variables are the surface roughness identified based on the photographed image of the inspected area, the brightness information of the measurement environment, and the curvature of the inspected area. The correction coefficient of the ground truth data is a value calculated by dividing the actual thickness of the inspected area by the thickness measurement value measured by the ultrasonic sensor 16.

[0056] Step S12: The control unit 31 estimates the plate thickness of the inspection target portion based on the correction coefficient and the plate thickness measurement value measured by the ultrasonic sensor. Specifically, the control unit 31 calculates the plate thickness of the inspection target portion based on the following formula (2). t = αt' (2) Here, t is the plate thickness at the inspected portion (in other words, the plate thickness measurement value after correction), and t' is the plate thickness measurement value measured by the ultrasonic sensor. That is, the control unit 31 estimates the plate thickness at the inspected portion by multiplying the plate thickness measurement value measured by the ultrasonic sensor by the correction coefficient.

[0057] The above-mentioned processes of steps S11 and S12 are executed by a program stored in the storage unit 32. In other words, the control unit 31 executes these processes by a program related to the plate thickness estimation process (hereinafter, also referred to as the plate thickness estimation program) stored in the storage unit 32.

[0058] Step S13: The control unit 31 displays the estimated plate thickness on the display unit 35. That is, the control unit 31 displays the output result of the plate thickness estimation program on the display unit 35. Any method can be adopted as the display output mode. For example, the control unit 31 may display on the display unit 35 a two-dimensional or three-dimensional image of the facility structure, superimposing information indicating the plate thickness on the image.

[0059] An example of a display output mode is shown with reference to Fig. 3. Fig. 3 shows an example in which information indicating plate thickness is displayed on a measurement object 41 captured in a photographed image output from an unmanned photographing device (drone). A rectangular display object 42 is displayed in a thin-walled portion. A text object 43 indicating plate thickness information (here, a thickness of 5.5 mm) is superimposed and displayed near the display object 42. By displaying and outputting in this manner, it is possible to easily grasp which part of the measurement object 41 is corroded.

[0060] In this way, according to the structure inspection system 1 of this embodiment, the plate thickness of the inspected portion is estimated by correcting the plate thickness measurement value of the inspected portion measured by the ultrasonic sensor with a correction coefficient calculated from the surface roughness, brightness information, and curvature of the inspected portion. Since the plate thickness measurement value of the inspected portion measured by the ultrasonic sensor is affected by the surface roughness, brightness information, and curvature of the inspected portion, the plate thickness estimation technology can be improved by using the correction coefficient determined by these factors to improve the accuracy of the plate thickness estimation.

[0061] Furthermore, the control unit 31 of the information processing device 30 may calculate a plate thickness reduction rate based on the estimated plate thickness and the original thickness of the inspected portion, and if the plate thickness reduction rate exceeds a threshold value, estimate whether the cause of the plate thickness reduction is internal corrosion or external corrosion based on the surface roughness.

[0062] A process of estimating whether the cause of the plate thickness reduction is internal corrosion or external corrosion will be described with reference to Fig. 4. The estimation process is executed by a program stored in the storage unit 32. In other words, the control unit 31 executes these processes by a program related to the process of identifying the cause of corrosion stored in the storage unit 32 (hereinafter, also referred to as a corrosion cause estimation program).

[0063] Step S20: The control unit 31 of the information processing device 30 calculates the plate thickness reduction rate. The plate thickness reduction rate is calculated based on the following formula (3).

[0064] A =1- t / t0 (3) Here, A is the plate thickness reduction rate, t is the estimated plate thickness, and t0 is the original thickness of the inspection target portion. Data on the original thickness of the inspection target portion is stored in the storage unit 32 in advance.

[0065] Step S21: The control unit 31 determines whether the thickness reduction rate exceeds a threshold value. The threshold value may be, for example, 0.5. If the thickness reduction rate exceeds the threshold value, the process proceeds to step S22. On the other hand, if the thickness reduction rate does not exceed the threshold value (if it is equal to or less than the threshold value), the process ends.

[0066] Step S22: The control unit 31 determines whether the surface roughness Ra exceeds a predetermined value. The predetermined value may be, for example, 100. If the surface roughness Ra exceeds the predetermined value, the process proceeds to step S23. On the other hand, if the surface roughness Ra does not exceed the predetermined value (is equal to or less than the predetermined value), the process proceeds to step S24.

[0067] Step S23: The control unit 31 estimates that the cause of the thickness reduction is external corrosion. Then, the control unit 31 causes the display unit 35 to display and output an alert related to the external corrosion.

[0068] Step S24: The control unit 31 estimates that the cause of the reduction in plate thickness is inner surface corrosion. Then, the control unit 31 causes the display unit 35 to display and output an alert relating to the inner surface corrosion.

[0069] Any method may be adopted as the display output of the alert in step S23 and step S24. For example, the control unit 31 may superimpose information indicating the plate thickness reduction rate on a two-dimensional or three-dimensional image of the facility structure and display it on the display unit 35. The control unit 31 may also superimpose information indicating whether the cause of the plate thickness reduction is internal corrosion or external corrosion at a location in the two-dimensional or three-dimensional image of the facility structure where the plate thickness reduction rate exceeds a threshold value. Furthermore, the control unit 31 may estimate a risk level based on the plate thickness reduction rate and the cause of the plate thickness reduction, and superimpose and display information indicating the risk level.

[0070] An example of a display output mode is shown with reference to Fig. 5 and Fig. 6. Fig. 5 shows an example in which information indicating the plate thickness reduction rate is displayed on a measurement object 51 captured in a photographed image output from an unmanned photographing device (drone). A rectangular display object 52 is displayed in a thin-walled portion. A text object 53 indicating information on the plate thickness reduction rate (here, a reduction rate of 30%) is superimposed and displayed near the display object 52. By displaying and outputting in this manner, the plate thickness reduction rate of the corroded portion of the measurement object 51 can be easily grasped.

[0071] FIG. 6 shows an example in which, in addition to the plate thickness reduction rate, information on the cause of the plate thickness reduction and the degree of danger are displayed on a measurement object 61 captured in a photographed image output from an unmanned photographing device (drone). A rectangular display object 62 is displayed in a thin-walled portion. A text object 63 (here, reduction rate 60%, internal corrosion (danger)) indicating the plate thickness reduction rate, information on the cause of the plate thickness reduction, and the degree of danger is superimposed and displayed near the display object 62. By displaying and outputting in this manner, the plate thickness reduction rate of the corroded portion of the measurement object 61, its cause, and the degree of danger can be easily understood.

[0072] (Example) The configuration of an embodiment according to the present disclosure is shown in FIG. 5. The operator operates the drone 100, approaches the chimney 300, which is the object to be measured, and obtains the plate thickness measurement value based on the ultrasonic sensor 116. The obtained plate thickness measurement value is corrected by the above-mentioned correction coefficient, thereby configuring a system for identifying the thin-walled portion 400. Specifically, based on the operation of the operator of the drone 100, a plate thickness estimation program is executed, and an estimated plate thickness value is output. In addition, this system is also equipped with a function of simultaneously mapping the surrounding corrosion condition on the image captured by the camera by implementing a corrosion cause estimation program. In this embodiment, the captured image is also displayed on the operator's remote controller. In addition, since the captured image is also saved in the information processing device, it is possible to check it on the information processing device after the drone operation is completed. The results of plate thickness measurement at points with different original thicknesses using this program are shown in Table 1. For comparison, the measurement value from a wall thickness measurement sensor other than the sensor mounted on the drone is recorded as t". It can be seen that by using the correction coefficient α obtained from equation (1) above, wall thickness can be measured with high accuracy (with an agreement rate of 90% or more). In Table 1, the threshold value for the plate thickness reduction rate, which is the criterion for the occurrence of corrosion that should be subject to an alert, is set to 0.5, and the threshold value for the surface roughness, which is the criterion for determining the direction of corrosion, is set to 100.

[0073] [Table 1]

[0074] Although the present disclosure has been described based on the drawings and embodiments, it should be noted that those skilled in the art can easily make various modifications and corrections based on the present disclosure. Therefore, it should be noted that these modifications and corrections are included in the scope of the present disclosure. For example, the functions included in each means or step can be rearranged so as not to be logically inconsistent, and multiple means or steps can be combined or divided into one.

[0075] For example, in addition to r, Ra, and lx, information such as material, years of use, humidity, temperature, distance from the revetment, type of coating, etc. may be additionally input to the plate thickness estimation program. In other words, the learning model according to this embodiment may include, as explanatory variables, in addition to r, Ra, and lx, material, years of use, humidity, temperature, distance from the revetment, type of coating, etc.

[0076] Furthermore, when inspecting a structure using the unmanned camera 10, it is possible to check whether there is any leakage of flammable (potentially explosive) gas in the atmosphere around the object to be inspected (i.e., the flight route of the unmanned camera 10), and only when no flammable gas is present, to inspect the structure using the unmanned camera 10. In this case, the unmanned camera 10 may be equipped with a leakage sensor (for example, an infrared gas detector such as an infrared camera) capable of detecting the presence or absence of flammable gas, in addition to the various sensors described above. [Explanation of symbols]

[0077] 1. Structural Inspection System 10 Unmanned Cameras 11 Control section 12 Storage section 13. Communications Department 14 Positioning unit 15 Imaging unit 16 Ultrasonic Sensor 17 Illuminance sensor 20 Remote Controller 21 Control section 22 Memory section 23 Communications Department 24 Input section 30 Information processing device 31 Control Unit 32 Storage section 33 Communications Department 34 Input section 35 Display section 41, 51, 61 Measurement object 42, 52, 62 Display Objects 43, 53, 63 Text Objects 100 Drones 116 Ultrasonic Sensor 300 Chimney 400 Thin wall area

Claims

1. An information processing method for estimating a plate thickness of an inspection target portion in a facility structure, comprising: Calculating a correction coefficient based on a surface roughness and brightness information identified based on a photographed image of the inspection target area, and a curvature of the inspection target area; Estimating a plate thickness of the inspection target portion based on the correction coefficient and a plate thickness measurement value measured by an ultrasonic sensor; Outputting the plate thickness of the inspection target portion; An information processing method comprising:

2. The information processing method according to claim 1, characterized in that a plate thickness reduction rate is calculated based on the plate thickness and the original thickness of the inspected portion, and if the plate thickness reduction rate exceeds a threshold value, it is estimated whether the cause of the plate thickness reduction is internal corrosion or external corrosion based on the surface roughness.

3. 3. The information processing method according to claim 1, wherein the information indicating the plate thickness is superimposed on a two-dimensional or three-dimensional image of the facility structure and displayed on a display unit.

4. The information processing method according to claim 2, characterized in that information indicating the plate thickness reduction rate is superimposed on a two-dimensional or three-dimensional image of the facility structure and displayed on a display unit.

5. The information processing method according to claim 4, characterized in that information indicating whether the cause of the thickness reduction is internal corrosion or external corrosion is further superimposed at locations in the two-dimensional or three-dimensional image of the facility structure where the plate thickness reduction rate exceeds the threshold value.

6. An information processing device that includes a control unit and estimates a plate thickness of an inspection target portion in a facility structure, the control unit comprising: Calculating a correction coefficient based on the surface roughness and brightness information identified based on the photographed image of the inspection target area, and the curvature of the inspection target area; The plate thickness of the inspection target portion is estimated based on the correction coefficient and the plate thickness measurement value measured by the ultrasonic sensor, An information processing device that outputs the plate thickness of the inspected portion.

7. The information processing device according to claim 6, characterized in that the control unit further calculates a plate thickness reduction rate based on the plate thickness and the original thickness of the inspected portion, and when the plate thickness reduction rate exceeds a threshold value, estimates whether the cause of the plate thickness reduction is internal corrosion or external corrosion based on the surface roughness.

8. The information processing device according to claim 6 or 7, characterized in that the control unit further superimposes information indicating the plate thickness on a two-dimensional or three-dimensional image of the facility structure and displays the image on a display unit.

9. The information processing device according to claim 7, characterized in that the control unit further superimposes information indicating the plate thickness reduction rate on a two-dimensional or three-dimensional image of the facility structure and displays the superimposed information on the display unit.

10. The information processing device according to claim 9, characterized in that the control unit further superimposes information indicating whether the cause of the thickness reduction is internal corrosion or external corrosion at a location in the two-dimensional or three-dimensional image of the facility structure where the thickness reduction rate exceeds the threshold value, and displays the superimposed information on the display unit.

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