Internal defect inspection method and internal defect inspection system

Abstract

The present invention provides an internal defect inspection method and inspection system that can detect internal defects in concrete floor slabs with high accuracy by analyzing thermal images captured of the surface of asphalt pavement. [Solution] The method includes a temperature fluctuation calculation step S11 for calculating, for each pixel G constituting a thermal image NG38, a low-frequency drift component Tav(kx,ky) contained in the temperature T of Nx pixels G arranged in the X-axis direction, and calculating a high-frequency temperature fluctuation ΔT(kx,ky) by subtracting the drift component Tav(kx,ky) from the temperature T(kx,ky). The method also includes a defective pixel extraction step S12 for extracting, as defective pixels KG(kx,ky), pixels G(kx,ky) whose temperature fluctuation ΔT(kx,ky) exceeds a reference value Tth. The method also includes a defect area identification step S13 for identifying a defect area KKR where an internal defect NK has occurred in the concrete slab 26 when the inspection target structure 24 is viewed from above, based on position information of the defective pixel KG(kx,ky) extracted in the defective pixel extraction step S12.

Description

[Technical Field]

[0001] The present invention relates to an internal defect inspection method and an internal defect inspection system for detecting internal defects in an inspection target structure by analyzing a thermal image of the surface of the inspection target structure. [Background technology]

[0002] Many bridges and viaducts have asphalt pavement laid on top of a concrete deck to form the road. This type of structure can develop internal defects such as peeling or lifting inside the concrete deck or at the boundary between the concrete deck and the asphalt pavement due to deterioration or poor construction, and it is necessary to detect and address these internal defects early to prevent serious accidents from occurring.

[0003] The most direct inspection method is to remove the asphalt pavement and inspect the condition of the concrete deck visually and by hammering, but this has problems such as prolonging traffic restrictions, increasing the burden on workers and construction costs, etc. Another option is to conduct a hammering inspection from above the asphalt pavement, but this requires inspectors with special skills, making it difficult to inspect a large number of structures across the country using this method.

[0004] In recent years, in order to efficiently inspect a large number of structures, an inspection method has been proposed in which the surface of a structure is imaged with a thermal camera mounted on an unmanned aerial vehicle (drone, etc.), and the captured thermal image is analyzed to detect internal defects. Generally, the surface temperature of a structure has the property that a temperature difference occurs between the area where an internal defect exists inside (defective area) and the area where no internal defect exists (healthy area). Therefore, by analyzing the thermal image, it is possible to estimate the presence or absence of internal defects in the structure, as well as their location and size, without contact.

[0005] For example, the method of detecting a deteriorated part disclosed in Patent Document 1 involves photographing the surface of a concrete surface to be inspected using an infrared thermography device, and identifying the deteriorated part of the concrete surface based on the thermal image of the surface to be inspected. The thermal image is composed of multiple pixels aligned horizontally and vertically, and each pixel has temperature data.

[0006] To briefly explain the processing flow in Figure 1 of Patent Document 1, first, the horizontal temperature gradient for each pixel is calculated [S4, S5], and it is determined whether the calculated temperature gradient is greater than a threshold [S6]. A horizontal binarized image is created [S7], with pixels greater than the threshold set to 1 and pixels less than the threshold set to 0. Next, the vertical temperature gradient for each pixel is calculated [S8, S9], and it is determined whether the temperature gradient is greater than a threshold [S10]. A vertical binarized image is created [S11], with pixels greater than the threshold set to 1 and pixels less than the threshold set to 0. The horizontal and vertical binarized images are then superimposed [S12], and the area of ​​the surface to be inspected corresponding to the area surrounded by pixels set to 1 in the superimposed binarized image is determined to be a deformed area [S13, S14]. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-99687 Summary of the Invention [Problem to be solved by the invention]

[0008] The deformation detection method in Patent Document 1 detects deformation in the surface layer of concrete based on thermal images taken directly of the concrete surface to be inspected. However, as mentioned above, many bridges and viaducts have asphalt pavement laid on top of the concrete deck, so it is unclear whether this method is effective when asphalt pavement is laid.

[0009] Furthermore, when capturing images using a thermal camera, the optical characteristics of the thermal camera can cause low-frequency drift components in the temperature data characteristics of the thermal image. For example, even if the temperature of the actual surface being inspected is uniform, the center of the thermal image captured by the thermal camera tends to be the hottest and the temperature gradually decreases as the distance from the center increases. This temperature shift, regardless of the presence or absence of internal defects, results in low-frequency drift components in the temperature characteristic curve of the thermal image data. Furthermore, when inspecting road surfaces such as bridges, center lines and tire marks on the road surface can also be sources of error when capturing images using a thermal camera. Therefore, in order to perform high-precision inspections based on thermal images, it is necessary to take these error factors (drift components, etc.) into consideration. However, the deformation detection method of Patent Document 1 does not particularly take this into consideration.

[0010] The present invention has been made in consideration of the above-mentioned background art, and aims to provide an internal defect inspection method and an internal defect inspection system that can detect internal defects in concrete decks with high accuracy by analyzing thermal images taken of the surface of asphalt pavement. [Means for solving the problem]

[0011] The present invention is an internal defect inspection method executed by a computer system, which detects internal defects in an inspection target structure by analyzing a thermal image of the top surface of the inspection target structure, which has asphalt pavement laid on the top surface of a concrete deck to form a walkway. In the present invention, when the axis parallel to the length direction of the passage is the X axis and the axis parallel to the width direction of the passage is the Y axis, the multiple pixels G constituting the thermal image are arranged in order from a reference point in the X axis direction in Nx pieces and in order in the Y axis direction in Ny pieces, and when each pixel G is expressed as pixel G(kx,ky)[1≦kx≦Nx, 1≦ky≦Ny] and the temperature data held by pixel G(kx,ky) is expressed as temperature T(kx,ky), then: a temperature fluctuation calculation step of calculating, for each pixel G, the low-frequency drift component Tav(kx,ky) contained in the temperature T of Nx pixels G arranged in the X-axis direction, and calculating the high-frequency temperature fluctuation ΔT(kx,ky) by subtracting the drift component Tav(kx,ky) from the temperature T(kx,ky); a defective pixel extraction step of extracting, as a defective pixel KG(kx,ky), a pixel G(kx,ky) whose temperature fluctuation ΔT(kx,ky) calculated in the temperature fluctuation calculation step exceeds a reference value Tth; and a defect area identification step for identifying a defect area where an internal defect has occurred in the concrete slab when the inspection target structure is viewed from above, based on the position information of the defective pixel KG(kx, ky) extracted in the defective pixel extraction step.

[0012] In the temperature fluctuation calculation step, the drift component Tav(kx,ky) can be calculated as the average value of the temperatures T of a specified number of pixels G adjacent to pixel G(kx,ky) and aligned in the X-axis direction, or the average value of the temperatures T of a specified number of pixels G aligned in the X-axis direction, including pixel G(kx,ky). Alternatively, in the temperature fluctuation calculation step, the drift component Tav(kx,ky) can be calculated by fitting the temperatures T of Nx images G aligned in the X-axis direction using a polynomial.

[0013] In the defective area specifying step, it is preferable that the plurality of defective pixels KG(kx, ky) extracted in the defective pixel extracting step are clustered by applying a predetermined clustering algorithm, and the resulting cluster or an area surrounding the cluster is defined as the defective area.

[0014] Preferably, the method further comprises a defect area display step of displaying on a display device a binary image in which an area other than the defect area on the upper surface of the inspection target structure is designated as a healthy area and the healthy area and the defective area are represented in different colors. In this case, the defective pixel extracting step sets a plurality of reference values ​​Tth and extracts defective pixels KG(kx,ky) for each reference value Tth, the defect area identifying step identifies the defective area for each reference value Tth, and the defect area displaying step displays the binary image for each reference value Tth in such a way that the healthy areas are represented in the same color and the defective areas are represented in different colors, and these images are superimposed on each other and displayed on the display device.

[0015] In the defective pixel extraction step, it is preferable to set a first reference value Tth1 and a second reference value Tth2 as the reference values, set the first reference value Tth1 to a value h1 times (h1 is a constant where h1 > 0) the standard deviation σ when the distribution of the Nx temperature fluctuations ΔT(kx, ky) that make up each row is considered to be a normal distribution, set the second reference value Tth2 to an arbitrary constant greater than zero, and extract as a defective pixel KG(kx, ky) a pixel G(kx, ky) whose temperature fluctuation ΔT(kx, ky) calculated in the temperature fluctuation calculation step exceeds both the reference values ​​Tth1 and Tth2.

[0016] Furthermore, the configuration can include a defect depth estimation step in which the area other than the defect area on the top surface of the inspected structure is defined as a healthy area, and the depth position of the internal defect occurring in the inspected structure is estimated based on the rate of change Rp of temperature fluctuation ΔT(kx, ky) occurring at the boundary between the defect area and the healthy area, or based on the average value Tmean of temperature T(kx, ky) and the rate of change Rp.

[0017] In this case, model structure related information, which is numerical information about a virtual model structure that simulates the inspection target structure, and model structure information, which is information calculated by a heat conduction analysis using the model structure related information and indicates the temperature distribution of each part of the model structure, including the defective area and the sound area. Temperature change informationa database preparation step for preparing a database in which the data are linked and registered; the model structure-related information includes at least structural information indicating the structural characteristics of the model structure, environmental temperature information indicating the characteristics of the environmental temperature at the installation location of the model structure, site information indicating the characteristics related to the amount of solar radiation on the top surface of the model structure, and defect information indicating the presence or absence of internal defects in the model structure and the characteristics of the internal defects; In the defect depth estimation step, the model structure related information and the model structure related information registered in the database are Temperature change information It is preferable to prepare a defect depth estimation model created by machine learning using the above as training data, and input to the defect depth estimation model inspection target structure related information, which is numerical information corresponding to information other than the defect information in the model structure related information, and information on the temperature T(kx, ky) of the inspection target structure or information on the temperature fluctuation ΔT(kx, ky) calculated in the temperature fluctuation calculation step, or both, to estimate the depth position of the internal defect occurring in the inspection target structure.

[0018] The present invention also provides an internal defect inspection system consisting of a computer system that detects internal defects in a structure to be inspected by analyzing thermal images of the top surface of the structure to be inspected, which has asphalt pavement laid on the top surface of a concrete deck to form a walkway.

[0019] When the axis parallel to the length direction of the passage is the X axis and the axis parallel to the width direction of the passage is the Y axis, the multiple pixels G that make up the thermal image are arranged in order from the reference point in the X axis direction, Nx in number, and in the Y axis direction, Ny in number. When each pixel G is represented as pixel G(kx,ky)[1≦kx≦Nx, 1≦ky≦Ny] and the temperature data at the position of pixel G(kx,ky) is represented as temperature T(kx,ky), then: a temperature fluctuation calculation unit that calculates, for each pixel G, a low-frequency drift component Tav(kx,ky) contained in the temperature T of Nx pixels G arranged in the X-axis direction, and calculates a high-frequency temperature fluctuation ΔT(kx,ky) by subtracting the drift component Tav(kx,ky) from the temperature T(kx,ky); a defective pixel extracting unit that extracts a pixel G(kx,ky) whose temperature fluctuation ΔT(kx,ky) calculated by the temperature fluctuation calculating unit exceeds a reference value Tth as a defective pixel KG(kx,ky); and a defect area identification unit that identifies a defect area where an internal defect has occurred in the concrete slab when the inspection target structure is viewed from above, based on the position information of the defective pixel KG(kx, ky) extracted by the defective pixel extraction unit.

[0020] The temperature fluctuation calculation unit may be configured to calculate, as the drift component Tav(kx,ky), an average value of the temperatures T of a specified number of pixels G adjacent to pixel G(kx,ky) and aligned in the X-axis direction, or an average value of the temperatures T of a specified number of pixels G aligned in the X-axis direction, including pixel G(kx,ky). Alternatively, the temperature fluctuation calculation unit may be configured to calculate the drift component Tav(kx,ky) by fitting the temperatures T of Nx images G aligned in the X-axis direction using a polynomial.

[0021] It is preferable that the defective area identifying unit is configured to cluster the multiple defective pixels KG(kx, ky) extracted by the defective pixel extracting unit by applying a predetermined clustering algorithm, and to identify the obtained cluster or an area surrounding the cluster as the defective area.

[0022] The inspection system preferably includes a display device and a defect area display unit that defines an area on the top surface of the inspection target structure other than the defect area as a healthy area and displays a binary image on the display device in which the healthy area and the defective area are represented in different colors. In this case, the defective pixel extraction unit may set a plurality of reference values ​​Tth and extract defective pixels KG(kx,ky) for each reference value Tth, the defect area identification unit identifies the defective area for each reference value Tth, and the defect area display unit may display the binary image for each reference value Tth in such a way that the healthy areas are represented in the same color and the defective areas are represented in different colors, and the binary images are superimposed on each other and displayed on the display device.

[0023] Furthermore, it is preferable to configure the structure so that the area on the top surface of the inspected structure other than the defect area is defined as a healthy area, and the structure is provided with a defect depth estimation unit that estimates the depth position of the internal defect occurring in the inspected structure based on the rate of change Rp of temperature fluctuation ΔT(kx, ky) occurring at the boundary between the defect area and the healthy area, or based on the average value Tmean of temperature T(kx, ky) and the rate of change Rp.

[0024] In this case, model structure related information, which is numerical information about a virtual model structure that simulates the inspection target structure, and model structure information, which is information calculated by a heat conduction analysis using the model structure related information and indicates the temperature distribution of each part of the model structure, including the defective area and the sound area. Temperature change information However, it has a database where the information is linked and registered, the model structure-related information includes at least structural information indicating the structural characteristics of the model structure, environmental temperature information indicating the characteristics of the environmental temperature at the installation location of the model structure, site information indicating the characteristics related to the amount of solar radiation on the top surface of the model structure, and defect information indicating the presence or absence of internal defects in the model structure and the characteristics of the internal defects; The defect depth estimation unit is configured to estimate the model structure related information and the model structure related information registered in the database. Temperature change informationIt is preferable to prepare a defect depth estimation model created by machine learning using the above as training data, and input to the defect depth estimation model inspection target structure related information, which is numerical information corresponding to information other than the defect information in the model structure related information, and information on the temperature T(kx, ky) of the inspection target structure or information on the temperature fluctuation ΔT(kx, ky) calculated by the temperature fluctuation calculation unit, or both of these information, to estimate the depth position of the internal defect occurring in the inspection target structure. [Effects of the Invention]

[0025] The internal defect inspection method and internal defect inspection system of the present invention, when analyzing the temperature data of a thermal image taken of the top surface of an inspected structure (a structure in which asphalt pavement is laid on top of a concrete floor slab to form a walkway), uses a unique method to cancel out error factors such as low-frequency drift components contained in the characteristics of the temperature data, thereby making it possible to easily and accurately detect the presence or absence of internal defects, the location of defect areas where internal defects exist inside, etc. [Brief explanation of the drawings]

[0026] [Figure 1] 1 is a block diagram showing a first embodiment of an internal defect inspection system according to the present invention. [Figure 2] 1A is an overall flowchart showing a first embodiment of the internal defect inspection method of the present invention (an inspection method executed by the internal defect inspection system of FIG. 1), and FIG. 1B is a flowchart showing the contents of a temperature fluctuation calculation step and a defective pixel extraction step. [Figure 3] (a) is a perspective view showing how a thermal image of an inspection area on the top surface of a structure to be inspected is captured, and (b) is a graph of the temperature T of pixels G(1,ky) to G(Nx,ky) among the pixels G that make up the thermal image. [Figure 4] Graph (a) shows the temperature fluctuation ΔT calculated for each pixel G(1,ky) to G(Nx,ky), and graph (b) compares a thermal image showing the defective area detected by the hammering test with a binarized image showing the defective area identified in the defect area identification step. [Figure 5] 6A and 6B are graphs showing the results of a verification test of the internal defect inspection method according to the first embodiment. [Figure 6] 10A and 10B are diagrams showing two examples of a method for setting a reference value Tth in the defective pixel extraction step. [Figure 7] FIG. 10 is a block diagram showing a second embodiment of the internal defect inspection system of the present invention. [Figure 8] FIG. 10A is an overall flowchart showing a second embodiment of the internal defect inspection method of the present invention (an inspection method executed by the internal defect inspection system of FIG. 7), and FIG. 10B is a block diagram showing the flow of information when creating a database in the database preparation step. [Figure 9] Graph (a) of the simulation results showing the relationship between the temperature change rate Rp at the boundary between the defective area and the healthy area on the surface of a model structure and the depth position Zf of an internal defect, and a schematic diagram (b) showing an overview of the model structure. [Figure 10] FIG. 1A is a block diagram showing the flow of information in a defect depth estimation step, and FIG. 1B is a block diagram showing a method for creating a defect depth estimation model. [Figure 11] 10 is a diagram showing an example of model structure related information. [Figure 12] 10 is a table showing an example of model structure temperature change information. [Figure 13] 10 is a diagram showing an example of inspection target structure summary information. [Figure 14] 10A is a diagram showing an example of information related to the inspection target structure, and FIG. 10B is a diagram showing an example of information on the temperature T and temperature fluctuation ΔT of pixel G. [Figure 15] 1A is a diagram showing an example of information on the temperature of the boundary portion derived during the defect depth estimation step, and FIG. 1B is a diagram showing an example of information on the depth and size of the internal defect output as an estimation result of the defect depth estimation step. DETAILED DESCRIPTION OF THE INVENTION

[0027] <<Internal Defect Inspection System 10 / Internal Defect Inspection Method of First Embodiment>> A first embodiment of an internal defect inspection system and an internal defect inspection method of the present invention will be described below with reference to Figures 1 to 6. The internal defect inspection system 10 of this embodiment is a computer system that inspects internal defects in a structure to be inspected by analyzing a thermal image captured of the top surface of the structure to be inspected, which has asphalt pavement laid on the top surface of a concrete floor slab to form a walkway. As shown in Figure 1, the internal defect inspection system 10 includes functional blocks of a temperature fluctuation calculation unit 12, a defective pixel extraction unit 14, a defective area identification unit 16, and a defective area display unit 18, and further includes a display device 20 such as a display and an information input device 22 such as a keyboard or mouse operated by a user.

[0028] The first embodiment of the temperature measurement prediction method of the present invention is an inspection method executed by an internal defect inspection system 10, and as shown in FIG. 2(a), it is composed of a temperature fluctuation calculation step S11 executed by a temperature fluctuation calculation unit 12, a defective pixel extraction step S12 executed by a defective pixel extraction unit 14, a defective area identification step S13 executed by a defective area identification unit 16, and a defective area display step S14 executed by a defective area display unit 18. The temperature measurement result prediction method (and the temperature measurement result prediction system 10) of this embodiment will be described with reference to the overall flowchart of FIG. 2(a), but before that, the inspection target structure and thermal image will be described.

[0029] The structure to be inspected is, for example, a bridge girder or the like, in which a road 30 is formed by laying asphalt pavement 28 on the top surface of a concrete deck 26, as in the structure to be inspected 24 shown in Figure 3(a). A white or yellow center line 32 is drawn in the center of the width direction of the top surface of the road 30. Note that ancillary components such as parapets have been omitted from this drawing.

[0030] The thermal image was taken of an inspection area 38 on the top surface of the inspection target structure 24 (top surface of the road 30) using a thermal camera 36 mounted on an unmanned aerial vehicle 34. The thermal image NG38 shown in the upper part of Figure 3(b) is composed of a large number of aligned pixels G, and temperature data and coordinate data are assigned to each pixel G.

[0031] If the axis parallel to the length of the road 30 is the X-axis and the axis parallel to the width of the road 30 is the Y-axis, Nx pixels G are arranged in order along the X-axis from the reference point, and Ny pixels G are arranged in order along the Y-axis. Here, each pixel G is assigned an X-coordinate and represented as pixel G(kx,ky)[1≦kx≦Nx, 1≦ky≦Ny]. The temperature data held by each pixel G is represented as temperature T(kx,ky). When analyzing the thermal image NG38, the thermal image NG38 is treated as image data (a collection of temperature data and coordinate data for each pixel) rather than as an image that provides visual information.

[0032] The thermal image NG38 has Ny rows, each with Nx pixels G arranged in the X-axis direction. Focusing on the row of pixels G(1,ky) to G(Nx,ky), the distribution of temperatures T(1,ky) to T(Nx,ky) is shown in the lower graph of Figure 3(b). Looking at this graph, the center of the temperature characteristic curve bulges gently upward, which is the low-frequency drift component of the temperature characteristic curve explained in the Background Art section. The high-frequency fluctuation component superimposed on the low-frequency drift component shown in the graph of Figure 3(b) is important data for detecting the status of internal defects.

[0033] <Temperature Fluctuation Calculation Step S11 / Temperature Fluctuation Calculation Unit 12> In the temperature fluctuation calculation step S11, a thermal image NG38 is acquired, and the low-frequency drift component is canceled from the temperature T(kx,ky) to extract the high-frequency fluctuation component. Specifically, steps S111 and S112 shown in FIG. 2(b) are performed in order. First, for each pixel G, the average value (moving average value) of the temperatures T of a specified number of pixels G adjacent to pixel G(kx,ky) and arranged in order in the X-axis direction is calculated as the drift component Tav(kx,ky) [Step S111]. Alternatively, for each pixel G, the average value (moving average value) of the temperatures T of a specified number of pixels G arranged in the X-axis direction, including pixel G(kx,ky), is calculated as the drift component Tav(kx,ky) [Step S111]. Either moving average value can be used, but empirically, similar results are obtained. The drift components Tav(1,ky) to Tav(Nx,ky) for the row of pixels G(1,ky) to G(Nx,ky) correspond to the "low-frequency drift component" in the lower graph of Figure 3(b) described above.

[0034] Then, for each pixel G, the drift component Tav(kx,ky) is subtracted from the temperature T(kx,ky) to calculate the temperature fluctuation ΔT(kx,ky) [step S112]. The graph in FIG. 4(a) shows the distribution of the temperature fluctuations ΔT(1,ky) to ΔT(Nx,ky) for the rows of pixels G(1,ky) to G(Nx,ky). The temperature fluctuations ΔT(1,ky) to ΔT(Nx,ky) correspond to the "high-frequency fluctuation component" described above, and by performing steps S111 and S112, this important data can be accurately extracted.

[0035] Another feature of this temperature fluctuation calculation step S11 is that when calculating the low-frequency drift component, it uses a "moving average in the X-axis direction (the length direction of the road 30)" rather than a "moving average in the Y-axis direction (the width direction of the road 30)." Since the road 30 often has a centerline 32 or tire marks in the length direction (X-axis direction), differences in the solar absorptance between areas with and without these marks can cause temperature unevenness in the thermal image NG38. However, by using a "moving average in the X-axis direction (the length direction of the road 30)" when calculating the low-frequency drift component, the effects of temperature unevenness can be minimized.

[0036] <Defective pixel extraction step S12 / defective pixel extraction unit 14> In the defective pixel extraction step S12, pixels G(kx, ky) for which the temperature variation ΔT(kx, ky) calculated in the temperature variation calculation step S11 exceeds the reference value Tth are extracted as defective pixels KG(kx, ky). As described above, when there is an internal defect in the structure, there is a property that the temperature difference between the region (defective region KKR) where the internal defect exists inside and the region (sound region KZR) where the internal defect does not exist on the surface of the structure becomes large. Therefore, it is determined that pixels G(kx, ky) for which the temperature variation ΔT(kx, ky) exceeds the reference value Tth are defective pixels KG(kx, ky) that are highly likely to belong to the defective region KKR.

[0037] In this embodiment, the reference value Tth is set to a value that is h1 times the standard deviation σ (h1 is a constant where h1 > 0) when the distribution of the Nx temperature variations ΔT(kx, ky) constituting each row is regarded as a normal distribution. For example, in the graph of FIG. 4(a), when comparing the case where the reference value Tth is set to σ [h1 = 1] and the case where it is set to 2σ [h1 = 2], the number of defective pixels KG(kx, ky) is larger when the reference value Tth = σ. This means that the smaller the reference value Tth, the higher the defect detection sensitivity.

[0038] In addition, there are other ways to determine the reference value Tth. For example, the reference value Tth can be set to the h2-th value (1 < h2 < Nx) counted from the larger values among the Nx temperature variations ΔT(kx, ky) constituting each row, or the reference value Tth can be set to an arbitrary constant greater than zero.

[0039] <Defective region identification step S13 / Defective region identification unit 16><Defective region display step S14 / Defective region display unit 18> In the defective region identification step S13, based on the position information of the defective pixels KG(kx, ky) extracted in the defective pixel extraction step S12, the defective region KKR in the thermal image NG38 is identified, and the other regions are set as the sound region KZR. For example, one possible method is to use the position of each defective pixel KG(kx,ky) extracted in defective pixel extraction step S12 as the defective area KKR, or to use the area surrounding all of the defective pixels KG(kx,ky) as the defective area KKR.Another possible method is to cluster the multiple defective pixels KG(kx,ky) extracted in defective pixel extraction step S12 by applying a predetermined clustering algorithm to remove noise data, and then use the resulting clusters or the areas surrounding the clusters as the defective area KKR.

[0040] Then, in a defective area display step S14, a binary image 40 in which the defective area KKR and the healthy area KZR are represented in different colors is displayed on the display device 20. The binary image 40 shown in the lower part of Fig. 4(b) is a binary image when the reference value Tth = σ[h1 = 1] is used, and the defective area KKR is represented in black and the healthy area KZR is represented in white.

[0041] In the thermal image NG38 in the upper part of Fig. 4(b), the main defect area KKR detected by an experienced inspector through hammering testing is shown enclosed in a rectangular frame. It can be seen that the defect area KKR shown in the binarized image 40 is generally located in a similar position to the actual defect area (the defect area KKR detected through hammering testing).

[0042] In the defective area display step S14, the binarized image 40 when the reference value Tth=σ and the binarized image 40 when the reference value Tth=2σ may be displayed in an overlapping manner. In this case, the defective areas KKR are displayed in different colors (e.g., black and red) and the healthy areas KZR are displayed in the same color (e.g., white), thereby enabling the user to visually recognize the difference between the defective areas KKR due to the difference in the reference value Tth.

[0043] <Additional information about steps S11 to S13> To make the explanation easier to understand, the contents of steps S11 to S13 have been explained as follows: "A thermal image NG38 of a specific inspection area 38 is analyzed, and a defect area KKR within the inspection area 38 is identified." However, since a typical inspection target structure 24 is very large, in many cases the surface of the inspection target structure 24 is divided into multiple inspection areas 38, and multiple thermal images NG38 taken for each inspection area 38 are analyzed to inspect the entire inspection target structure 24.

[0044] Therefore, the flow for identifying the defect area KKR in the entire inspection target structure 24 can be, for example, "before step S11, all thermal images NG38 are combined to create data of an entire thermal image, and steps S11 to S13 are executed in order for the data of the entire thermal image to identify the defect area KKR in the entire inspection target structure 24." Alternatively, the flow can be "executing steps S11 to S13 individually for each thermal image NG38, identifying the defect area KKR for each inspection area 38, and then combining the results to identify the defect area KKR in the entire inspection target structure 24." Regardless of which flow is selected, substantially the same results can be obtained. It is up to the user to decide at what stage to combine information obtained from a plurality of thermal images NG38, as long as the defective region KKR of the entire inspection target structure 24 can be identified in the end.

[0045] <Results of verification test for the first embodiment> Next, a description will be given of a verification test that was conducted to verify the defect detection accuracy of the internal defect inspection method (and the internal defect inspection system 10) of this embodiment. In the verification test, for a specific structure that was actually in operation, a defect area KKR detected using the internal defect inspection method of this embodiment was compared with a defect area KKR detected by a hammering test by an experienced inspector, and the degree to which the former defect area KKR matched the latter defect area KKR was evaluated.

[0046] Here, evaluation was performed using two indices: an area-based precision rate and a number-based precision rate. The area-based precision rate is a value calculated by using the surface area of ​​the entire structure (the sum of the area [R] of the defective regions KKR and the area [S] of the sound regions KZR) as the denominator and the area of ​​the portion where the detection results by the internal defect inspection method of this embodiment and the detection results by the hammering inspection match (the sum of the area [A] of the defective regions KKR and the area [D] of the sound regions KZR) as the numerator. The number-based precision rate is a value calculated by using the number [R] of defective regions KKR detected by the hammering inspection as the denominator and the number [A] of internal defects KKR detected by the internal defect inspection method of this embodiment among the number [R] as the numerator.

[0047] The graph in Figure 5(a) shows the verification results when the reference value Tth in the defective pixel extraction step S12 was set to σ[h1=1], with the area-based precision rate being approximately 85% or higher and the number-based precision rate being 85%. The graph in Figure 5(b) shows the verification results when the reference value Tth in the defective pixel extraction step S12 was set to 2σ[h1=2], with the area-based precision rate being approximately 85% or higher and the number-based precision rate being 62%.

[0048] Focusing on the area-based accuracy rate, good results of generally 85% or more were obtained when the reference value Tth was σ or 2σ. The first goal of the present invention is to easily identify defective areas by analyzing thermal images and efficiently narrow down structures that urgently require rigorous inspection, such as hammering inspection. Therefore, if the area-based accuracy rate is 85% or more, it can be said that the internal defect inspection method of this embodiment is fully practical.

[0049] Incidentally, when we looked at the precision rate based on the number of cases, setting the reference value Tth to σ[h1=1] yielded better results (85%) than setting it to 2σ[h2=2]. However, we did not consider reducing the reference value Tth below σ[h1=1]. This is because the defect detection sensitivity is higher when the reference value Tth=σ[h1=1] than when it is set to 2σ[h1=2], and the number of cases [C] incorrectly identified as defective KKR regions also increases, making it undesirable to further increase the defect detection sensitivity. The current precision rate based on the number of cases is thought to be limited to 85% due in part to the influence of noise data contained in the thermal image NG38. Therefore, if we could reduce the noise data contained in the thermal image NG38, for example by using a more powerful thermal camera36, we believe the precision rate based on the number of cases would be further improved.

[0050] There is also room for improvement in the method for setting the reference value Tth. In the above verification test, we chose to set the reference value Tth at h1 times the standard deviation σ (h1 > 0). This is to enable statistical and objective evaluation, but on the other hand, as shown in the graph on the right side of Figure 6(a), there is a possibility that a pixel G(kx,ky) located in an area with many healthy regions KZR may be judged to be a defective pixel KG(kx,ky) in an overly strict manner.

[0051] Therefore, it is considered effective to set a first reference value Tth1 and a second reference value Tth2, set the first reference value Tth1 to a value h1 times the standard deviation σ (h1 is a constant greater than 0), set the second reference value Tth2 to an arbitrary constant greater than zero, and extract a pixel G(kx,ky) whose temperature fluctuation ΔT(kx,ky) exceeds both the reference values ​​Tth1 and Tth2 as a defective pixel KG(kx,ky). As shown in the graph on the right side of Figure 6(b), this allows a pixel G(kx,ky) located in an area with a high proportion of healthy regions KZR to be determined to be a defective pixel KG(kx,ky) with the same strictness as a pixel G(kx,ky) located in an area with a high proportion of defective regions KKR, which is considered to further improve the precision rate based on the number of cases.

[0052] <Summary of the First Embodiment> As described above, the internal defect inspection method and internal defect inspection system 10 of this embodiment, when analyzing the temperature data of the thermal image NG38 captured of the top surface of the inspected structure 24 (a structure in which asphalt pavement 28 is laid on the top surface of a concrete deck 26 to form a road 30), uses a unique method to cancel out error factors such as low-frequency drift components contained in the temperature data, thereby making it possible to easily and accurately detect the presence or absence of an internal defect and the location of the defect area KKR where an internal defect exists inside.

[0053] <<Second embodiment of internal defect inspection system 42 / internal defect inspection method>> Next, a second embodiment of an internal defect inspection system and an internal defect inspection method of the present invention will be described with reference to Figures 7 to 15. Here, the same components as those in the first embodiment will be given the same reference numerals and descriptions thereof will be omitted. 7, the internal defect inspection system 42 of this embodiment is a computer system that has the same configuration as the above-mentioned internal defect inspection system 10, but further includes a defect depth estimation unit 44 and a database 46 to add a new function. The new function is a function to estimate the depth position of an internal defect NK occurring in the inspection target structure 24 based on the thermal image NG38.

[0054] The second embodiment of the temperature measurement prediction method of the present invention is an inspection method executed by an internal defect inspection system 42, and as shown in Figure 8(a), in addition to the above-mentioned temperature fluctuation calculation step S11, defective pixel extraction step S12, defective area identification step S13, and defective area display step S14, a database preparation step S21 for preparing a database 46 and a defect depth estimation step S22 executed by a defect depth estimation unit 44 are added.

[0055] After this, the temperature measurement result prediction method (and temperature measurement result prediction system 42) of this embodiment will be explained, focusing on the two newly added steps S21 and S22. Before that, however, the characteristics that form the technical basis for estimating the depth position of an internal defect will be explained.

[0056] The graph in Fig. 9(a) is a graph of the simulation results of heat conduction analysis showing the relationship between the rate of change Rp of temperature of the boundary portion KYB between the defective region KKR and the sound region KZR on the surface of a model structure X, which is a simulation model, and the depth position Zf of the internal defect NK, and Fig. 9(b) is a schematic diagram showing an outline of the model structure X. The inventors of the present application carried out simulations such as those shown in Figs. 9(a) and 9(b) using various model structures X, and found that there is a correlation between the rate of change Rp (= temperature gradient) of the surface temperature of the boundary portion KYB and the depth position Zf of the internal defect NK.

[0057] There are several possible ways to quantify the rate of change of surface temperature Rp, but here, as shown in the graph on the right side of Figure 9(a), the rate of change of surface temperature Rp is quantified as Rp = Ta / Tb. Ta is the difference between the "main temperature of the defective area KKR" and the "temperature at the boundary between the defective area KKR and the healthy area KZR," and Tb is the difference between the "main temperature of the defective area KKR" and the "main temperature of the healthy area KZR." Therefore, the rate of change of surface temperature Rp = Ta / Tb becomes large when the temperature gradient is large and becomes small when the temperature gradient is small.

[0058] In the graph on the left side of Figure 9(a), the horizontal axis represents the length of the internal defect NK in the X-axis direction (2 x Lx), and the vertical axis represents the rate of change of surface temperature Rp. The solid line represents the characteristics when the internal defect NK is located at a depth Zf = 45 mm, i.e., in this embodiment, the characteristics when the internal defect NK (void) is located at the boundary between the asphalt pavement 28 and the concrete slab 26. The dashed line represents the characteristics when the internal defect NK is located at a depth Zf = 85 mm, i.e., the characteristics when the internal defect NK (void) is located inside the concrete slab 26 deeper than the asphalt pavement 28. From this graph, it can be seen that if the length of the internal defect NK (2 x Lx) is equal to or greater than a certain value, there is a strong correlation between the rate of change of surface temperature Rp and the depth position Zf of the internal defect NK.

[0059] Therefore, if the correlation between the surface temperature change rate Rp and the depth position Zf is known in advance, the depth position Zf can be easily calculated by substituting the temperature change rate Rp derived from the thermal image NG38 into the formula expressing the correlation, or by substituting the temperature change rate Rp derived from the thermal image NG38 and the mean value Tmean, and the size of the internal defect NK can also be easily calculated. Alternatively, a conversion table may be created based on the correlation and used to derive the value. The mean value Tmean is the mean value of the temperature T of each pixel G in the thermal image NG38. Furthermore, the surface temperature change rate Rp may be any index other than Ta / Tb as long as it quantifies the magnitude of the temperature gradient.

[0060] It should be noted that the surface temperature of a structure is affected by changes in the outside air temperature over time. For example, the surface temperature of a structure at 12:00 (outside air temperature 20°C) will be slightly different if the outside air temperature at 6:00 on that day is 0°C than if the outside air temperature at 6:00 on that day is 10°C. Therefore, in order to identify the depth position Zf with higher accuracy, it is preferable to analyze the thermal image NG38 taking into account the outside air temperature history. The database preparation step S21 and defect depth estimation step S22 described below also take into account changes in the outside air temperature over time.

[0061] <Database preparation step S21 / Database 28> In the database preparation step S21, a database 46 is prepared in which model structure related information and model structure temperature change information are linked to each other and registered, as shown in Fig. 8(b). The model structure X is a virtual structure that simulates the inspection target structure 24, and is a simulation model for heat conduction analysis.

[0062] First, the model structure-related information will be described. The model structure-related information is numerical information for performing a heat conduction analysis simulation for a model structure X (model structures 1, 2, ...), and includes at least structural information indicating the structural characteristics of the model structure X, environmental temperature information indicating the characteristics of the environmental temperature at the installation location of the model structure X, site information indicating the characteristics of the amount of solar radiation on the top surface of the model structure X, and defect information indicating the presence or absence of internal defects NK in the model structure X and the characteristics of the internal defects NK.

[0063] In the example shown in Figure 11, the thickness of asphalt and concrete, the thermal conductivity of asphalt and concrete, etc. are set as structural information items, and the outdoor temperature data for each month and time are set as environmental temperature information items. For outdoor temperature data, it is advisable to use data for an appropriate location published by the Japan Meteorological Agency, for example.

[0064] Additionally, the site information items include the asphalt's solar absorptivity value and data on global solar radiation by month and time. It is recommended to use data on global solar radiation for an appropriate location published by the Japan Meteorological Agency, for example. The asphalt's solar absorptivity and global solar radiation values ​​are used to calculate the equivalent outside air temperature required for heat conduction analysis. Furthermore, if the presence of internal defects (NK) is assumed, the defect information items include values ​​such as the depth position, thickness, length, and thermal conductivity of the air layer.

[0065] Next, we will explain the model structure temperature change information. The model structure temperature change information is data on the temperature distribution of each part calculated by performing a heat conduction analysis simulation based on the model structure related information, and as shown in Figure 12, it is the temperature of each part of model structure X (model structures 1, 2, ...) calculated for each month and time.

[0066] The calculated temperature distribution data includes at least the temperature data of the defect region KKR and the sound region KZR on the surface of the model structure X. If an internal defect NK such as a void exists inside the model structure X, the temperature difference between the defect region KKR and the sound region KZR on the structure surface becomes large, and the isotherms 48 at the boundary between the two regions tend to become dense, so the degree of density of the isotherms 48 at the boundary becomes important information.

[0067] In this way, the model structure-related information shown in Figure 11 and the model structure temperature change information shown in Figure 12 are registered in database 46 in a linked manner, and this information serves as training data when creating the defect depth estimation model 44a (machine learning model) described below. Incidentally, "preparing database 46" means creating database 46 and storing it within the system, making database 46 already stored within the system usable, making information from database 46 stored in other devices or storage media available for retrieval, etc.

[0068] <Defect Depth Estimation Step S22 / Defect Depth Estimation Unit 26> As shown in FIG. 8(a), the defect depth estimation step S22 is a step performed after the defect area identification step S13 and the database preparation step S21. In the defect depth estimation step S22, as shown in Figure 10(a), a predetermined defect depth estimation model 44a (machine learning model) is prepared, and the depth position Zf of the internal defect NK occurring in the inspection target structure 24 is estimated by inputting information related to the inspection target structure and information on the temperature T(kx, ky) of the thermal image NG38 of the inspection target structure 24 or information on the temperature fluctuation ΔT(kx, ky) calculated in the temperature fluctuation calculation step S12, or both, into the defect depth estimation model 44a.

[0069] First, the defect depth estimation model 44a will be described. As shown in FIG. 10(b), the defect depth estimation model 44a is a machine learning model created by machine learning using model structure-related information and model structure temperature change information stored in the database 46 as training data. Therefore, the defect depth estimation model 44a necessarily performs estimation taking into account the "correlation between the rate of change Rp of surface temperature and the depth position Zf" and the "change in outside air temperature over time." Note that the machine learning algorithm, etc., is not particularly limited, and for example, a model formula created using a multiple regression analysis technique may be used, or techniques such as random forests and neural networks may also be used.

[0070] Incidentally, "preparing a defect depth estimation model 44a" means creating a defect depth estimation model 44a and storing it in the system, making a defect depth estimation model 44a that is already stored in the system usable, making a defect depth estimation model 44a that is stored in another device or storage medium usable, etc.

[0071] Next, the inspection target structure related information to be input to the defect depth estimation model 44a will be described. Fig. 14(a) shows an example of the inspection target structure related information, and each item of the inspection target structure related information is numerical information corresponding to information other than the defect information in the model structure related information shown in Fig. 11 (structure information, environmental temperature information, and site information).

[0072] Although the information related to the inspection target structure [= numerical information] is basically provided by the user, some items have values ​​that are difficult to identify unless the user has specialized knowledge. Therefore, in order to reduce the burden on the user, in this embodiment, the user provides the inspection target structure summary information, which is text information [= character information or numerical information], and a process is performed to derive the inspection target structure related information from the provided inspection target structure summary information.

[0073] To explain this point, let us compare the example of the inspection target structure summary information shown in Figure 13 with the example of the inspection target structure related information shown in Figure 14(a). For example, the value of the "Asphalt Thickness" item in the inspection target structure related information can be relatively easily identified even by a user without specialized knowledge, so the inspection target structure summary information [= numerical information] of "25" is obtained from the user and used as is as the inspection target structure related information [= numerical information].

[0074] On the other hand, for example, it is difficult for a user to identify the value of the "thermal conductivity of asphalt" item in the information related to the structure to be inspected unless they have specialized knowledge. Therefore, the system obtains from the user the general information (=text information) of the structure to be inspected, such as "standard (density of asphalt)" and "low moisture content (moisture content of asphalt)," and based on this, automatically derives the information (=numerical information) related to the structure to be inspected, such as "1.45 (thermal conductivity of asphalt)." The derivation method is not particularly limited, but one possible method is to prepare a conversion table in advance that converts specific text information into specific numerical information.

[0075] For example, the "outside temperature by time" item in the information related to the structure to be inspected can be determined by the user by examining past data from the Japan Meteorological Agency, but examining each and every one is tedious. Therefore, the system obtains from the user general information [=text information] about the structure to be inspected, such as "April 2nd (inspection date)" and "XX district of Toyama City (installation location)," and based on this, automatically derives the information related to the structure to be inspected [=numerical information], which is the outside temperature data by time. While there are no particular limitations on the derivation method, one possible method is to store temperature data from all over the country published by the Japan Meteorological Agency in a database in advance, automatically search the database based on the text information, and extract the relevant numerical information.

[0076] In addition, to reduce the burden on the user, items such as "solar radiation absorption rate of asphalt" and "global solar radiation by time" among the information related to the structure to be inspected are also automatically derived from text information about the structure to be inspected, based on which text information about the structure to be inspected (numeric information) is obtained from the user. Note that if the user can specify the numerical values ​​for all items, the system may be configured so that the user provides all items of information related to the structure to be inspected (numeric information).

[0077] Next, the temperature information of the inspection target structure 24 input to the defect depth estimation model 44a will be described. As shown in FIG. 14(b), the temperature information is information on the temperature T(kx,ky) of each pixel G of the thermal image NG38, information on the temperature fluctuation ΔT(kx,ky) of each pixel G, or both. As described above, this defect depth estimation step S22 estimates the depth position Zf of the internal defect NK based on the "correlation between the rate of change Rp of the surface temperature and the depth position Zf." Therefore, information that enables estimation (or calculation) of the "rate of change Rp of the surface temperature" of the inspection target structure 24 is required. Therefore, information on at least the temperature T or the temperature fluctuation ΔT is sufficient, and if both information are available, the "rate of change Rp of the surface temperature" can be estimated (or calculated) more efficiently.

[0078] Fig. 15(a) shows an example of temperature information for each time derived at an intermediate stage when the defect depth estimation model 44a outputs the estimation result, i.e., an example of temperature information for each time of the defect area KKR (defect area 1, 2, ...) on the surface of the inspection target structure 24. Then, based on this temperature information, the defect depth estimation model 44a estimates the depth position Zf of each internal defect NK (internal defect 1, 2, ...) and outputs it in a format that is easy for the user to understand, as shown in Fig. 15(b). In addition, since the size of the internal defect NK can be easily calculated, the size of the internal defect NK may also be output.

[0079] <Summary of the Second Embodiment> As described above, according to the internal defect inspection method and internal defect inspection system 42 of this embodiment, it is possible to obtain the same effects as those of the first embodiment, and further, it is possible to estimate the depth position Zf of the internal defect NK occurring in the inspection target structure 24 with high accuracy.

[0080] <<Other embodiments and modifications>> The internal defect inspection method and internal defect inspection system of the present invention are not limited to the above-described embodiment. For example, in the description of the above-described embodiment, the thermal image NG38 of the inspection target structure 24 is captured by the thermal camera 36 mounted on the drone, but the method for capturing the thermal image is free, and the size and number of pixels (Nx × Ny) of the thermal image are not particularly limited.

[0081] In the temperature fluctuation calculation step (temperature fluctuation calculation unit) of the above embodiment, the moving average method is used to calculate the low-frequency drift component Tav(kx,ky) contained in the temperature T of the Nx pixels G arranged in the X-axis direction. However, other methods may also be used. For example, a method can be considered in which the drift component Tav(kx,ky) is calculated by performing polynomial fitting on the temperature T of the Nx images G arranged in the X-axis direction. While the moving average method is very simple and easy to use, if there are many pixels G having temperature information other than the drift component among the specified number of pixels G when calculating the moving average, the drift component cannot be calculated appropriately. In contrast, if a polynomial fitting method is used, the drift component can be calculated more accurately, although the processing becomes somewhat more complicated. Note that although polynomials are preferably used for fitting, various functions such as trigonometric functions, exponential functions, and sigmoid functions may also be used if the conditions are met. Alternatively, fitting processing using a low-pass filter may also be performed.

[0082] Furthermore, the specific contents (items and numerical values) of the model structure related information, inspection target structure summary information, and inspection target structure related information shown in Figures 11, 13, and 14(a) are merely examples, and may be changed as appropriate to suit the method of heat conduction analysis simulation and the characteristics of the structure. [Explanation of symbols]

[0083] 10,42 Internal defect inspection system 12 Temperature fluctuation calculation section 14 Defective pixel extraction unit 16 Defective area identification section 18 Defective area display section 20 Display device 22 Information input device 24 Structures to be inspected 26 Concrete deck 28 Asphalt pavement 30 Road (passage) 38 Inspection Area 40 Binarized Images 44 Defect depth estimation section 44a Defect depth estimation model 46 databases S11 Temperature fluctuation calculation step S12 Defective pixel extraction step S13 Defect area identification step S14 Defect area display step S21 Database Preparation Steps S22 Defect depth estimation step G(kx,ky) pixels KG(kx,ky) defective pixels KKR Defective Area KYB boundary part KZR healthy area NG38 Thermal image of inspection area NK Internal Defect Rp Rate of change of surface temperature T(kx,ky) Temperature of each pixel Tav(kx,ky) Temperature moving average of each pixel Tmean The average temperature of each pixel in the thermal image ΔT(kx,ky) Temperature fluctuation

Claims

1. 1. A method for detecting internal defects in a structure to be inspected, the method being executed by a computer system, comprising: analyzing a thermal image of the upper surface of the structure to be inspected, the upper surface of the structure having an asphalt pavement laid on top of a concrete deck to form a walkway; When the axis parallel to the length direction of the passage is the X axis and the axis parallel to the width direction of the passage is the Y axis, the multiple pixels G that make up the thermal image are arranged in order from the reference point in the X axis direction, Nx pixels, and in order in the Y axis direction. When each pixel G is expressed as pixel G(kx,ky) [1≦kx≦Nx, 1≦ky≦Ny] and the temperature data held by pixel G(kx,ky) is expressed as temperature T(kx,ky), then: a temperature fluctuation calculation step of calculating, for each pixel G, a low-frequency drift component Tav(kx,ky) contained in the temperature T of Nx pixels G arranged in the X-axis direction, and calculating a high-frequency temperature fluctuation ΔT(kx,ky) by subtracting the drift component Tav(kx,ky) from the temperature T(kx,ky); a defective pixel extraction step of extracting, as a defective pixel KG(kx,ky), a pixel G(kx,ky) whose temperature fluctuation ΔT(kx,ky) calculated in the temperature fluctuation calculation step exceeds a reference value Tth; and a defect area identification step of identifying a defect area where an internal defect has occurred in the concrete slab when the structure to be inspected is viewed from above, based on the position information of the defective pixel KG(kx, ky) extracted in the defective pixel extraction step.

2. 2. The internal defect inspection method according to claim 1, wherein the temperature fluctuation calculation step calculates, as the drift component Tav(kx,ky), an average value of the temperatures T of a specified number of pixels G adjacent to pixel G(kx,ky) and arranged in order in the X-axis direction, or an average value of the temperatures T of a specified number of pixels G arranged in the X-axis direction including pixel G(kx,ky).

3. 2. The internal defect inspection method according to claim 1, wherein the temperature fluctuation calculation step calculates the drift component Tav(kx, ky) by performing polynomial fitting on the temperatures T of Nx images G arranged in the X-axis direction.

4. 2. The internal defect inspection method according to claim 1, wherein the defective area specifying step clusters the plurality of defective pixels KG(kx, ky) extracted in the defective pixel extracting step by applying a predetermined clustering algorithm, and the resulting cluster or an area surrounding the cluster is defined as the defective area.

5. 2. The internal defect inspection method according to claim 1, further comprising a defect area display step of designating an area on the top surface of the inspection target structure other than the defect area as a healthy area, and displaying on a display device a binary image in which the healthy area and the defect area are represented in different colors.

6. In the defective pixel extraction step, a plurality of reference values ​​Tth are set and a defective pixel KG(kx, ky) is extracted for each reference value Tth; In the defect area specifying step, the defect area is specified for each reference value Tth; 6. An internal defect inspection method according to claim 5, wherein in the defect area displaying step, the binary images for each reference value Tth are displayed such that the healthy areas are displayed in the same color and the defective areas are displayed in different colors, and these are displayed in an overlapping manner on the display device.

7. In the defective pixel extraction step, a first reference value Tth1 and a second reference value Tth2 are set as the reference values, The first reference value Tth1 is set to a value h1 times the standard deviation σ when the distribution of the Nx temperature fluctuations ΔT(kx, ky) constituting each row is regarded as a normal distribution (h1 is a constant greater than 0); A second reference value Tth2 is set to an arbitrary constant greater than zero; 2. An internal defect inspection method according to claim 1, wherein a pixel G(kx, ky) whose temperature fluctuation ΔT(kx, ky) calculated in the temperature fluctuation calculation step exceeds both reference values ​​Tth1 and Tth2 is extracted as a defective pixel KG(kx, ky).

8. 8. An internal defect inspection method according to claim 1, further comprising a defect depth estimation step of estimating a depth position of the internal defect occurring in the inspection target structure based on a rate of change Rp of temperature fluctuation ΔT(kx, ky) occurring at the boundary between the defect area and the healthy area, or based on a mean value Tmean of temperature T(kx, ky) and the rate of change Rp.

9. a database preparation step of preparing a database in which model structure-related information, which is numerical information about a virtual model structure simulating the inspection target structure, and model structure temperature change information, which is information calculated by a heat conduction analysis calculation using the model structure-related information and indicates the temperature distribution of each part of the model structure, including the defective area and the sound area, are linked to each other and registered; the model structure-related information includes at least structural information indicating the structural characteristics of the model structure, environmental temperature information indicating the characteristics of the environmental temperature at the installation location of the model structure, site information indicating the characteristics related to the amount of solar radiation on the top surface of the model structure, and defect information indicating the presence or absence of internal defects in the model structure and the characteristics of the internal defects; 9. The internal defect inspection method according to claim 8, wherein the defect depth estimation step prepares a defect depth estimation model created by machine learning using the model structure-related information and the model structure temperature change information registered in the database as training data, and estimates the depth position of the internal defect occurring in the inspection target structure by inputting into the defect depth estimation model inspection target structure-related information, which is numerical information corresponding to information other than the defect information in the model structure-related information, and information on the temperature T(kx, ky) of the inspection target structure or information on the temperature change ΔT(kx, ky) calculated in the temperature change calculation step, or both of these information.

10. An internal defect inspection system comprising a computer system for detecting internal defects in an inspection target structure by analyzing a thermal image of the upper surface of the inspection target structure, the upper surface of which is covered with asphalt pavement to form a walkway, the system comprising: When the axis parallel to the length direction of the passage is the X axis and the axis parallel to the width direction of the passage is the Y axis, the multiple pixels G that make up the thermal image are arranged in order from the reference point in the X axis direction, Nx pixels, and in order in the Y axis direction. When each pixel G is expressed as pixel G(kx,ky) [1≦kx≦Nx, 1≦ky≦Ny] and the temperature data at the position of pixel G(kx,ky) is expressed as temperature T(kx,ky), then: a temperature fluctuation calculation unit that calculates, for each pixel G, a low-frequency drift component Tav(kx,ky) contained in the temperature T of Nx pixels G arranged in the X-axis direction, and calculates a high-frequency temperature fluctuation ΔT(kx,ky) by subtracting the drift component Tav(kx,ky) from the temperature T(kx,ky); a defective pixel extraction unit that extracts a pixel G(kx, ky) for which the temperature fluctuation ΔT(kx, ky) calculated by the temperature fluctuation calculation unit exceeds a reference value Tth as a defective pixel KG(kx, ky); and a defect area identification unit that identifies a defect area where an internal defect has occurred in the concrete floor slab when the inspection target structure is viewed from above, based on the position information of the defective pixel KG(kx, ky) extracted by the defective pixel extraction unit.

11. 11. The internal defect inspection system according to claim 10, wherein the temperature fluctuation calculation unit calculates, as the drift component Tav(kx,ky), an average value of the temperatures T of a specified number of pixels G adjacent to pixel G(kx,ky) and arranged in order in the X-axis direction, or an average value of the temperatures T of a specified number of pixels G arranged in the X-axis direction including pixel G(kx,ky).

12. 11. The internal defect inspection system according to claim 10, wherein the temperature fluctuation calculation unit calculates the drift component Tav(kx, ky) by performing polynomial fitting on the temperatures T of the Nx images G arranged in the X-axis direction.

13. 11. The internal defect inspection system according to claim 10, wherein the defective area specifying unit clusters the plurality of defective pixels KG(kx, ky) extracted by the defective pixel extracting unit by applying a predetermined clustering algorithm, and identifies the obtained cluster or an area surrounding the cluster as the defective area.

14. a display device; 11. The internal defect inspection system according to claim 10, further comprising a defect area display unit that defines an area on the top surface of the inspection target structure other than the defect area as a healthy area, and causes the display device to display a binary image in which the healthy area and the defect area are represented in different colors.

15. the defective pixel extraction unit sets a plurality of reference values ​​Tth and extracts a defective pixel KG(kx, ky) for each reference value Tth; the defective area identifying unit identifies the defective area for each reference value Tth; 15. The internal defect inspection system according to claim 14, wherein the defect area display unit displays the binarized images for each reference value Tth in such a way that the healthy areas are displayed in the same color and the defective areas are displayed in different colors, and the images are displayed in an overlapping manner on the display device.

16. 16. An internal defect inspection system according to any one of claims 10 to 15, further comprising a defect depth estimation unit that defines an area on the top surface of the inspection target structure other than the defect area as a healthy area, and estimates the depth position of the internal defect occurring in the inspection target structure based on the rate of change Rp of temperature fluctuation ΔT(kx, ky) occurring at the boundary between the defect area and the healthy area, or based on the mean value Tmean of temperature T(kx, ky) and the rate of change Rp.

17. a database in which model structure-related information, which is numerical information about a virtual model structure that simulates the inspection target structure, and model structure temperature change information, which is information calculated by a heat conduction analysis using the model structure-related information and indicates the temperature distribution of each part of the model structure, including the defective area and the sound area, are linked and registered; the model structure-related information includes at least structural information indicating the structural characteristics of the model structure, environmental temperature information indicating the characteristics of the environmental temperature at the installation location of the model structure, site information indicating the characteristics related to the amount of solar radiation on the top surface of the model structure, and defect information indicating the presence or absence of internal defects in the model structure and the characteristics of the internal defects; 17. The internal defect inspection system of claim 16, wherein the defect depth estimation unit prepares a defect depth estimation model created by machine learning using the model structure-related information and the model structure temperature change information registered in the database as training data, and estimates the depth position of the internal defect occurring in the inspection target structure by inputting into the defect depth estimation model inspection target structure-related information, which is numerical information corresponding to information other than the defect information in the model structure-related information, and information on the temperature T(kx, ky) of the inspection target structure or information on the temperature fluctuation ΔT(kx, ky) calculated by the temperature fluctuation calculation unit, or both of these information.

Images