Detection device, detection method, and detection system

Abstract

The presence or absence of an internal back surface structure that cannot be seen from the surface of the object being measured is detected, and the size of that internal back surface structure is estimated. [Solution] The detection device is a detection device for detecting an internal back surface structure that includes at least one of a hollow portion that may exist inside the object to be measured and a notched portion that may exist on the back surface of the object to be measured, and comprises: an image acquisition unit that acquires images of the object to be measured before and after deformation; a strain distribution calculation unit that calculates the strain distribution on the surface of the object to be measured by comparing and analyzing the images of the object to be measured before and after deformation; a strain gradient distribution calculation unit that calculates the strain gradient distribution by differentiating the calculated strain distribution in the distance direction; and an internal back surface structure detection unit that estimates the size of the internal back surface structure by analyzing the calculated strain gradient distribution.

Description

Technical Field

[0001] The present invention relates to a detection device, a detection method, and a detection system.

Background Art

[0002] As a technique for measuring the displacement and strain distribution on the surface of a structure, the digital image correlation method (hereinafter referred to as DIC (Digital Image Correlation)) is known.

[0003] Regarding the strain distribution measurement using DIC, for example, Patent Document 1 describes "forming a feature pattern 5 on a bolt 2 that visually reveals the strain distribution based on the displacement information of the bolt, taking a first image of the feature pattern, applying a known external force to the bolt, taking a second image of the feature pattern, calculating strain values from each of the two captured images of the feature pattern, calculating the difference between these strain values, and inputting the difference in the axial force of the bolt calculated from the external force and the difference in the calculated strain values into a calibration curve showing the relationship between the axial force of the bolt and the strain value of the bolt, calculating the axial force of the bolt, comparing the calculated axial force of the bolt with the target axial force of the bolt, and determining the necessity of reinspection, a bolt axial force inspection method".

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Prior art such as Patent Document 1 is specialized in detecting the surface condition of an object to be measured by applying a characteristic pattern, such as detecting strain distribution or cracks on the surface of the object to be measured, and cannot detect hollow areas that occur inside the object to be measured or notches that occur on the back surface of the object to be measured. Hereinafter in this specification, hollow areas that occur inside the object to be measured and notches that occur on the back surface of the object to be measured will be referred to as the internal back surface structure.

[0006] The present invention has been made in view of the above points, and aims to enable the detection of the presence or absence of an internal back surface structure that cannot be seen from the surface of an object being measured, and to enable the estimation of the size of said internal back surface structure. [Means for solving the problem]

[0007] This application includes several means to solve at least some of the above problems, and some examples are as follows.

[0008] To solve the above problems, a detection device according to one aspect of the present invention is a detection device for detecting an internal back surface structure including at least one of a hollow portion that may exist inside an object to be measured and a notched portion that may exist on the back surface of the object to be measured, comprising: an image acquisition unit that acquires images of the object to be measured before and after deformation; a strain distribution calculation unit that calculates the strain distribution on the surface of the object to be measured by comparing and analyzing the images of the object to be measured before and after deformation; a strain gradient distribution calculation unit that calculates the strain gradient distribution by differentiating the calculated strain distribution in the distance direction; and an internal back surface structure detection unit that estimates the size of the internal back surface structure by analyzing the calculated strain gradient distribution. [Effects of the Invention]

[0009] According to the present invention, it is possible to detect the presence or absence of an internal back surface structure that cannot be seen from the surface of the object being measured, and to estimate the size of the internal back surface structure.

[0010] Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 shows an example of the configuration of an internal back surface structure detection system according to one embodiment of the present invention. [Figure 2] Figure 2 shows an example of an object to be measured. [Figure 3] Figure 3 shows an example of the strain distribution that occurred in the object being measured. [Figure 4] Figure 4 shows an example of the strain distribution in the cross-section corresponding to each of the two line segments shown in Figure 3. [Figure 5] Figure 5 shows the strain gradient distribution corresponding to the strain distribution shown in Figure 4. [Figure 6] Figure 6 is a flowchart illustrating an example of the internal back surface structure detection process using a detection device. [Figure 7] Figure 7 shows an example of the UI screen display. [Figure 8] Figure 8 shows an example of a monochrome image corresponding to the strain gradient distribution obtained by differentiating the strain distribution in Figure 3. [Figure 9] Figure 9 shows an example of a graph illustrating the relationship between a threshold value for comparison with the strain gradient and the length of the white region in a monochrome image. [Figure 10] Figure 10 shows an example graph illustrating the relationship between a threshold value for comparison with the strain gradient and the length of the white region in a monochrome image, which is relevant when the white region in a monochrome image is unclear. [Modes for carrying out the invention]

[0012] One embodiment of the present invention will be described below with reference to the drawings. One embodiment is an example for illustrating the present invention, and has been omitted and simplified as appropriate for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. The position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc., in order to facilitate understanding of the invention. In all drawings illustrating the embodiments, the same reference numeral is used for the same member as a general rule, and repeated explanations are omitted. Also, in the following embodiments, the components (including element steps, etc.) are not necessarily essential unless specifically stated or considered to be clearly essential in principle. Also, when saying "consisting of A," "made of A," "having A," or "including A," other elements are not excluded unless specifically stated that only that element is included. Similarly, in the following embodiments, when referring to the shape, positional relationship, etc. of components, etc., it includes those that are substantially similar or similar to that shape, etc., unless specifically stated or considered to be clearly not the case in principle. Furthermore, "acquisition" shall, as a concrete example, include at least generating, calculating, or receiving from an external source by the subject.

[0013] <Example of configuration of internal back surface structure detection system 10 according to one embodiment of the present invention> Figure 1 shows an example of the configuration of an internal back surface structure detection system 10 according to one embodiment of the present invention. The internal back surface structure detection system 10 measures the strain distribution of the object to be measured 31 based on an image of the object to be measured 31, thereby detecting the presence or absence of internal back surface structures such as hollow areas and notches that may exist inside the object to be measured 31 that are not visible from the surface of the object to be measured 31, and estimating the size of the internal back surface structure.

[0014] The internal back surface structure detection system 10 comprises a detection device 20, an imaging device 30, and a terminal device 40.

[0015] The detection device 20 consists of a general computer such as a personal computer or a server computer. The computer includes a processor such as a CPU (Central Processing Unit), a memory such as a DRAM (Dynamic Random Access Memory), a storage such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), an input device such as a keyboard, a mouse, and a media drive, an output device such as a display, and a communication module such as an Ethernet (trademark) card or a Wi-Fi (trademark) adapter.

[0016] The detection device 20 may be realized by one physical or logical computer, or may be realized by two or more physical or logical computers. The two or more physical or logical computers may be distributed and arranged on the network N respectively.

[0017] The detection device 20 has functional blocks of a processing unit 21, a storage unit 22, a communication unit 23, and a display unit 24.

[0018] The processing unit 21 is realized by the processor of the computer forming the detection device 20. The processing unit 21 controls the whole detection device 20. The processing unit 21 (processor) realizes each functional block of an image acquisition unit 211, a strain distribution calculation unit 212, a strain gradient distribution calculation unit 213, an internal surface structure detection unit 214, and a UI control unit 215 by executing a program (not shown) stored in the storage unit 22.

[0019] The image acquisition unit 211 acquires at least two images of the measurement object 31 before deformation without applying a load and after deformation due to the load from the imaging device 30. The image acquisition unit 211 stores the acquired images in the image DB (database) 221 of the storage unit 22.

[0020] The strain distribution calculation unit 212 calculates the strain distribution occurring on the surface of the object to be measured 31 by comparing and analyzing the images of the object to be measured before and after deformation, which are acquired by the image acquisition unit 211. Conventional techniques such as DIC or sampling moiré method are used to calculate the strain distribution.

[0021] The strain gradient distribution calculation unit 213 calculates the strain gradient distribution on the surface of the object to be measured 31 by further differentiating the strain distribution on the surface of the object to be measured 31, which has been calculated by the strain distribution calculation unit 212, in the distance direction.

[0022] The internal back surface structure detection unit 214 detects the presence or absence of an internal back surface structure by analyzing the strain distribution on the surface of the object 31 to be measured, which is calculated by the strain distribution calculation unit 212. The internal back surface structure detection unit 214 also estimates the size of the internal back surface structure by analyzing the strain gradient distribution calculated by the strain gradient distribution calculation unit 213.

[0023] The UI control unit 215 accepts user input. The UI control unit 215 also displays a UI screen 300 (Figure 7) on the display unit 24, which shows the presence or absence of an internal back structure, the estimated size of the internal back structure, and other relevant information. The UI control unit 215 can also display the UI screen 300 on the terminal device 40.

[0024] The storage unit 22 is implemented by the memory and storage of the computer that constitutes the detection device 20. The storage unit 22 has an image database 221. The image database 221 stores images of the object to be measured 31 acquired from the imaging device 30. In addition to the image database 221, the storage unit 22 may also store various other information and data.

[0025] The communication unit 23 is implemented by a communication module of the computer that constitutes the detection device 20. The communication unit 23 connects to the imaging device 30, terminal device 40, etc., via the network N. The network N is a bidirectional communication network, such as the Internet.

[0026] The display unit 24 is implemented by the output device of the computer that constitutes the detection device 20. The display unit 24 displays the UI screen 300 in accordance with the control of the UI control unit 215.

[0027] The imaging device 30 captures images of the object to be measured 31 in an unloaded state (undeformed state) and in a loaded state (deformed state), and outputs the resulting images to the detection device 20. In this embodiment, the imaging device 30 is connected to the detection device 20 via the network N, but the imaging device 30 and the detection device 20 may be connected directly without using the network N.

[0028] The surface of the object to be measured 31 is pre-treated by applying a random pattern when the strain distribution calculation is performed using DIC, or a periodic pattern when the strain distribution calculation is performed using the sampling moiré method.

[0029] The terminal device 40 consists of a general-purpose computer, such as a personal computer. The terminal device 40 is connected to the detection device 20 via the network N. The terminal device 40 can display, for example, a UI screen 300.

[0030] Next, we will describe the expected internal back structure and its size for the object under measurement 31. Figure 2 shows an example of the object under measurement 31, where the upper part of the figure shows the surface (imaging plane) of the object under measurement 31 in the xy plane, and the lower part of the figure shows the cross-section of the xz plane at the line segment AA' shown in the xy plane of the object under measurement 31 (hereinafter referred to as cross-section AA'; the same applies to other cross-sections).

[0031] A measurement area P is defined on the object to be measured 31, and its surface (hereinafter referred to as surface P) is pre-treated (coated with a random pattern, etc.). The object to be measured 31 has a length L1 in the y direction. On the back surface R of the object to be measured 31 (the surface opposite to surface P), there is an internal back surface structure T with a notched shape, whose length in the y direction is L2 and whose length (width) in the x direction is w. The width w is assumed to be sufficiently small compared to the length L2.

[0032] Figure 3 shows an example of the strain distribution in the x-direction when a tensile load F in the x-direction is applied to the object 31 shown in Figure 2. The magnitude of the strain in the load direction is represented by the intensity of the grayscale image.

[0033] Figure 4 shows an example of strain distribution in the cross-sections corresponding to line segments AA' and BB' shown in Figure 3. The upper solid line in the figure represents the strain distribution of the cross-section AA' in the xz plane corresponding to line segment AA', and the lower solid line in the figure represents the strain distribution of the cross-section BB' in the yz plane corresponding to line segment BB'. In this figure, the dotted line shows the strain distribution when the internal back surface structure T does not exist in the object 31 under measurement in Figure 2.

[0034] As shown by the dotted line in the figure, if the object to be measured 31 does not have an internal back surface structure T, when a tensile load F in the x-direction is applied to the object to be measured 31, tensile strain is generated uniformly in both cross-sections AA' and BB'.

[0035] Conversely, if an internal back surface structure T exists in the object 31 being measured, when a tensile load F in the x-direction is applied to the object 31, deformation occurs in which the internal back surface structure T opens, that is, a concave bending deformation occurs on the surface P side simultaneously. Therefore, on the surface P near the internal back surface structure T, as shown in the upper part of the figure, a compressive strain component due to this bending deformation is added, and a strain distribution is created in a direction that cancels out the tensile strain. Thus, the presence or absence of the internal back surface structure T can be determined by detecting such a characteristic strain distribution.

[0036] However, as shown in the lower part of Figure 4, the strain distribution in cross-section BB' changes continuously, so the range affected by the compressive strain component due to bending deformation is larger than the length L2 of the internal back structure T. Therefore, it is difficult to estimate the length L2 of the internal back structure T from the strain distribution. In this embodiment, the strain gradient distribution is calculated by differentiating the strain distribution in the length direction, and the length L2 of the internal back structure T is estimated using the strain gradient distribution.

[0037] Figure 5 shows the strain gradient distributions calculated by differentiating the strain distributions shown in Figure 4 along the lengthwise direction. The upper solid line in the figure represents the strain gradient distribution of section AA', and the lower solid line represents the strain gradient distribution of section BB'.

[0038] The distance between the minimum and maximum values ​​of the strain gradient distribution, shown by the dashed line in the upper part of the figure, is equivalent to the width w of the internal back structure T. The distance between the minimum and maximum values ​​of the strain gradient distribution, shown by the dashed line in the lower part of the figure, is equivalent to the length L2 of the internal back structure T. Therefore, by calculating the distance between the minimum and maximum values ​​of the strain gradient distribution, the size of the internal back structure T (length L2 and width w) can be estimated.

[0039] <Regarding the internal back surface structure detection process by the detection device 20> Figure 6 is a flowchart illustrating an example of the internal back surface structure detection process by the detection device 20.

[0040] The internal back surface structure detection process is initiated, for example, in response to a predetermined operation by the user.

[0041] First, the image acquisition unit 211 acquires at least two images from the imaging device 30 of the object to be measured 31 before and after deformation due to the load, and stores them in the image DB 221 (step S1).

[0042] Next, the strain distribution calculation unit 212 calculates the strain distribution occurring on the surface of the object to be measured 31 based on the image acquired in step S1 (step S2).

[0043] Next, the internal back surface structure detection unit 214 analyzes the strain distribution calculated in step S2 to determine the presence or absence of an internal back surface structure (step S3). Specifically, it extracts the strain distributions in the orthogonal cross-sections AA' and BB' of the object to be measured 31 and determines the presence or absence of an internal back surface structure based on whether the strain distribution is characteristic of the presence of an internal back surface structure (for example, as shown in the upper part of Figure 4, compressive strain occurs in response to a tensile load).

[0044] If it is determined that an internal back surface structure exists (YES in step S3), the strain gradient distribution calculation unit 213 then calculates the strain gradient distribution on the surface of the object to be measured 31 by further differentiating the strain distribution on the surface of the object to be measured 31 calculated in step S2 in the distance direction (step S4).

[0045] Next, the internal back surface structure detection unit 214 estimates the size (length and width) of the internal back surface structure by analyzing the strain gradient distribution calculated in step S4 (step S5). Specifically, it calculates the distance between the minimum and maximum values ​​in the strain gradient distribution.

[0046] Next, the UI control unit 215 displays the presence or absence of an internal back surface structure and the size of the internal back surface structure on the UI screen 300 (step S6).

[0047] In step S3, if the internal back surface structure detection unit 214 determines that there is no internal back surface structure (NO in step S3), steps S4 and S5 are skipped and the process proceeds to step S6. In this case, in step S6, the UI control unit 215 displays on the UI screen 300 that there is no internal back surface structure in the object to be measured 31. This concludes the explanation of the internal back surface structure detection process by the detection device 20.

[0048] Figure 7 shows an example of the UI screen 300 displayed on the display unit 24. The UI screen 300 includes a display area 301 for displaying an image of the surface P of the object under measurement 31, and a display area 302 for displaying the presence or absence and size of the internal back structure. The user can check the presence or absence and size of the internal back structure of the object under measurement 31 using the UI screen 300. In addition to the presence or absence and size of the internal back structure, the UI screen 300 may also display a strain distribution diagram and a strain gradient distribution diagram. Furthermore, the UI screen 300 may also display the confidence level for the estimated size of the internal back structure, which will be described later.

[0049] <Other methods for estimating the size of the internal back surface structure using the internal back surface structure detection unit 214> In the internal back surface structure detection process described above, the distance between extremes in the strain gradient distribution was calculated as an estimate of the size of the internal back surface structure. However, the size of the internal back surface structure may be estimated by other methods.

[0050] For example, the strain gradient distribution may be binarized to generate a monochrome image, and the size of the internal back surface structure may be estimated based on this monochrome image.

[0051] Figure 8 shows an example of a monochrome image obtained by differentiating the strain distribution of the surface P of the object 31 shown in Figure 3 to calculate the strain gradient distribution, and then further binarizing it. Here, the strain gradient G is defined as the magnitude of the vector obtained by differentiating the strain ε of the surface P of the object 31 in the x and y directions, respectively, as shown in the following equation. G = √((dε / dx)) 2 +(dε / dy) 2 )

[0052] In this monochrome image, areas where the absolute value of the strain gradient G is greater than or equal to a predetermined threshold (including the threshold value of the strain gradient G) are shown in white, and areas where the absolute value of the strain gradient G is less than the predetermined threshold are shown in black. The method for selecting the predetermined threshold will be described later.

[0053] Then, in the monochrome image, the length L3 in the y-direction of the white region is measured by labeling image processing, which extracts adjacent pixels having the same pixel value (white pixel value) as connected components, and the length L2 of the internal back surface structure T is estimated in a simplified manner by considering this length L3 as the length L2 of the internal back surface structure T.

[0054] Regarding the threshold for comparison with the strain gradient G, it is desirable to pre-select a value that provides high estimation accuracy for the length L2 of the internal back structure T, for example, based on past estimation results. However, if it is difficult to select a threshold, such as when there are no past estimation results, the threshold should be intentionally changed, and the length L2 of the internal back structure T should be estimated based on the length L3 of the white region, which changes accordingly.

[0055] Figure 9 illustrates a method for estimating the length L2 of the internal back surface structure T when no threshold has been selected for comparison with the strain gradient G.

[0056] The figure shows an example of a graph where the horizontal axis represents a threshold for comparison with the strain gradient G, and the vertical axis represents the length L3 of the white region in a monochrome image corresponding to each threshold on the horizontal axis. The threshold on the horizontal axis is changed at regular intervals from 0 to the point where all pixels in the monochrome image become black, and the range from the maximum to the minimum value of the corresponding white region length L3 is defined as the evaluation range. Next, based on the assumption that the white region length L3 changes around the true value, the median value of the white region length L3 in the evaluation range is taken as the estimated value of the internal back surface structure length L2, thereby allowing for a simple estimation of the internal back surface structure length L2 using this assumption.

[0057] Furthermore, as mentioned above, in a simplified estimation method in which the threshold for comparison with the gradient G is intentionally changed, if the white area does not appear clearly and it is difficult to measure its length L3, that is, if the existence of the internal back surface structure T is unclear, some estimated value L2 may be output as the length L2 of the internal back surface structure T, and in some cases this may differ significantly from the true value. Therefore, it is desirable to be able to easily evaluate the reliability of the estimated value L2 as well.

[0058] Therefore, in this embodiment, as an indicator for evaluating the reliability of the estimated value L2, we focus on the continuity of the graph showing the relationship between the threshold value for comparison with the gradient G shown in Figure 9 and the length of the white area.

[0059] When the white areas in a monochrome image are clearly defined, as shown in the graph in Figure 9, the length L3 of a specific white area decreases as the threshold increases, resulting in a shape that continuously decreases to the right.

[0060] Figure 10 shows an example graph illustrating the relationship between a threshold value for comparison with the strain gradient G, which corresponds to the case where the white region in a monochrome image is unclear, and the length L3 of the white region in the monochrome image.

[0061] As shown in the figure, when the white areas in a monochrome image are unclear, each change in the threshold changes the area recognized by the image processing, increasing the probability that the graph will become discontinuous. Therefore, the internal back surface structure detection unit 214 evaluates the relative reliability of the estimated length L2 of the internal back surface structure T by evaluating the continuity of the graph. This reliability may be displayed to the user, for example, on the UI screen 300 (Figure 7).

[0062] A useful method for evaluating the continuity of the graph is to focus on the change in the length L3 of the white region δ when the threshold is changed at regular intervals, and to consider locations where the change in δ exceeds a specified value as discontinuities. Various methods can be considered for quantifying discontinuities, such as the ratio of the number of discontinuities to the number of times the threshold is changed during binarization. In this example, if the interval of threshold change is small and the number of data points is large, the ratio of discontinuities decreases, and differences become less apparent. Therefore, it is desirable to use the change in δ for evaluation, for example, by defining the discontinuity as the sum of the changes in δ at discontinuities.

[0063] The present invention is not limited to the embodiments described above, and various modifications are possible. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace or add to the configurations of one embodiment with those of another embodiment. [Explanation of Symbols]

[0064] 10...Internal back surface structure detection system, 20...Detection device, 21...Processing unit, 211...Image acquisition unit, 212...Distribution calculation unit, 213...Gradient distribution calculation unit, 214...Internal back surface structure detection unit, 215...UI control unit, 22...Storage unit, 221...Image DB, 23...Communication unit, 24...Display unit, 30...Imaging device, 31...Object to be measured, 40...Terminal device, 300...UI screen, 301...Display field, 302...Display field

Claims

1. A detection device for detecting an internal back surface structure that includes at least one of a hollow portion that may exist inside the object to be measured and a notched portion that may exist on the back surface of the object to be measured, An image acquisition unit that acquires images of the object to be measured before and after deformation, A strain distribution calculation unit calculates the strain distribution on the surface of the object to be measured by comparing and analyzing the images of the object to be measured taken before and after deformation, A strain gradient distribution calculation unit calculates the strain gradient distribution by differentiating the calculated strain distribution in the distance direction, An internal back surface structure detection unit analyzes the calculated strain gradient distribution to estimate the size of the internal back surface structure, A detection device equipped with the following features.

2. A detection device according to claim 1, The internal back surface structure detection unit calculates the distance between extreme values ​​in the strain gradient distribution as an estimate of the size of the internal back surface structure. Detection device.

3. A detection device according to claim 1, The internal back surface structure detection unit calculates the size of the region in the monochrome image obtained by binarizing the strain gradient distribution where the pixel value is greater than or equal to a threshold, as an estimate of the size of the internal back surface structure. Detection device.

4. A detection device according to claim 3, The internal back surface structure detection unit calculates the median size of the region, which changes in response to the change in the threshold used when binarizing the strain gradient distribution, as an estimate of the size of the internal back surface structure. Detection device.

5. A detection device according to claim 4, The internal back surface structure detection unit evaluates the reliability of the estimated size of the internal back surface structure. Detection device.

6. A detection device according to claim 5, The system includes a UI control unit that displays an estimated size of the internal back structure and at least one of the confidence level of the estimated size on a UI screen. Detection device.

7. A detection device according to claim 5, The internal back surface structure detection unit calculates the median of the size of the region that changes in response to the change in the threshold when the strain gradient distribution is binarized, as an estimate of the size of the internal back surface structure, and evaluates that the higher the continuity of the size of the region that changes in response to the change in the threshold, the higher the reliability. Detection device.

8. A detection device according to claim 7, The internal back surface structure detection unit evaluates the continuity of the size of the region based on the sum of the changes in the size of the region that change in response to minute changes in the threshold. Detection device.

9. A detection method using a detection device for detecting an internal back surface structure that includes at least one of a hollow portion that may exist inside the object to be measured and a notched portion that may exist on the back surface of the object to be measured, An image acquisition step to acquire images of the object to be measured before and after deformation, A strain distribution calculation step in which the strain distribution on the surface of the object to be measured is calculated by comparing and analyzing the images of the object to be measured taken before and after deformation, A strain gradient distribution calculation step, which involves calculating the strain gradient distribution by differentiating the calculated strain distribution in the distance direction, An internal back surface structure detection step in which the calculated strain gradient distribution is analyzed to estimate the size of the internal back surface structure, A detection method that includes [details omitted].

10. A detection system comprising an imaging device, a detection device, and a terminal device, for detecting an internal back surface structure including at least one of a hollow portion that may exist inside an object to be measured and a notched portion that may exist on the back surface of the object to be measured, The imaging device is The object to be measured is imaged before and after deformation. The detection device is An image acquisition unit that acquires an image captured by the aforementioned imaging device, A strain distribution calculation unit calculates the strain distribution on the surface of the object to be measured by comparing and analyzing the images of the object to be measured taken before and after deformation, A strain gradient distribution calculation unit calculates the strain gradient distribution by differentiating the calculated strain distribution in the distance direction, An internal back surface structure detection unit analyzes the calculated strain gradient distribution to estimate the size of the internal back surface structure, The terminal device is equipped with a UI control unit that causes the terminal device to display a UI screen showing an estimated value of the size of the internal back surface structure, The aforementioned terminal device is Display the aforementioned UI screen Detection system.

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