Defect storage device, defect storage method, and defect storage program

Abstract

The defect storage device allows users to input defects on an image captured by a camera, and then automatically input the defects on a drawing of the object.In this case, rather than relying solely on image recognition technology, sensors such as LiDAR are used to identify the target structure, making it possible to remove noise and accurately identify the structure.This aims to accurately input and store the location of defects on a drawing. [Solution] The defect storage device is a device that stores defect locations in a structure, and has a defect input unit that accepts input of defect locations in images acquired from a camera, and a mapping unit that estimates where the defect locations are located on a pre-selected drawing.

Description

[Technical Field]

[0001] The present disclosure relates to a defect storage device, a defect storage method, and a defect storage program. [Background technology]

[0002] In the fields of architecture and civil engineering, defects such as cracks in concrete and peeling paint can occur when working on buildings and structures. Whether it's new construction or repair work, defects that exist after construction is complete must be repaired to within a specified range. Previously, these defects were checked manually, with defective areas noted on blueprints and other documents, and repairs confirmed upon completion. However, advances in information technology have made it possible to support this series of tasks with computers, reducing the burden on workers as much as possible and improving work efficiency.

[0003] For example, Patent Document 1 discloses a technique for detecting cracks from image data obtained by photographing the surface of a structure. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2024-80934 Summary of the Invention [Problem to be solved by the invention]

[0005] Patent Document 1 uses machine learning to detect cracks from image data, making it possible to detect defects without manual intervention. This reduces the burden on workers. On the other hand, going further, if it becomes possible to automatically determine the location of defects such as cracks on the drawing when they are detected, it would be possible to further reduce the burden on workers and increase work efficiency.

[0006] The present disclosure has been proposed in consideration of the above-mentioned problems, and aims to provide a defect storage device that, when a defect in a structure is detected, captures the shape of the structure around the defect and maps where the defect is located on the drawing. [Means for solving the problem]

[0007] In order to achieve the above object, the defect storage device of the present disclosure has a defect input unit that accepts input of a location where a defect exists in an image acquired from a camera, and a mapping unit that estimates where the location of the defect is located on a pre-selected drawing.

[0008] In order to achieve the above-mentioned object, the defect storage method of the present disclosure includes a defect input step of accepting input of a location of a defect in an image acquired from a camera, and a mapping step of estimating where the location of the defect is located on a pre-selected drawing.

[0009] In order to achieve the above object, the defect storage program of the present disclosure includes a defect input step for accepting input of a location of a defect in an image acquired from a camera, and a mapping step for estimating where the location of the defect is located on a pre-selected drawing. [Effects of the Invention]

[0010] According to the present disclosure, by using a sensor that uses reflected laser light to capture the shape of the structure around the defect, it is possible to grasp the shape of the structure more accurately than by relying on image recognition, and by accurately matching it with the drawing, it is possible to accurately map where the defect is located on the drawing. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing a scene in which the defect storage device 10 is used. [Figure 2] FIG. 2 is a diagram showing the hardware configuration of a defect storage device 10. [Figure 3]FIG. 2 is a block diagram showing the basic functions of the defect storage device 10. [Figure 4] FIG. 10 is a diagram showing an elevation view as an example of a drawing read by the defect storage device 10. [Figure 5] 10 is a diagram showing an example of image data acquired by the defect storage device 10 from a camera. FIG. [Figure 6] FIG. 10 is a diagram showing an example of defective locations extracted by the defect storage device 10. [Figure 7] 10 is a diagram showing an example of point cloud data acquired by the defect storage device 10. FIG. [Figure 8] 10 is a flowchart showing an example of control processing performed by the defect storage device 10. DETAILED DESCRIPTION OF THE INVENTION

[0012] The present disclosure will now be described with reference to the drawings.

[0013] (Usage of the defective storage device 10) 1 is a diagram showing a scene in which the defect storage device 10 is used, and shows a scene in which defects such as cracks in a structure are stored. The structure 20 is shown as an example, but it may be a building or other structure, such as a bridge. It may also be a movable property (construction) such as a machine, and any building, structure, or movable property (construction) for which drawings exist may be a target.

[0014] The defect storage device 10 is directed toward the structure 20 to be searched for in the drawings, and acquires an image in the direction of the structure 20 as input data using a sensor that detects reflected laser light and a camera or the like.

[0015] (Hardware configuration of the defective storage device 10) 2 is a diagram showing the hardware configuration of the defect storage device 10. The defect storage device 10 can be configured using a general-purpose computer.

[0016] As shown in FIG. 2, the fault storage device 10 includes a processor 11, a memory 12, a storage 13, a communication IF 14 (note that IF is written as an abbreviation for Interface, the same applies hereinafter), and an input / output IF 15.

[0017] The processor 11 is hardware for executing an instruction set written in a program, and is composed of an arithmetic unit, a register, a peripheral circuit, and the like.

[0018] The memory 12 is for temporarily storing programs and data to be processed by the programs, and is realized by a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0019] The storage 13 is a storage device for saving data such as programs, and is realized by, for example, a flash memory, an SSD (Solid State Drive), or an HDD (Hard Disk Drive).

[0020] The communication IF 14 is an interface for transmitting and receiving signals so that the defect storage device 10 can communicate with other devices. For example, if a database of drawing data is stored in a database server on a network, the defect storage device 10 may acquire the drawing data via the communication IF 14. However, the drawing data can also be stored in the storage 13, for example, and the communication IF 14 is not an essential component of the defect storage device 10.

[0021] The input / output IF 15 functions as an interface for an image input device such as a camera, a sensor for detecting reflected laser light, input devices such as a keyboard, mouse, and numeric keypad for receiving input from the user, and an output device such as a display for presenting information to the user. The defect storage device 10 acquires image data 32 from a camera or the like and data from a sensor for detecting reflected laser light, so the input / output IF 15 is connected to these devices to send and receive data.

[0022] 3 is a block diagram for explaining the basic functions of the defect storage device 10. The defect storage device 10 includes a communication unit 110, a storage unit 120, and a control unit .

[0023] The communication unit 110 performs processing for communicating with other devices. For example, when a database exists externally via a network, the communication unit 110 may acquire data such as drawings via the communication unit 110. Note that the communication unit 110 is not an essential component.

[0024] The storage unit 120 stores data held by the defect storage device 10. For example, the storage unit 120 includes a drawing database 121 and stores drawings to be searched. The storage unit 120 may also store a program to be executed by the defect storage device 10.

[0025] The control unit 130 controls the operation of the defect storage device 10. Specifically, the control unit 130 includes a drawing selection unit 131, a defect input unit 132, an extraction unit 133, a distance estimation unit 134, a mapping unit 135, and a counting unit 136.

[0026] The drawing selection unit 131 selects a drawing of the structure 20 that is the target of defect detection.

[0027] The plan selection unit 131 may select the plan of the structure 20 by acquiring information on the plan selected by the user via the input / output IF 15, for example.

[0028] For example, the plan selection unit 131 may automatically select a plan of the structure 20. For example, the plan may include location information, and the plan may be selected using location information from a global positioning system (GPS).

[0029] The plan selection unit 131 may be configured to, for example, acquire an image of the structure 20 and then automatically select a plan that matches the image. This may be achieved by using existing technology such as machine learning.

[0030] The plan selection unit 131 may be configured to, for example, acquire the shape of the structure 20 using a sensor and then automatically select a plan that matches it.

[0031] The defect input unit 132 receives and acquires input of a location where a defect exists in an image acquired from a camera.

[0032] The defect input unit 132 acquires, for example, a location that a user has input as a defect in an image acquired from a camera. The user may input the location of a defect in the image using a pointing device such as a mouse, a touchpad, or a touch panel, and the defect input unit 132 may acquire the location (position) of the defect.

[0033] For example, the defect input unit 132 may acquire the location of the defect after the user inputs the location of the defect by eye-gaze input.

[0034] The extraction unit 133 extracts the range of the defect location from the defect location acquired by the defect input unit 132. For example, the extraction unit 133 uses machine learning to extract the range of the defect from the defect location acquired by the defect input unit 132. A defect corresponds to a crack in concrete or peeling of a paint film, and generally, the defect is not a point but has a certain range. The extraction unit 133 may extract the range of the defect from image data around the defect, including the defect, using a machine learning model that has previously learned the range of the defect using image data or the like.

[0035] The extraction unit 133 may extract the range of defects not only by machine learning but also by using a method generally used in the field of cognitive engineering.

[0036] The extraction unit 133 functions in conjunction with the defect input unit 132, and when the user inputs the defect range using a pointing device or the like, the defect input unit 132 may acquire the defect location and the extraction unit 133 may acquire the defect range.

[0037] The distance estimation unit 134 extracts a plurality of feature points from the structure 20 and estimates the distance between the plurality of feature points. For example, the distance estimation unit 134 uses LiDAR (Light Detection and Ranging) to irradiate a laser in the direction of a defect in the structure 20 and detect the reflected light with a sensor, thereby acquiring point cloud data.

[0038] The distance estimation unit 134 performs processing to detect the contour and shape of an object by using image processing techniques or the like to enhance contours from the point cloud data. Then, feature points are extracted from the shape of the object. When an object has a contour, feature points are the endpoints of the lines or curves that make up the contour. In other words, feature points are points where multiple lines intersect, points where a line and a curve intersect, and points where curves intersect. These include, for example, points where a line or curve intersects with another line or curve, forming a corner.

[0039] After extracting the feature points as described above, the distance estimation unit 134 uses a sensor that senses reflected light using LiDAR as a base point and measures the distance and angle to the extracted feature points.

[0040] The distance estimation unit 134 uses LiDAR to measure the distance and angle between the base point and each feature point, and then uses this information to calculate the distance between the feature points.

[0041] When extracting the distances between feature points, the distance estimation unit 134 does not need to calculate the distances for all combinations of feature points, but only needs to calculate the distances between adjacent feature points along the contour (outer shape), in other words, the distances of the straight lines or curves that make up the contour (outer shape).

[0042] The distance estimation unit 134 is expected to achieve higher accuracy by extracting feature points and contours of an object using an optical sensor such as LiDAR, compared to extracting contours using image recognition technology.

[0043] First, the mapping unit 135 reads the drawing selected by the drawing selection unit 131 from the drawing database 121. At this time, distances between feature points may be stored in advance as numerical values ​​in the drawing data, and such distances between feature points may also be acquired at the same time. Furthermore, when distances between feature points are not stored as numerical values ​​in the drawing data, they may be calculated from the distances between feature points drawn on the drawing, for example, by the following method.

[0044] The feature points grasped by the mapping unit 135 from the drawing data are points that can be regarded as the end points of the lines or curves that make up the drawing. That is, points where multiple lines intersect to form corners, points where lines and curves intersect, and points where curves intersect are regarded as feature points.

[0045] The mapping unit 135 selects any two adjacent feature points for one drawing read from the drawing database 121 and calculates the distance on the drawing. In other words, the mapping unit 135 calculates the distance between the straight lines or curves that make up the drawing. Then, for the read drawing, the mapping unit 135 calculates the lengths of all the straight lines and curves that make up the drawing.

[0046] Secondly, the mapping unit 135 selects any one straight line or curve from the read drawing, and calculates the ratio between that line and the adjacent straight line or curve.

[0047] Thirdly, the mapping unit 135 aligns a space made up of feature points generated based on the image data acquired from the camera and the point cloud data read from the sensor by calibration, and maps the location of the defect onto the space made up of feature points (on the point cloud data read from the sensor). At this time, the mapping unit 135 may calculate the scale ratio between the image data acquired from the camera and the image data read from the sensor.

[0048] Fourth, the mapping unit 135 determines whether the ratio of adjacent straight lines or curves selected from the drawing matches the ratio of two adjacent straight lines or curves formed by the feature points calculated by the distance estimation unit 134.

[0049] When the ratio of adjacent lines or curves selected from the drawing matches the ratio of two adjacent lines or curves calculated by the distance estimation unit 134, the mapping unit 135 determines whether the ratio of adjacent lines or curves in the drawing matches the ratio of further adjacent lines or curves calculated by the distance estimation unit 134, continues the search as long as there is a match, and finally calculates the number of matches. At this time, when determining whether the ratio of adjacent lines or curves selected from the drawing matches the ratio of two adjacent lines or curves calculated by the distance estimation unit 134, a threshold may be set and an error within the threshold may be allowed.

[0050] If the ratio of adjacent lines or curves selected from the drawing does not match the ratio of two adjacent lines or curves formed by the feature points calculated by the distance estimation unit 134, the mapping unit 135 shifts any line or curve that starts the search on the drawing and sets it as a line or curve, and then performs a similar search.

[0051] The mapping unit 135 changes the line or curve that starts the search on the drawing, determines whether it matches the ratio of the line or curve formed by the feature points calculated by the distance estimation unit 134, and determines the matching degree of that starting point as the value with the highest number of matches.

[0052] Fifth, the mapping unit 135 determines that the straight line or curve with the highest degree of match in the selected drawing represents the configuration of the drawing around the defect input by the defect input unit 132. Then, using the coincidence between the distance ratio of the straight lines or curves constituting the structure on the drawing and the distance ratio of the straight lines or curves estimated by the distance estimation unit 134, the mapping unit 135 matches the shape of the defect periphery acquired by the sensor with the drawing. That is, the mapping unit 135 compares the distance ratio between the feature points calculated by the distance estimation unit 134 with the distance ratio of the straight lines or curves constituting the object on the selected drawing, and matches the point with the largest number of matching straight lines or curves as the point captured by the sensor on the drawing. Since the mapping unit 135 aligns the image data and point cloud data in the third process, it is possible to identify the defect location on the drawing.

[0053] The mapping unit 135 may calculate the scale ratio from the ratio between a straight line or curve on the drawing and the straight line or curve estimated by the distance estimation unit 134. In other words, since the scale ratio is stored in the drawing, the scale ratio of the straight line or curve estimated by the distance estimation unit 134 may be calculated from the scale ratio of the drawing using the ratio between the straight line or curve on the drawing and the straight line or curve estimated by the distance estimation unit 134.

[0054] The mapping unit 135 aligns the position of the defect location with the drawing in an area based on point cloud data acquired using a sensor, thereby enabling matching while eliminating information that becomes noise in the image data, such as background and color. Furthermore, since the matching of the image data and point cloud data acquires data of approximately the same area, accurate alignment can be achieved.

[0055] The counting unit 136 may store data obtained by trimming images of defective areas extracted by the extraction unit 133 in the defect database 122. Alternatively, the counting unit 136 may count the number of defective areas and store the total number.

[0056] The counting unit 136 may calculate the area of ​​the defect from the data obtained by trimming the image of the defect-containing portion extracted by the extraction unit 133 and store the calculated area in the defect database 122.

[0057] To calculate the area of ​​the location where the defect extracted by extraction unit 133 exists, tallying unit 136 calculates the area of ​​the location where the defect extracted by extraction unit 133 exists on the image input from the camera. Then, if tallying unit 136 can calculate the ratio between the scale on the image input from the camera and the scale on the drawing, it can calculate the actual area of ​​the location where the defect exists.

[0058] The counting unit 136 calculates the scale of the image data acquired from the camera from the scale of the drawing, using the ratio of the scale of the image data acquired from the camera calculated by the mapping unit 135 to the scale of the point cloud data read from the sensor, and the ratio of the straight line or curve on the drawing calculated by the mapping unit 135 to the straight line or curve estimated by the distance estimation unit 134. In this way, the counting unit 136 calculates the area of ​​the location where a defect exists in the image input from the camera.

[0059] At construction sites, manual work is often required to tally up the locations (positions) of defects, their number, and their area, so if the tallying unit 136 can automatically tally up the defects, it will be possible to reduce the burden of manual work.

[0060] (Specific example of processing performed by control unit 130) 4 shows an elevation 31 of a certain structure 20. The drawing selection unit 131 selects a drawing of the structure 20 for which defects are to be stored, and selects, for example, a drawing such as that shown in FIG.

[0061] 5 is a diagram showing an example of an image acquired from a camera by the defect storage device 10. The defect input unit 132 inputs a defective portion (defect 323 in FIG. 5) into the image data 32 as shown in FIG.

[0062] 6 is a diagram showing an example in which the extraction unit 133 extracts a defective portion 331 from a defect 323 in the image data 32. The extraction unit 133 extracts the defective portion 331 using a frame such as a rectangle for the defect in the image data.

[0063] Fig. 7 shows an example of point cloud data obtained by using technology such as LiDAR (Light Detection and Ranging) to irradiate a laser in the direction of a defect in structure 20 and detect the reflected light with a sensor. For example, when LiDAR is used on a structure such as that shown in Fig. 4, Fig. 7 shows the detection of the periphery of frame 314. In reality, points 341-342-344-343, 345-346-348-347 are detected as a point cloud, but when the endpoints of the straight lines or curves are extracted, they become 341, 342, 343, 344, 345, 346, 347, and 348.

[0064] The distance estimation unit 134 extracts corner points from the point cloud data and calculates the distance between adjacent corner points, as shown in Fig. 7. In the example of Fig. 7, the distance estimation unit 134 calculates the distances between 341 and 342, 342 and 344, 344 and 343, 343 and 341, 345 and 346, 346 and 348, 348 and 347, and 347 and 345.

[0065] The mapping unit 135 estimates and maps the position on the elevation 31 of the defect 323 in the image data 32. The mapping unit 135 reads a drawing such as the elevation 31 selected by the drawing selection unit 131, obtains or calculates distance information of the straight lines or curves that make up features such as 312 to 316, and calculates their ratios.

[0066] The mapping unit 135 calibrates the image data 32 acquired from the camera and the point cloud data 34 acquired from the LiDAR to understand the correspondence relationship. At this time, the ratio between the image data 32 and the point cloud data 34 (for example, image data 32:point cloud data 34=1:R1) is calculated.

[0067] The mapping unit 135 detects that the ratio of each of the sides of 341-342-344-343 in the point cloud data 34 is similar to the ratio of the side of 314 in the elevation 31, but does not match those of 312-313 and 315-316. Also, the mapping unit 135 detects that the ratio of each of the sides of 345-346-348-347 is similar to the ratio of the side of 316 in the elevation 31, but does not match those of 312-315. From this, it is determined that the point cloud data 34 captures the areas around 314 and 316 on the elevation 31, and mapping of the point cloud data 34 and the elevation 31 is performed.

[0068] By the above processing, the image data 32 and the point cloud data 34 are calibrated, and the position of the defect 323 can be grasped on the point cloud data 34, and the point cloud data 34 and the elevation 31 are mapped, and the position of the defect 323 can be grasped. As a result, the position of the defect 323 on the image data 32 can be grasped on the elevation 31.

[0069] The mapping unit 135 calculates the ratio of the point cloud data 34 to the elevation data 31 (for example, point cloud data 34:elevation data 31=1:R2) from the distance of the point cloud data 34 and the distance of the elevation data 31. As a result, the ratio of the image data 32:elevation data 31 becomes 1:R1R2.

[0070] The counting unit 136 may count the number of defective locations extracted by the extraction unit 133, and may count the total number of defective locations in the structure 20, etc.

[0071] The aggregation unit 136 may calculate the area of ​​the defect, for example, by calculating the area of ​​the defect 323 in the image data 32 on the image data and multiplying it by the square of the ratio R1R2 (because it is an area), thereby calculating the actual area.

[0072] By performing the above-described processing, it becomes possible to incorporate information about defects in the image data captured by the camera onto the drawing.

[0073] Furthermore, when attempting to align (map) the defect location between image data captured by a camera and a drawing, there is a risk of accuracy decreasing because the image data contains a lot of information that can become noise. On the other hand, by mapping the object shape based on point cloud data acquired by sensors such as LiDAR with the drawing, it becomes possible to map the location of the detected defect on the drawing with greater accuracy.

[0074] (Processing flow) An example of a control process in which the defect storage device 10 executes defect detection will be described below with reference to FIG.

[0075] The control unit 130 of the defect storage device 10 selects a drawing of the structure 20 for which defects are to be stored based on a user input or using information from a camera or sensor (step S101).

[0076] The control unit 130 of the defect storage device 10 acquires the location of the defect on the image data input from the camera using a pointing device, eye-gaze input, etc. Alternatively, the location of the defect may be acquired automatically using machine learning, etc. (Step S102).

[0077] The control unit 130 of the defect storage device 10 extracts the range of the defect. For example, examples of defects in a structure include cracks in concrete and peeling paint, and the range of these is extracted (step S103).

[0078] The control unit 130 of the defect storage device 10 estimates the distance between adjacent feature points (points that are the end points of a line or a curve) from point cloud data acquired using LiDAR or the like (step S104).

[0079] The control unit 130 of the defect storage device 10 calibrates the drawing data and the point cloud data, and further matches the point cloud data with the drawing data using the degree of agreement between the ratio of the distances between the feature points of the point cloud data and the ratio of the distances between the feature points of the drawing data as an index, and maps the defects on the image data onto the drawing data (step S105).

[0080] The control unit 130 of the defect storage device 10 calculates and tally the total number and area of ​​defect locations (step S106).

[0081] (Explanation of effect) The configuration of the defect storage device 10 has been described above, but by using data acquired using LiDAR or the like, it is possible to project the location of defects in image data onto a drawing. Simply attempting to map image data onto drawing data makes it difficult to perform accurate mapping due to the effects of noise and distance distortion, so the method disclosed herein makes it possible to improve accuracy.

[0082] While the preferred embodiments of the present disclosure have been described above, the present disclosure is not limited to such specific embodiments, and includes the disclosure set forth in the claims and their equivalents. Furthermore, the device configurations described in the above embodiments and modifications can be combined as appropriate as long as no technical contradiction occurs. [Explanation of symbols]

[0083] 10...defect storage device, 11...processor, 12...memory, 13...storage, 14...communication IF, 15...input / output IF, 20...structure, 30...input device, 31...elevation, 32-33...image data, 34...point cloud data, 110...communication unit, 120...memory unit, 121...drawing database, 130...control unit, 131...measurement unit, 132...defect input unit, 133...extraction unit, 134...distance estimation unit, 135...mapping unit, 136...aggregation unit, 311-316...frame, 321-322...frame, 323...defect, 331...defect location, 341-348...feature point

Claims

1. A device for storing locations of defects in a structure, a defect input unit that receives input of a location where a defect exists in an image acquired from the camera; a mapping unit for estimating where the defect is located on the preselected drawing.

2. A device for storing locations of defects in a structure, a defect input unit that receives input of a location where a defect exists in an image acquired from the camera; a distance estimation unit that acquires, from a sensor, reflected light of a laser directed at a location of the structure where the defect is present, extracts a plurality of feature points, and calculates the distances between the feature points; a mapping unit that aligns the image acquired from the camera with a space consisting of the feature points by calibration, maps the location of the defect onto a section consisting of the feature points, and estimates the location of the defect on a preselected drawing using the ratio of the distances of straight lines or curves on the preselected drawing and the ratio of the distances between the feature points calculated by the distance estimation unit.

3. A device for storing locations of defects in a structure, a defect input unit that receives input of a location where a defect exists in an image acquired from the camera; a distance estimation unit that acquires, from a sensor, reflected light of a laser directed at a location of the structure where the defect is present, extracts an outline of the structure, extracts a plurality of points that are endpoints of straight lines or curves as feature points, and calculates the distances between the feature points; a mapping unit that aligns the image acquired from the camera with a space consisting of the feature points by calibration, maps the location of the defect onto a section consisting of the feature points, and estimates the location of the defect on a preselected drawing using the ratio of the distances of straight lines or curves on the preselected drawing and the ratio of the distances between the feature points calculated by the distance estimation unit.

4. A device for storing locations of defects in a structure, a defect input unit that receives input of a location where a defect exists in an image acquired from the camera; an extraction unit that extracts a range where the defect exists from the image acquired by the camera; a distance estimation unit that acquires, from a sensor, reflected light of a laser directed at a location of the structure where the defect is present, extracts a plurality of feature points, and calculates the distances between the feature points; a mapping unit that aligns the image acquired from the camera with a space consisting of the feature points by calibration, maps the location of the defect onto a section consisting of the feature points, and estimates the location of the defect on a preselected drawing using the ratio of the distances of straight lines or curves on the preselected drawing and the ratio of the distances between the feature points calculated by the distance estimation unit.

5. A device for storing locations of defects in a structure, a defect input unit that receives input of a location where a defect exists in an image acquired from the camera; an extraction unit that extracts the range of the defect from the image acquired by the camera using a model that has been trained in advance by machine learning; a distance estimation unit that acquires reflected light of a laser directed toward a location of the structure where the defect is present using a sensor, extracts a plurality of feature points, and calculates the distances between the feature points; a mapping unit that aligns the image acquired from the camera with a space consisting of the feature points by calibration, maps the location of the defect onto a section consisting of the feature points, and estimates the location of the defect on a preselected drawing using the ratio of the distances of straight lines or curves on the preselected drawing and the ratio of the distances between the feature points calculated by the distance estimation unit.

6. A device for storing locations of defects in a structure, a defect input unit that receives input of a location where a defect exists in an image acquired from the camera; an extraction unit that extracts the range of the defect and the type of the defect from the image acquired by the camera; a distance estimation unit that acquires reflected light of a laser directed toward a location of the structure where the defect is present using a sensor, extracts a plurality of feature points, and calculates the distances between the feature points; a mapping unit that aligns the image acquired from the camera with a space consisting of the feature points by calibration, maps the location of the defect onto a section consisting of the feature points, and estimates the location of the defect on a preselected drawing using the ratio of the distances of straight lines or curves on the preselected drawing and the ratio of the distances between the feature points calculated by the distance estimation unit.

7. 7. The defect storage device according to claim 4, further comprising a counting unit that stores data obtained by trimming the images of the defect locations extracted by the extraction unit and the total number of defect locations.

8. 7. The defect storage device according to claim 4, further comprising a calculation unit that calculates the area of ​​the defect using the range of the defect extracted by the extraction unit and the scale of the drawing mapped by the mapping unit.

9. A method for storing locations of defects in a structure, comprising: a defect input step of receiving input of a location where a defect exists in the image acquired from the camera; a mapping step of estimating where the defect is located on a preselected drawing.

10. A program that stores locations of defects in a structure, a defect input step of receiving input of a location where a defect exists in the image acquired from the camera; and a mapping step of estimating where the defect is located on the preselected drawing.

Images