JP2012202858A - Closure crack detection method of concrete surface - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically detect a closure crack by performing image processing on original image data obtained by imaging a concrete surface.SOLUTION: Image processing is performed on original images in a plurality of rectangular processing regions obtained by dividing upper, lower, left and right rectangular processing regions so as to be overlapped just by an area covering the size of a closure crack estimated generally. A crack line segment is extracted with a subpixel precision equal to or higher than an imaging resolution. Fine particles in areas smaller than or equal to a fixed resolution are removed, and proximate line segments are connected with each other, thereby producing a closed region. Labeling is then performed on all the regions, it is determined whether coordinate values in upper, lower, left and right terminal portions in each region are settled within a range of the rectangular processing region and if the coordinate values are settled within the range, a closure crack is determined.

Description

  The present invention relates to a method for image processing of original image data obtained by imaging a concrete surface such as a tunnel lining surface and detecting a closed crack generated on the concrete surface.

As shown in FIG. 2 of Patent Document 1, various deformations such as closed cracks, cross cracks, and parallel cracks occur on the concrete surface such as the tunnel lining surface. Since these deformations are caused by the deterioration of the tunnel, it greatly affects the determination of the soundness of the tunnel. In particular, when the cracks are connected to each other and a closed crack that is a geometrically closed region is generated, there is an increased risk of the falling of the concrete in that portion.
As a method for detecting a crack on the concrete surface, for example, as shown in the following patent document, a method for extracting a general crack by image processing original image data obtained by imaging the concrete surface has been developed. However, no method has been developed for extracting closed cracks that cause flaking by image processing. This is because, when a noise component having a similar shape such as a cable overlaps with a crack, it is very difficult to extract only the crack well by a normal image processing method.

Patent Document 2 discloses a crack detection method using wavelet transform. This crack detection method is basically identification based on the luminance distribution, but the detection accuracy of cracks in the vicinity of the boundary between the bright part and the dark part decreases, and parts that are not clearly cracked, such as joints and cables, are judged as cracks. There are problems such as. Moreover, there is also the trouble that a threshold table must be prepared in advance.

In the crack detection method disclosed in Patent Document 3, a target surface is photographed while changing the irradiation angle of a light source, and cracks are to be detected from the dispersion of luminance values. It is necessary to shoot many times while changing the position of the light source, the reflected light is easily affected by the material and dirt on the concrete surface, and in order to obtain images continuously, install multiple light sources or install a camera. There is a problem that a plurality of image capturing devices are required.

Patent Document 4 also discloses a crack detection method using wavelet transform. In this crack detection method, an area formed by a plurality of black pixels is specified as a crack area, but there is a problem that the detection accuracy decreases under a dark background such as dirt or water leakage.

Patent Document 5 describes a sub-pixel processing technique for measuring a fine crack width after identifying a background and a crack portion while comparing a background color density database with a color density of a target point. This crack detection method has a problem that the detection accuracy of a crack portion is lowered under a complicated background such as dirt or water leakage.

A concrete internal defect diagnosis system using a non-contact inspection method is an extremely effective method as a quantitative method to replace the conventional hammering inspection. However, the non-contact method does not act on the area to be inspected on the concrete surface, but acts on the part visually identified by the worker. However, it is not always certain to visually identify the closing crack candidate portion, and it takes a long working time. Therefore, it is desired to develop a method for specifying a closed crack candidate portion as quickly and objectively as possible.

In short, closed area concrete has the risk of falling and must be preferentially discovered. That is, from the knowledge that the cracked closed area of the concrete wall must be carefully inspected, the area extracted by image processing of the concrete wall image is very likely to be a closed area, that is, highly dangerous. The development of an algorithm that fully reflects the knowledge of concrete structure specialists, so that the location can be accurately identified, is desired. In particular, for the closed cracks that occur in the tunnel zenith and arch, which may have a large effect on train travel if concrete pieces fall, the detailed position can be accurately grasped by the adjustment function of the above extraction sensitivity. High-precision detailed inspection is possible.

Japanese Patent No. 4279159 Japanese Patent No. 4006007 Japanese Patent No. 4186117 Japanese Patent No. 4488308 Japanese Patent No. 4292095

An object of the present invention is a closed crack inspection method that can supplement visual inspection or become part of mechanization, and obtain original image data obtained by imaging a closed crack generated on a concrete surface such as a tunnel lining surface. To provide a closed crack detection method for automatically extracting closed cracks by image processing.

In order to solve the above problems, the present inventor divides a developed image of a tunnel wall surface into a plurality of rectangular processing regions in the vertical and horizontal directions, taking the tunnel wall surface as an example, and generally assumes the processing regions in the vertical and horizontal directions. It has been noted that by overlapping only the area that covers the size of the closed crack to be formed, even if a closed crack is applied to the boundary of a certain processing region, it always falls within one of the upper, lower, left and right processing regions.

A closed crack detection method for solving the above-mentioned problem is a closed crack detection method for a concrete surface, in which an image processing is performed on an original image for each of a plurality of rectangular processing areas obtained by dividing a developed image of a tunnel wall surface to extract closed cracks The upper, lower, left, and right processing regions are overlapped by an area that covers a generally assumed size of a closed crack.
In a plurality of rectangular processing areas obtained by dividing the developed image of the tunnel wall surface, the rectangular processing area at the top of the wall image is made larger in both the cross-sectional direction and the extension direction of the tunnel than the other processing areas. Closure cracks generated at the top are reliably extracted.

More specifically, the closed crack detection method that solves the above-mentioned problem is a method of removing a wall attachment such as a cable from an original image obtained by imaging a tunnel wall surface, which is a processing area, and performing an inpainting process on the removed wall attachment area. Then, a pre-processing step for generating a tunnel wall surface image from which unnecessary objects are removed, and a closed area are generated by performing image processing on the tunnel wall image from which unnecessary objects have been removed, and the coordinate values of the upper, lower, left and right edges of the area It comprises a post-processing step of determining whether or not the rectangular processing region is within the range and detecting a closed crack.

The pre-processing step includes a step of registering a wall surface attachment in a computer as a template, a step of extracting a wall surface additive from an original image of a rectangular processing region by performing shape-based pattern matching corresponding to a change in scale, and extracting A step of inpainting an extraction region of the wall surface attachment with surrounding tunnel wall surface pixels while considering a texture around the wall surface attachment.

The post-processing step is a step of extracting a crack line segment with sub-pixel accuracy equal to or higher than the imaging resolution with respect to the tunnel wall surface image from which unnecessary objects are removed, removing fine particles having a certain area or less by blob (region) analysis, and The step of connecting adjacent line segments to generate a closed region, the step of labeling all the regions and specifying the coordinate values of the upper, lower, left and right ends of each region, the coordinate values of the ends are It is determined whether it is within the range of the rectangular processing region, and if both are within the range, the region is determined to be a closed crack.
Focusing on the opinions of civil engineers and field workers that the risk of concrete falling is high when cracks are combined to form a closed area, extracting areas that do not reach the edge of the image processing area In particular, this is a detection method that has become the key to extracting closed cracks.

The solution described above is based on the knowledge that concrete with closed area has a risk of falling and needs to be detected preferentially, that is, the closed area must be inspected carefully. The region extracted from the image by image processing is very likely to be a closed region, that is, it is a closed crack detection method that sufficiently reflects the knowledge of a concrete structure specialist, which can accurately identify highly dangerous places. is there.

According to the present invention, there is provided a closed crack detection method for automatically extracting closed cracks by subjecting closed cracks generated on a concrete surface such as a tunnel lining surface to image processing of original image data obtained by imaging the concrete surface. Therefore, since the present invention can record and analyze the situation of the closed crack, it is extremely effective in practical use as a supplementary method of the visual inspection method of the closed crack or to realize a part of the mechanization of the visual inspection. It has become possible to save labor and improve efficiency. Incidentally, the current visual field inspection work performed at 1 to 2 km / h can be performed 5 to 10 times faster without causing eye fatigue when the present invention is adopted.
In addition, for tunnel zeniths and arches, which may have a large impact on train travel if concrete pieces fall, a high weight is given to the result extracted from the area, so that Deformation can be reliably extracted.
Further, since the data of the image processing result is recorded and stored, it can be used as objective data for tunnel health diagnosis. The recorded data is repeated in the room, evaluated by the parties concerned, and used effectively for specifying the detailed inspection location.
Furthermore, according to the present invention, a closed crack inspection method for improving the efficiency of a concrete internal defect diagnosis system using a non-contact inspection method is provided.

It is a flowchart which shows the flow of a process of the closure crack detection of the concrete surface which concerns on this invention as an example for a tunnel wall surface. It is the figure which showed typically an example of the rectangular screen which divided | segmented the expansion | deployment image of the tunnel wall surface into the size of the process area | region suitable for closed crack detection. It is the figure which showed typically an example of the screen on which the closed crack 10 exists in the rectangular screen which divided | segmented the expansion | deployment image of the tunnel wall surface into the size of the processing area suitable for closed crack detection. It is the figure which showed the closed crack 10 typically. It is a concrete wall surface image in which a closing crack is generated as an original image. It is a concrete wall image from which crack components were extracted with subpixel accuracy. It is the concrete wall surface image from which the minute line segment was removed. It is a concrete wall surface image in which a closed region generated by connecting adjacent line components is displayed. It is a concrete wall surface image in which a closed region having a certain area or more is displayed without touching the periphery of the processing region. It is a concrete wall image on which the finally detected closing crack is displayed. It is the concrete wall surface image in which the extraction process of the cable area | region was performed. It is the concrete wall surface image in which the extraction process of various structures was performed. It is the concrete wall image which repaired the cable area automatically in the background. It is the concrete wall image which repaired structure areas, such as a reinforcement metal fitting, automatically in the background.

The present invention is based on the original image of a plurality of rectangular processing regions obtained by dividing the developed image of the tunnel wall surface by overlapping the processing regions on the top, bottom, left, and right by an area that covers a generally assumed size of the closed crack. A closed crack detection method for detecting closed cracks by performing image processing, wherein a wall surface attachment such as a cable is removed from an original image obtained by imaging a tunnel wall surface, which is the rectangular processing area, and an area of the removed wall attachment is determined. It comprises a pre-processing step for generating a tunnel wall image from which unnecessary objects have been removed by in-painting, and a post-processing step for detecting a closed crack by performing image processing on the tunnel wall image from which unnecessary objects have been removed. To do.

FIG. 1 is a flowchart showing a flow of processing for detecting a crack on a concrete surface according to the present invention, taking a tunnel wall surface as an example. First, the original image input to the image processing apparatus by the worker is converted into a grayscale image of 256 gradations by the image processing apparatus.
As will be described later, this original image is an image obtained through a preprocessing step described later, and the frame, dirt, cables, and other attachments existing on the wall surface of the tunnel are removed from the screen and removed. This is an image obtained by repairing an area using a surrounding image. That is, it is a wall surface image from which noise components for detection objects such as closed cracks and water leakage are removed.

The original image is a rectangular processing region as shown in FIG. 2 in which a developed image of the tunnel wall surface, which is captured so that the zenith portion 11 called a tunnel crown portion is located at the center, is divided into sizes suitable for processing. In FIG. 2, it is divided into three types of rectangular processing areas A, B, and C. The maximum size processing region A is the wall surface of the arch portion 12 centered on the tunnel zenith 11, the minimum size processing region C is the wall surface around the lower end 13 of the tunnel side wall, and the intermediate size processing region B is the tunnel. The arch part 12 is set so as to cover the periphery of the side wall part. The reason why the size of the rectangular processing area A is set to the maximum is to reliably extract the closed cracks generated around the zenith portion 11 of the tunnel. This is because the closing crack generated around the zenith 11 of the tunnel is a cautionary deformation.

In short, in an image taken of a concrete surface, etc., the processing area divided into a certain size for image processing is particularly dangerous if cracks occur in that area, and it is necessary to extract it with higher sensitivity. The size of the region is set to be large for a certain place, or the size of the region is set to be large for the direction that needs to be extracted with higher sensitivity.

As shown in the right side of FIG. 2, the tunnel wall development image is divided into a maximum size processing region A in the middle, a middle size processing region B above and below it, and a minimum size processing region C above and below it. Generally, they are arranged so as not to overlap each other. In other words, the developed image of the tunnel wall surface shows the edge of the wall surface of the tunnel cross section from the lower end portion 13 of one side wall portion to the lower end portion 13 of the other side wall portion through the zenith portion 11 and the other side from the one side entrance. It is a large rectangular image having the horizontal length of the tunnel length to the doorway, and this large rectangular image is generally divided into 5 × M pieces. M is an integer and is a value obtained by dividing the length of the tunnel by the length of the side in the horizontal direction of the processing area.

On the other hand, in the present invention, as shown on the left side of FIG. 2, the developed image of the tunnel wall surface is divided so as to overlap the rectangular processing areas of the upper, lower, left, and right sides. The size of the overlap is d1 in the vertical direction and d2 in the horizontal direction. If the vertical direction is defined as a row and the horizontal direction is defined as a column, the processing area is increased by one in the row direction by partially overlapping the upper, lower, left, and right rectangular processing areas, and the column direction is considerably larger than M. The number increases to N, and the total number is 6 × N. In the example shown on the left side of FIG. 2, the processing areas in the first column are C, B, A, A, B, and C from the top, and these overlap the upper and lower processing areas by the length d1. The processing areas in the second column are C, B, A, A, B, and C from the top, and they overlap with the upper and lower processing areas by the length d1, and the length of the processing area in the left first column. It overlaps by d2. Similarly, the processing areas in the third and lower columns overlap the upper, lower, left, and right processing areas by a predetermined length.

In this way, by providing a plurality of rectangular processing regions obtained by dividing the developed image of the tunnel wall surface by overlapping the processing regions on the top, bottom, left, and right by an area that covers the size of the generally assumed closing crack. As shown in FIG. 3, the closing crack 10 comes to be located within the range of one of the processing regions. In other words, since the rectangular processing areas of the top, bottom, left, and right are set so as to overlap the area that covers the size of the generally assumed closed crack, the closed crack 10 is generated at any location. However, it falls within the range of any rectangular processing area. In the present invention, the rectangular processing areas on the top, bottom, left, and right are set so as to overlap by an area that covers the generally assumed size of the closed crack, and image processing is performed on the original image of the rectangular processing area. The closed crack is detected.

First, the image processing apparatus performs a smoothing process on the photographic resolution gray value using, for example, a filter mask having a width of 31 × height 31 (S1).

Next, the smoothed image is subjected to dynamic threshold processing that is not easily affected by uneven brightness or changes in brightness (S2). That is, the original image and the smoothed image are compared, and a region where the value obtained by subtracting the offset value from the gray value of the smoothed image is equal to or greater than the gray value of the original image is extracted. The offset value is set to 5, for example.

Next, an opening process for contracting and expanding the same number of times is performed to remove small isolated noise (S3). In the opening process, for example, C is 5 times, B is 4 times, and A is 3 times for the processing areas C, B, A, A, B, and C shown in FIG. The number of contractions and expansions decreases as the time goes on. As a result, when the concrete pieces fall, the area A of the tunnel zenith and arch, which may have a large effect on train travel, can suppress excessive compression of information compared to the surrounding area. Adjustment is possible. In short, the extraction sensitivity adjustment function based on the part that can grasp the deformation of the point requiring attention by changing the weight and evaluating the result extracted from the processing area of the part important for the management of the concrete structure. It is equipped with.

Next, a crack component is extracted from the image after the opening process with subpixel accuracy equal to or higher than the imaging resolution (S4). When extracting crack components with sub-pixel accuracy, B is more than C and A is more than B than processing regions C, B, A, A, B, and C shown in FIG. The edge extraction threshold is adjusted so that the fine crack width can be extracted. As a result, a finer cracking component is extracted in the area A of the tunnel zenith and the arch where there is a possibility of having a great influence on the train running if the concrete piece falls. Can be adjusted. In short, the extraction sensitivity adjustment function based on the part that can grasp the deformation of the point requiring attention by changing the weight and evaluating the result extracted from the processing area of the part important for the management of the concrete structure. It is equipped with.

Next, a crack component is extracted from the image after the opening process with subpixel accuracy equal to or higher than the imaging resolution (S4).

Subsequently, fine particles having an area equal to or smaller than a certain value are removed by blob (region) analysis, and adjacent line components are connected to each other to generate a closed crack candidate region (S5).

Labeling is performed on all the extracted regions including the candidate region for the closed crack, and a point 10a having a leftmost column coordinate, a point 10b having a topmost row coordinate, and a bottommost row coordinate of each region And the coordinate value of the point 10c having the rightmost column coordinate and the point 10d having the rightmost column coordinate are specified (S6).

Finally, the image processing apparatus performs the following process on each area of the closed crack candidate area to detect the closed crack.

That is, the image processing apparatus determines whether or not the point 10a having the leftmost column coordinate of the candidate region for the closed crack is on the left frame 15 of the rectangular processing region (S7), and if NO, step (S8). Proceed to
In step S8, it is determined whether or not the point 10b having the uppermost row coordinate of the closed crack candidate area is on the upper frame 16 of the rectangular processing area, and if YES, it is determined as a non-closed crack (S12). If NO, the process proceeds to step (S9).
In step S9, it is determined whether or not the point 10c having the row coordinate at the lowermost end of the closed crack candidate area is on the lower frame 17 of the rectangular processing area. If YES, it is determined as a non-closed crack (S12). If NO, the process proceeds to step (S10).
In step S10, it is determined whether or not the point 10d having the rightmost column coordinate of the closed crack candidate region is on the right frame 18 of the rectangular processing region. If NO, the closed crack is determined (S11). Exit.

If the determination result in any of step S6, step S7, step S8, and step S9 is YES, the image processing apparatus determines whether or not it is the last region of the closed crack candidate region extracted in the original image that is currently image-processed. (S12). If NO, the process returns to step S7. If the decision result in the step S12 is YES, the process is ended.

By performing such a determination process, it is determined whether or not the closed crack candidate region is within the rectangular processing region, and if it is determined to be within the range, the closed crack candidate region is determined as a closed crack. To decide.

Next, a preprocessing process for a concrete wall surface image such as a railway tunnel will be described.

In addition to deformations such as cracks and water leaks, the concrete wall image of a railway tunnel shows many attachments such as molds, dirt, and cables. Usually, not only the target deformation, but also image processing acts uniformly on the entire screen, so that only cracks cannot be selectively processed. In the case of extracting a crack based on the general feature of “a line darker than the background”, it is possible to improve the extraction accuracy by performing the process after removing similar noise.

The pre-processing step is image processing for removing a portion unnecessary for deformation extraction from a captured image of a concrete wall surface and automatically repairing the removed region (defect region).
First, the image processing apparatus converts the input original image into a 256 gray scale image. The original image is obtained by dividing a developed image of a tunnel wall surface imaged so that a zenith portion called a tunnel crown portion is located substantially in the center into rectangles having a size suitable for processing as shown in FIG.

Next, image processing is performed to extract a horizontal wall surface structure such as a lower bundle for suspending cables and wires from the ceiling. That is, a horizontal structure is extracted by shape-based pattern matching corresponding to a change in scale using a template of a horizontal structure to be extracted that is registered in advance in the image processing apparatus.
FIG. 11 is a concrete wall image that has been subjected to cable region extraction processing.

Next, image processing is performed to extract vertical structures such as reinforcing brackets, repair plates, and fluorescent lamps. That is, a vertical structure is extracted by shape-based pattern matching corresponding to a change in scale using a template of a vertical structure to be extracted that is registered in advance in the image processing apparatus.
FIG. 12 is a concrete wall image on which various structures are extracted by pattern matching.

The above-described structure extraction processing does not use the brightness density, but uses the contour data that defines the characteristics of the object and the gradient data of the density value in the normal direction. This realizes extremely robust search even for images that are concealed or disturbed.

Note that a method of registering a template of a structure in an image processing apparatus by an operator is performed as follows, for example. Here, a reinforcing metal fitting will be described as an example of the wall surface attachment.
First, a pattern matching model is created from the reinforcing metal fittings in the image. Next, the region of the reinforcing metal fitting is extracted from the image. Then, the marker area is designated with the mouse. Subsequently, the designated marker area is expanded. This is because the outer edge of the reinforcing metal fitting is also used as a model. Subsequently, the image size of the reinforcing bracket is reduced in order to fit the model to the actual size. Finally, the reinforcing metal fittings extracted in this way are registered in the image processing apparatus with three levels of resolution (pyramids).

Subsequently, automatic restoration of the extraction region of the horizontal wall surface attachment is performed. Further, the automatic restoration of the extraction region of the vertical wall surface attachment is performed. In this automatic restoration, the extracted area of the wall surface supplement is complemented with the surrounding tunnel wall surface pixels while considering the texture around the wall surface extracted by pattern matching. Copy from the complete portion of the surrounding image until the area is covered with new gray values. As a result, a tunnel wall surface image without the wall surface attachments is generated.
FIG. 13 is a concrete wall image obtained by automatically repairing the cable area in the background, and FIG. 14 is a concrete wall image obtained by automatically restoring the wall attachment area such as a reinforcing bracket in the background.

10 Closed crack 10a Point 10b having the leftmost column coordinate of the closed crack 10b Point 10c having the highest row coordinate of the closed crack 10d Point 10d having the lowest row coordinate of the closed crack Has the right lower column coordinate of the closed crack Point 11 Tunnel zenith 12 Tunnel arch 13 Lower edge 15 of tunnel side wall 15 Left frame of rectangular processing area 16 Upper frame of rectangular processing area 17 Lower frame of rectangular processing area 18 Right frame of rectangular processing area A A rectangular processing area of the arch centered on the zenith portion obtained by dividing the developed image of the tunnel wall surface B A rectangular processing area around the side wall portion from the arch portion obtained by dividing the developed image of the tunnel wall surface Rectangle processing area at the bottom

Claims (12)

In a method for detecting closed cracks on a concrete surface that performs image processing for each processing area divided into a certain size and extracts closed cracks in an image of a concrete surface, etc., the processing areas on the top, bottom, left and right are generally assumed. A method for detecting a closed crack on a concrete surface, characterized by overlapping an area covering the size of the closed crack. In an image of a concrete surface, etc., where the processing area is divided into a certain size for image processing, it is particularly dangerous if cracks occur in that area, and it is necessary to extract it more sensitively. On the other hand, the size of the region is set to be large, and the method for detecting a closed crack on the concrete surface according to claim 1. In an image taken of a concrete surface, etc., it is particularly dangerous when cracks occur in a certain direction with respect to the processing area divided into a certain size for image processing, and the direction that needs to be extracted with higher sensitivity On the other hand, the size of the region is set to be large, and the method for detecting a closed crack on the concrete surface according to claim 1. In the pre-processing work when detecting closed cracks, the process of registering the concrete surface support in the computer as a template, the shape-based pattern matching corresponding to the change in scale, and the processing area divided into the predetermined size A series of processes including the process of extracting the concrete surface attachment from the image and the process of inpainting the extraction area of the concrete surface attachment with the surrounding pixels of the surrounding image while considering the texture around the extracted concrete surface attachment A closed crack detection method for a concrete surface characterized by combining as follows. 5. The method for detecting a crack on a concrete surface according to claim 4, wherein the pretreatment method includes a step of registering a concrete surface support in a computer as a template used for shape-based matching. In the pre-processing method, referring to a template registered in the computer, shape-based pattern matching corresponding to a change in scale is performed to extract a concrete surface attachment from the processing region divided into the predetermined sizes. The method for detecting a closed crack on a concrete surface according to claim 4, further comprising a step of: In the pre-processing method, the method includes inpainting the extracted region of the concrete surface attachment with the peripheral pixels of the photographed image while considering the surrounding texture of the extracted concrete surface attachment. The method for detecting a closed crack on a concrete surface according to claim 4. 5. A closed crack detection on a concrete surface, comprising: a step of extracting a crack line segment with sub-pixel accuracy equal to or higher than a shooting resolution for a shot image from which unnecessary objects are removed by the preprocessing method according to claim 4. Method. In the closed crack detection method of the concrete surface, for the crack line segment extracted with sub-pixel accuracy, a line segment having a certain area or less is removed by image analysis, and adjacent line segments are connected to form a closed region. A method for detecting a closed crack on a concrete surface, comprising the step of generating. In the method for detecting a crack on a concrete surface, the method includes a step of assigning an identification number to the closed area and specifying coordinate values of upper, lower, left and right ends of each closed area. Detection method. In the closed crack detection method for the concrete surface, it is determined whether or not the coordinate value of the end of the closed area is within the range of the processing area divided into the certain size, and if both are within the range A method for detecting a closed crack on a concrete surface, comprising: a process for determining that the region is a closed crack. 9. The closed crack detection method according to claim 8, wherein the result extracted from the processing region of the part important for the management of the concrete structure is evaluated by changing the weight to ensure the deformation of the point requiring attention. The method for detecting a closed crack on a concrete surface according to claim 1, further comprising an extraction sensitivity adjustment function based on a part to be grasped.