KR20040020261A - Apparatus for detecting crack in structure by input an image and method of the same - Google Patents

Abstract

PURPOSE: An apparatus and a method for detecting a crack of a structure are provided to precisely detect the width, the length, the position, and the direction of the crack by inputting an image into the crack detecting apparatus. CONSTITUTION: A crack detecting apparatus includes a conveying unit(10), a line illumination unit(20), an image input unit(30) and an encoder(40). The conveying unit(10) is provided to convey the line illumination unit(20), the image input unit(30), a control section and an image processing unit. The line illumination unit(20) radiates laser beam onto a surface of a structure to be inspected thereby forming a line illumination. The line illumination unit(20) has a semiconductor laser, a collimator, a spectrometer, and a plurality of prism and a plurality of mirror members.

Description

Apparatus for detecting crack in structure by input an image and method of the same}

The present invention relates to an apparatus and method for detecting a crack of a structure by an image input. More particularly, the present invention relates to an apparatus and method for detecting a crack of a structure by an image input. It relates to a crack detection apparatus and method of the structure.

In general, most structures, including tunnels, bridges, solders, bridges, and buildings, are damaged by natural and environmental factors, and age over time. In order to maintain the function and extend the life of such aged and damaged structures, continuous management and inspection of the structures is required.

The most basic survey item in performing such inspection and diagnosis is the external survey. Among the external surveys, the investigation of the cracks on the surface of the structure evaluates the condition inside and outside the structure, and the next level of local precision It is a very important factor in determining safety diagnosis performance items, procedures and methods.

Appearance survey method for cracks on the surface of structures The most common and representative method is visual inspection by human attraction.

However, the above-described visual inspection method lacks objectivity because it depends on personal knowledge and experience. In addition, the history of the ambient conditions, as well as the current state of the state can not be accurately understood, and can not be compared with the past inspection results, damage to the invisible parts, such as the interior of the concrete or inaccessible to manpower is experienced Even a technician is not easy to find.

In particular, in the case of concrete cracks, in general, there is no measuring device that can be quantitatively determined, so it is almost impossible to visually confirm the objective data, and in the case of tunnels or large structures of large sections, it is almost impossible to inspect shear surfaces. Do.

Therefore, the maintenance of the structure, as well as visual observation, it is desirable to perform the objective maintenance of the structure by the development of advanced exploration equipment using an automated imaging technique.

Accordingly, the present invention has been devised in view of the above-mentioned points, and it is possible to analyze, store, and output information such as width, length, position, and direction of the crack by automatically measuring the crack of the inspection target structure by image input. It is an object of the present invention to provide a crack detection apparatus and method of a structure by the image input.

1 is an external perspective view of a crack detection apparatus according to a preferred embodiment of the present invention;

2 is an enlarged view of the image input unit shown in FIG. 1;

3 is a schematic structural diagram of the line luminaire shown in FIG.

4 is a control block diagram for controlling the crack detection device shown in FIG.

5 is a flowchart for describing an operation of the control block illustrated in FIG. 4;

FIG. 6 is a flowchart for explaining a detailed process of the image processing step shown in FIG. 5; FIG.

<Explanation of symbols for the main parts of the drawings>

10: transport vehicle 11: wheels

20: line lighting means 21: semiconductor laser

22: collimator 23: spectrometer

24-1 to 24-3: prism 25, 26: mirror member

30: video input means 31: line camera

31-1: Case 32: Dustproof Plate

33: panning control section 34: tilting control section

40: encoder 50: control panel

60: information output unit 70: control unit

80: line light control unit 90: camera control unit

100: image storage unit 110: image processing unit

The apparatus for detecting a crack of a structure by an image input according to the present invention for achieving the above object includes a movement conveying means moving along a selected direction according to a user's operation, and being moved by the movement conveying means to a surface of the structure. Line illumination means for linearly irradiating a laser beam to generate line illumination, and input the surface image of the structure illuminated by the line illumination while being transported by the moving vehicle and generating image information corresponding to the input image. The image input means, position information generating means for generating position information to which the moving vehicle moves, and image information generated by the image input means and position information generated by the position information generating means. Image is saved by moving distance, and the crack is detected by analyzing the stored image information. Depending on the outputs, and storing the image processing means, the user's selection to a controller for controlling the line illumination device and the image input means and said image processing means is characterized in that configured.

According to an aspect of the present invention, there is provided a method of detecting a crack of a structure by an image input, the method comprising: a line illumination step of generating line illumination by linearly irradiating parallel light onto a surface of a structure to be inspected; A surface image acquisition step of acquiring an image of the surface of the projected structure, an edge extraction step of processing the acquired image to obtain local information from the image, and extracting edges of the crack by the obtained local information; The area forming step constituting the area of the crack by the extracted edges, the area labeling step of giving a unique identifier for each area of the formed crack, and the areas adjacent to each other for each area to which the identifier is assigned are connected to each other. Proximity zone connection step defined as a zone, and geometric characteristics of each zone after proximity zone connection A crack screening step for determining the shape and determining cracking based on the obtained feature, a crack measuring step for measuring the width, length, direction and density of the area determined as cracking, and storing information about the measured crack Characterized in that it comprises a step of outputting.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is an external perspective view of a crack detection apparatus according to a preferred embodiment of the present invention. ) And an image input means 30 and an encoder 40.

The moving vehicle 10 moves and carries the line lighting unit 20, the image input unit 30, and a control unit and image processing unit to be described later, and may be, for example, a vehicle such as a truck. Such, the movement carrying means 10 is moved in a predetermined direction along the structure of the inspection object in accordance with the user's operation.

The line lighting means 20 is a semiconductor laser to generate a line illumination by linearly irradiating a laser beam on the surface of the structure to be inspected while being transported by the movement carrying means 10, as shown in FIG. 21, a collimator 22, a spectrometer 23, a plurality of prisms 24-1 to 24-3, and a plurality of polygonal mirror members 25 and 26.

The semiconductor laser 21 generates laser light of a predetermined wavelength, and the collimator optical modular 22 converts the laser light generated from the semiconductor laser 21 into a parallel beam. do.

The beam splitter 23 spectroscopy the laser beam converted from the collimator 22 in different directions, and the plurality of prisms 24-1 to 24-3 are separated from the spectrometer 230. It is used to refract the spectroscopic laser beam and proceed in the desired direction.

The plurality of polygon mirrors 25 and 26 may be polygonal shapes (for example, about four to eight octagons), and may be rotated by forming or attaching mirrors to the side portions around the axis of rotation. ) Or the laser beam that has traveled from the prism 24-3.

Here, the plurality of polygonal mirror members 25 and 26 are arranged in parallel in a straight line, so that the laser beam reflected from each mirror member can cover the entire surface area of the structure of the target object to be inspected. 24-1 to 24-3) are arranged at regular intervals.

That is, each of the polygon mirror members 25 and 26 reflects the laser beams linearly at the reflection angles θ1 and θ2 respectively set by rotation, and the reflection angles of the mirror members 25 and 26 of the polygons are reflected. By arranging (θ1, θ2) so as to overlap at a predetermined point, straight line light L is formed.

In this case, depending on the shape of the structure to be inspected, it may be necessary to further widen or narrow the cover area of the line light L (that is, the linear distance of the line light reflected from the surface of the structure). By increasing or decreasing the number of members, the cover area of the line light L can be increased or decreased.

The image input means 30 inputs the surface image of the structure illuminated by the line illumination formed by the line lighting means 20 and generates image information corresponding to the input image, as shown in FIG. 3. Consists of.

Referring to FIG. 3, the image input means 30 includes a line camera 31 for generating a video signal corresponding to the captured image, and a case 31-in which the line camera 31 is housed. 1), the line camera 31 is fixed to the vibration plate 32 to attenuate shock and vibration, and installed on top of the vibration plate 32, including a rotary motor (not shown), gear means and the like; A panning control unit 33 for controlling the left and right rotation angles of the line camera 31, a line camera 31 installed above the dustproof plate 32 and including a rotating motor (not shown) and gear means. It is configured to include a tilting (Tilting) control unit 34 for controlling the vertical tilt angle of the).

The line camera 31 of the image input means 30 may input an image of an area within a predetermined angle range, and the image input means 30 when the surface of the structure to be inspected is a large cross section (for example, a tunnel). By arranging the image input means 30 at a proper angle in a straight line using a plurality of), to obtain an image of the surface of the structure of the large cross-section.

The encoder 40 is installed on the wheel 11 of the moving vehicle 10 to generate a pulse signal according to the rotation of the wheel (11).

The crack detection apparatus of the present invention configured as described above is controlled by a control block as shown in FIG.

In FIG. 4, the control block for controlling the crack detection apparatus includes an operation unit 50, an information output unit 60, a control unit 70, a line lighting control unit 80, a camera control unit 90, and an image storage unit 100. ) And an image processor 210.

The operation unit 50 is an interface device for a user to input an instruction command for controlling the crack detection device according to the present invention, the information output unit 60 is a device for outputting data on the detected crack, for example The information output unit 60 may be a monitor for displaying crack information or a printer for printing and outputting crack information.

The controller 70 is an encoder 40, a line lighting controller 80, a camera controller 90, an image storage unit 100, and an image processor 210 according to a user's instruction command input through the manipulation unit 50. To control.

The line lighting controller 80 applies power to the motors for rotating the semiconductor laser 21 and the polygon mirror members 25 and 26 according to a control signal applied from the controller 70, and controls the operation thereof. The camera controller 90 controls the operation of the line camera 31 of the image input means 30 and the panning controller 33 and the tilting controller 34 according to a control signal applied from the controller 70. .

The image storage unit 100 detects the position movement of the moving vehicle according to the pulse signal input from the encoder 40 under the control of the control unit 70 and accordingly controls the line image input from each line camera 31. The data is stored and transmitted to the image processor 210, and the crack information according to the result of the image processing by the image processor 210 is stored.

The image processor 210 may control the image information input from each line camera 31 under the control of the controller 70 and stored in the image storage unit 100 and the position information according to the pulse signal input from the encoder 40. Image of the surface of the structure is imaged by each moving distance and stored, and analyzed by the stored image information to detect and output the crack.

The operation of the present invention configured as described above will now be described with reference to the accompanying drawings.

First, the user inputs a test target through the operation unit 50 (S10). For example, information about the type and shape or material of the structure is input.

Next, the user controls the left and right rotation angle and the vertical tilt angle of each image input means 30 according to the shape of the structure to be inspected so that the field of view (FOV) of each line camera 31 can inspect the region to be inspected. Set so that (S20).

Instruction commands input by the user to control the left and right rotation angles and the up and down tilt angles of the respective image input means 30 are input from the control unit 50 to the control unit 70 and from the control unit 70 according to the input command. A control signal for controlling the left and right rotation angle and the vertical tilt angle of each image input means 30 is generated, and the camera controller 90 pans the image input means 30 by the control signal generated from the controller 70. The controller 33 and the tilting controller 34 are controlled to adjust left and right rotation angles and vertical tilt angles of the line cameras 31.

In addition, the user inputs an instruction command for turning on the line illumination through the operation unit 50, and a control signal for turning on the line illumination from the control unit 70 by the input instruction command is a line illumination control unit. 80 is applied.

In response to a control signal applied from the control unit 70, the line lighting control unit 80 applies power to the semiconductor laser 21 of the line lighting unit 20 to turn it on, and rotates the polygon mirror members 25 and 26. By controlling the motor to rotate each polygon mirror member (25, 26).

The laser light generated from the semiconductor laser 21 is converted into a parallel beam in the collimator 22 and then spectroscopically in different directions in the spectrometer 23, of which the laser beam is a polygon The laser beam proceeds to the mirror member 26, and the laser beam spectroscopy in the other direction is refracted by the plurality of prisms 24-1 to 24-3 and proceeds to the polygon mirror member 25.

The laser beams propagated to the polygon mirror members 25 and 26 are linearly reflected by the rotating polygon mirror members 25 and 26 to form line illumination on the surface of the structure to be inspected.

After operating the image input means 30 and the line light means 20 as described above, the user moves the moving conveying means 10 along the inspection target structure. For example, if the structure to be inspected is a tunnel, the process proceeds from the entrance of the tunnel to the exit.

When the moving vehicle 10 is moved as described above, a pulse signal according to the positional movement of the moving vehicle 10 is generated from the encoder 40 to generate a line of the image storage unit 100 and each image input unit 30. Input to the camera 31, the line camera 31 of each image input means 30 inputs a line image of the surface of the structure illuminated by the line illumination and the corresponding image data to the image storage unit 100 Enter (S40).

The image storage unit 100 stores the image data input from each of the line cameras 31 according to the movement speed of the moving vehicle according to the pulse signal input from the encoder 40 (S50), and stores the stored image data. Transfer to the image processing unit 210.

The image processor 210 acquires the crack information on the surface of the structure by processing the image data transmitted from the image storage unit 100 (S60), and a detailed operation thereof is shown in FIG. 6.

As shown in FIG. 6, the image processing unit 210 includes an edge extraction step S61, an area configuration S62, an area labeling step S63, a proximity area connection step S64, a feature calculation and a crack selection step. (S65) and the crack measurement step (S66) are performed.

The corner extraction step S61 will be described below.

An edge usually refers to the edge of an object. From an image processing point of view, an edge is a point where intensity changes in an image are severe. In essence, an image is a two-dimensional distribution of luminance and can be expressed as a function with two independent variables (coordinates of the image plane).

Therefore, the term "the change in intensity is severe" can be expressed by changing the mathematical expression "the magnitude of the gradient vector is large." The gradient vector g at the point (x, y) of the image f is defined as in Equation 1 below.

And its size ( ) And direction psi are defined as in Equation 2 below.

If you only want to know the size of the corner, you can use the Laplacian differential operator as shown in Equation 3 below instead of the gradient.

Here, "h" is a variable defining a function, and more accurately represents a second derivative.

The Laplacian operation as described above has the same value for all directions and has a rotation-invariant characteristic.

In order to apply the above Equations 1 to 3 to the digital image, partial derivatives should be approximated by a difference. In the present invention, the Sobel edge operator is approximated as shown in Equation 4 below.

In this case, since "fx", "fy", and "h" are also two-dimensional functions like the image "f", each operation may be expressed as a convolution as shown in Equation 5 below.

Where Sx is a Convolution Mask.

The above Equations 1 to 5 are summarized as Equation 6 below.

fx = 1 * f (x-1, y-1) + 2 * f (x-1, y) + 1 * f (x-1, y + 1)-[1 * f (x + 1, y- 1) + 2 * f (x + 1, y) + 1 * f (x + 1, y + 1)]

That is, the magnitude of the gradient can be obtained using the Laplacian differential operator, the edge is extracted from the point where the magnitude of the gradient becomes "0", and the direction of the edge can be obtained using the Sobel operator. have.

Next, the above-described area configuration step S62 will be described.

A point having a local minimum between two edges extracted in the edge extraction step S61 is defined as a valley, and in the case of a 2D image, a 1D profile is scanned by scanning in the direction of the edge. Get

At this time, if one of the following conditions (1) to (3) is satisfied while scanning in a direction opposite to the slope of the corner from one corner, the scanning is stopped.

(1) When encountering another corner

(2) In the original image, the luminance of the current pixel is higher than the luminance at the corner

(3) When the scan length is larger than the preset specific value

Among these, in the case of the above condition (1),

If the scan is interrupted by the condition (1), the position representing the local minimum value of the obtained profile is obtained and represented as a valley, and the thickness of the crack is calculated around the valley.

If the scan is interrupted by the above condition (2) or (3), the valley is not displayed. When the scan is interrupted by the double condition (3), not only the computational efficiency is increased but also the non-cracking is prevented. Can be removed. That is, if it is not an elongate region such as a crack, it is removed by the above condition (3).

Next, the area labeling step S63 will be described.

Areas extracted from images should be grouped according to their connection characteristics. The set of pixels connected to each other is found and assigned an identifier (generally expressed as an integer) that is distinct from the same but not connected set. Specific cracks have the same meaning as the set of pixels as described above.

For reference, a fast raster scanning algorithm is known for area labeling, but the label is sparsely distributed unless it requires an additional memory and performs an additional function of condensing the label. As such, it is desirable to label each area through another known technique, Depth First Search (DFS).

Next, in the proximity region connecting step S64, after performing the region labeling step S63, a specific number of pixels are applied to both endpoints of each labeling region for connection with other label regions existing in the adjacent region. The model is modeled as a straight line to find the slope, and when it has the same slope, it is defined as one region.

The characteristic shape calculation and the crack screening step S65 will be described below.

The region extracted by the above steps S61 to S64 may be a cracked region or a non-cracked region. For example, the extracted crack region may be due to a construction step, an artificial mark by an operator, or may be due to noise or stain.

This faulty area must be eliminated. Therefore, in order to distinguish between cracks and non-crackings, geometric features of each region are obtained and cracks are determined based on the geometric features of each region.

For example, areas with very small cracks are caused by noise, areas not elongated are caused by effects such as leaks, and areas that are too straight are formed by attachments such as construction steps or wires. Those that show up are determined to be not cracks.

Next, in the said crack measurement step S66, the width | variety, length, a direction, a density, etc. of the crack are calculated | required about the area | region determined by the crack.

The process of obtaining the width of the crack is as follows.

The width of the points constituting each edge when the area is configured from the edge in the area configuration step S62 has already been calculated. Among them, the width is averaged by applying a filter to remove the outliers.

For example, when the median filter having a length of 5 is applied and the width thereof is averaged, the thickness of the j-th point of the crack is calculated by the following equation (7).

Here, "med" means a median filter.

The length of the crack can be easily calculated as the number of pixels, and the length in the diagonal direction is calculated as 1 in the vertical and horizontal directions.

The direction of the crack is calculated by calculating the secondary area moment that expresses the area distribution on the vertical axis and the horizontal axis as the coordinate values of one region constituting the crack, and is obtained in the direction in which the ratio of these values is maximum.

The crack density is obtained by dividing the crack area in the image by the area of the image obtained within the set distance.

As described above, the crack information obtained through the steps S61 to S66 from the image processing unit 210 is stored in the image storage unit 100 under the control of the control unit 70 and output through the information output unit 60. It becomes (S70).

On the other hand, there is a region overlapping each other the image received by the inspection object through a plurality of cameras, a method of correcting the overlapping region will be described.

If the FOV of each line camera 31 is controlled according to the shape of the inspection object before the inspection starts, the inspection object region that each line camera 31 receives may be set. Through camera calibration that converts actual three-dimensional coordinates into two-dimensional image coordinates, the positions of both points of the FOV of each line camera 31 represent the first and last columns of the two-dimensional matrix image.

Therefore, although overlapping portions of an image received by each line camera 31 can be predicted, an overlapping region is compensated for in the following manner for accurate image merging.

In other words, the image received by the camera A on the left is taken by Aimg, and the image taken by the camera B next to A is defined by the width of Bimg and Aimg, and the location on Aimg in consideration of the FOV of the camera B is defined as Bcol.

Here, Bcol position selection on Aimg can be accompanied by an error, so the margin of about 10%, Bcol position is reset to B'col = Bcol-(Awidth-Bcol) * 0.1 and overlapping is expected in consideration of B'col. The image on Aimg is defined as Aover.

Next, we calculate the variance of the gray value of each row of Aover and the gray value of each column of Bimg, and consider the rows with small variance as overlapping columns, and remove these areas from the Bimg by treating them as overlapping columns.

In this way, the images are combined by removing images overlapping from the left to the right, and one image of the test object is stored.

On the other hand, the process of controlling the imaging sampling rate for the structure to be examined is described as follows.

The imaging sampling rate of the inspection object may be controlled by adjusting the line rate of the line camera 31.

That is, by measuring the speed of the mobile vehicle 10 by the encoder 40 and feeding back the measured signal to the line camera 31, even if the speed of the mobile vehicle 10 changes, a certain interval, that is, per actual scanning distance One line of image can be acquired.

That is, by photographing one line of camera at every preset interval (for example, about 0.3 mm), the image of the predetermined interval may express information about the distance scanned by the moving object.

Encoder 40 is a commercialized device that generates a constant pulse per unit rotation, the radius of the wheel 11 of the mobile vehicle 10 to be mounted encoder 40 is the number of pulses per revolution of the encoder 40 and It is determined by the image precision.

If the radius of the wheel 11 on which the encoder 40 is to be mounted is R, the moving distance is as shown in Equation 8 below.

Distance = number of rows in the imaged image * 2 * π * R / (number of encoder pulses / 1 revolution)

That is, since the position information in the scanning direction can be obtained from the image received through the line camera 31, the crack position in the image can be easily obtained through this.

The present invention is described above by illustrating specific embodiments, but the present invention is not limited to the above embodiments. Those skilled in the art can easily make various changes and modifications to the present invention, and it should be noted that such variations or modifications are included within the scope of the present invention as long as the features of the present invention are used.

As described above, the present invention automatically measures the crack of the inspection target structure by image input, and analyzes, stores, and outputs information such as width, length, position, and direction of the crack, thereby providing objective data on the crack of the structure. It can be secured.

Thus, it is possible to automate defect investigation such as cracking of a structure, which is conventionally performed manually by manpower, so that inspection of the cross section of a large structure such as a tunnel of a large cross section can be performed quickly, simply and easily.

In addition, in the present invention, a line illumination means for linearly irradiating a parallel laser beam as an illumination means for illuminating the surface of the inspection target structure, the line illumination means has excellent illumination uniformity and strength, and inspection at a distance The uniform brightness distribution on the surface of the object significantly reduces the error occurrence rate and has the advantage of not having to increase the number of lighting devices depending on the size of the structure.

Claims (12)

A moving vehicle moving along the selected direction according to the user's operation; A line illumination means for linearly irradiating a laser beam onto the surface of the structure while being moved by the moving vehicle to generate line illumination; Image input means for inputting a surface image of the structure illuminated by the line illumination while being moved by the moving vehicle and generating image information corresponding to the input image; Position information generating means for generating position information to which the movement means moves; According to the image information generated by the image input means and the position information generated by the position information generating means to image the image of the surface of the structure for each moving distance and to store and analyze the stored image information to detect and output the crack and output it Image processing means, And a control unit for controlling the line lighting means, the image input means, and the image processing means according to a user's selection. The method of claim 1, wherein the line lighting means, With a semiconductor laser, A collimator for forming the light generated from the semiconductor laser in a parallel beam shape; A polygon mirror member reflecting the laser beam formed from the collimator; Crack detection device of the structure by the image input, characterized in that the rotating means for rotating the polygon mirror member. According to claim 2, wherein said line illumination means, further provided with a spectroscope for spectroscopy a plurality of laser beams formed from the collimator, so as to extend the angle of the laser beam irradiation, the plurality of polygonal mirror member is provided in a straight line Crack detection device of the structure by the image input, characterized in that arranged at a distance by a predetermined distance. The apparatus according to any one of claims 1 to 3, wherein the video input means comprises: a camera for generating a video signal corresponding to the captured video; A panning controller for controlling the left and right rotation angles of the camera according to the control of the controller; And a tilting control unit configured to control a vertical tilt angle of the camera according to the control of the control unit. The apparatus of claim 4, wherein the image input means comprises a plurality of image input means so as to receive an image having an extended angle. A line illumination step of generating line illumination by linearly irradiating parallel light onto the surface of the structure to be inspected, A surface image acquisition step of acquiring an image of the surface of the structure illuminated by the line illumination; An edge extraction step of processing the obtained image to obtain local information from the image and extracting edges of the crack by the obtained local information; An area configuring step constituting an area of a crack by the extracted edges, An area labeling step of assigning a unique identifier to each area of the constructed crack, A proximity area connection step of defining areas as adjacent areas by connecting areas adjacent to each other with respect to each area to which an identifier has been assigned; A crack screening step of determining whether or not cracks are obtained based on the obtained geometric shapes by obtaining geometrical features of each region after the proximity region connection; A crack measurement step of measuring the width, length, direction, and density of the area determined by the crack; Method for detecting a crack of a structure by an image input comprising the step of storing and outputting information about the measured crack. The method of claim 6, wherein the edge extracting step obtains an inclination vector on the plane coordinates of the image and extracts an edge by finding a point having a high brightness change by the magnitude of the inclination vector. Detection method. The structure according to claim 7, wherein the gradient vector is obtained by extracting tilt direction information from an image using a Sobel Convolution Mask and using a Laplacian Convolution Mask. Crack detection method. 7. The structure according to claim 6, wherein the step of constructing the region comprises configuring a region by defining a valley with a point having a region minimum value between two edges and calculating a thickness of the crack around the valley. Crack detection method. 10. The method of claim 9, wherein the step of forming an area defines a valley by obtaining an area maximum value when a corner meets another corner while scanning in a direction opposite to the slope of the corner. A method for detecting cracks in a structure by image input, wherein the non-cracked element is removed by stopping the scan when the luminous intensity is higher than the luminous intensity at the edge or when the scan length is larger than a predetermined value. The method of claim 6, wherein in the image obtaining step, images are obtained by using a plurality of cameras arranged in a straight line, and the images are combined into one image. When the images overlap, one of the overlapping portions is removed to remove the overlapping portions. Crack detection method of the structure by the image input, characterized in that for processing. 12. The method according to any one of claims 6 to 11, wherein the means for generating the line illumination and the means for acquiring the image are carried on a moving vehicle to acquire the image, while measuring the speed of the moving vehicle and measuring the measurement. The method of detecting a crack of a structure by an image input, characterized in that to obtain an image of one line at a predetermined scanning distance by controlling an image acquisition according to a predetermined speed.