KR100892767B1 - System and Method for Shape Simulation of a Suspension Bridge - Google Patents

Abstract

A digital camera for taking an image; A photographing module configured to photograph one or more images at different photographing angles by controlling photographing angles of the digital camera; A template module for setting at least one sample pixel for determining a shape of a subject on each image; An analysis module for calculating coordinates of the sample pixel and calculating corrected coordinates of the sample pixel by correcting image distortion according to a photographing angle in the coordinates of the sample pixel; And an output module for outputting the corrected coordinates of the sample pixel. Using the shape simulation system and method according to the present invention, the shape of the suspension bridge can be measured using equipment such as a digital camera, which is less expensive than conventional surveying equipment, and compared with the prior art, which takes a long time to measure. The advantage is that you get accurate results in a short time.

Suspension bridge, surveying, image displacement, lens distortion, CCD, digital camera

Description

System and Method for Shape Simulation of a Suspension Bridge

The present invention relates to a system and method for simulating a shape of a suspension bridge using a digital image processing program. Specifically, a structure of a suspension bridge or the like is divided into a plurality of images using a digital camera, The present invention relates to a system and method that can simulate the shape of a structure by setting the coordinates of the sample pixels and correcting the distortion of the image at the set coordinates, and outputting the sample pixel coordinates that can accurately represent the shape of the structure.

Recently, construction of bridges using cables, such as suspension bridges and cable-stayed bridges, is actively underway. Most of these bridges are designed to have long spans and are greatly affected by the vibrations caused by wind and vehicle loads. In addition, the shape of the entire bridge may be changed in the long term due to the outside temperature, the slope of the pylon, the extension of the cable and the point condition, the movement of the anchor block, and the like. Therefore, it is necessary to constantly grasp these change factors and evaluate the health of the bridge. In particular, in the case of suspension bridges, it is important to determine the shape position in order to evaluate the bridge's safety because the change in the state of the bridge is represented by the change in the sag of the main cable.

Currently, total station and 3D laser method are mainly used for precise displacement measurement of suspension bridges and cable-stayed bridges.However, the equipment is expensive and requires a lot of manpower and cost. There is a problem that requires. Therefore, there is a need for a shape simulation method capable of measuring the shape of a structure in a short time at a reduced cost instead of the prior art which requires expensive precise surveying equipment.

The present invention for solving the above problems of the prior art, by analyzing the image of the suspension bridge photographed using a digital camera to simulate the shape of the suspension bridge, it is possible to measure the shape of the bridge accurately in a short time at a low cost It is an object to provide a shape simulation system and method.

Shape simulation system according to an embodiment of the present invention for achieving the above object, a digital camera for taking an image; A photographing module configured to photograph one or more images at different photographing angles by controlling photographing angles of the digital camera; A template module for setting at least one sample pixel for determining a shape of a subject on each image; An analysis module for calculating coordinates of the sample pixel and calculating corrected coordinates of the sample pixel by correcting image distortion according to a photographing angle in the coordinates of the sample pixel; And an output module for outputting corrected coordinates of the sample pixel.

A shape simulation method according to another aspect of the present invention includes the steps of photographing one or more images at different photographing angles; Setting one or more sample pixels to determine a shape of a subject in each of the photographed images; Calculating coordinates of the set sample pixel; Calculating the corrected coordinates by correcting the image distortion in the coordinates of the sample pixel; And outputting corrected coordinates of the sample pixel.

Using the shape simulation system and method of the suspension bridge according to the present invention, it is possible to measure the shape of the suspension bridge using equipment such as a digital camera which is less expensive than conventional surveying equipment, In comparison, there is an advantage of obtaining accurate results in a short time.

Hereinafter, with reference to the accompanying drawings looks at in detail with respect to the preferred embodiment of the present invention.

1 is a front view showing a suspension bridge as a preferred embodiment of the present invention. As shown, the suspension bridge structure is suspended in the main tower (1) of the forced or reinforced concrete structure, the main cable (2) suspended from the main tower (1), anchor block (3), the main cable (2) to the tension of the main cable It comprises a hanger (4) leading to the reinforcement type (5) hanging on the main cable (2). The present invention is configured to set a sample pixel which can grasp the position of each component in the image of the suspension bridge, and simulate the shape of the suspension bridge through the coordinates of the set sample pixel.

2 is a block diagram showing the configuration of a shape simulation system according to an embodiment of the present invention. As shown, the shape simulation system according to the embodiment includes a digital camera 21, a photographing module 22, a template module 23, an analysis module 24, and an output module 25. The image captured by controlling the photographing angle of the digital camera 21 in the photographing module 22 is transferred to the template module 23. The template module 23 sets up sample pixels for analyzing the shape of the structure on each image. The analysis module 24 calculates the coordinates of the sample pixels and corrects the distortion due to image capturing at the calculated coordinates. The output module 25 outputs the coordinates of each of the corrected sample pixels, and may determine the shape of the photographing target structure through the coordinates of the output sample pixels.

Shooting module

3 is a flowchart showing each step of the shape simulation method according to an embodiment of the present invention. The shape simulation method according to the above embodiment starts with the photographing module 22 controlling the digital camera 21 to photograph the structure (S301). The digital camera 21 includes a charge coupled device (CCD), and converts information about light incident to the digital camera 21 into an electrical signal to generate a digital image. In the generated digital image, each part of the suspension bridge can be represented by coordinates according to the number of pixels of the CCD, and the accuracy of the suspension bridge coordinates is improved as the higher resolution CCD is used.

In the case of simulating the shape of the structure by analyzing the photographed image, it is preferable to simulate the shape of the structure by using one image obtained by photographing the entire structure with high pixels. However, since the number of pixels that can be implemented is limited, it is very difficult to analyze the suspension bridge through a single image photographed using a high resolution Charge Coupled Device (CCD). Therefore, the present invention uses a method of photographing a structure by dividing one structure into several photographed images and analyzing the divided photographed images. The photographing module 22 controls the photographing angle of the digital camera 21 to photograph the photographed by dividing the suspension bridge structure which is the subject into a predetermined number of images. In this specification, the photographing angle means an angle at which the digital camera 21 is rotated from the center of the subject. For example, the photographing angle when photographing the center of the subject is 0 °.

The digital camera 21 divides and photographs a suspension bridge structure, which is a subject, by a predetermined number, and the photographed image file is stored for analysis. In an embodiment of the present invention, the image file may be stored using a bitmap format that can be stored in units of pixels, and various bitmap formats such as BMP, JPG, and GIF, which can maintain vivid image quality, may be used. .

template  module

When the captured image is stored, the template module 23 sets appropriate sample pixels in each image for analysis of the captured image (S302). In the present invention, the sample pixels are preferably set to pixels that make it easy to determine the shape of the object to be photographed. In one embodiment of the present invention with a suspension bridge, the sample pixels are the intersection of the main cable 2 and each hanger 4, the intersection of each hanger 4 and the reinforcement 5, the pylon 1 It can be set to pixels located at the top, the intersection of the anchor block 3 and the main cable 2, or the intersection of the reinforcement 5 truss.

4 is a photograph exemplarily illustrating positions of some pixels among sample pixels set on a captured image of a suspension bridge. Of the pixels 41, 42 indicated by the + symbol in the picture, the pixel 41 represents the intersection of the main cable 2 and the hanger 4, and the pixel 42 is the hanger 4 and the reinforcement 5. Indicates. In general, the larger the number of sample pixels, the better, and may be appropriately determined in consideration of the computational load of the equipment. Once the sample pixels are determined, the captured image is passed to the analysis module.

Analysis module

First, the coordinate setting unit 241 included in the analysis module 24 sets the coordinates of the sample pixels on the entire image in which each image is merged (S304). The coordinate value of the pixel depends on the total number of pixels included in the captured image, and can be generally expressed in the form of (x, y). For example, if the image has 3,072 pixels horizontally and 2,048 pixels vertically, the coordinates of the pixels can be set in the range of (0, 0) to (3072, 2048). In order to set the coordinates, the coordinate setting unit 241 first sets a coordinate system by merging each photographed image into one image.

The coordinate setting unit 241 compares the vertical length of the structure in each image photographed adjacently, for example, the length of the hanger 4 in the case of the suspension bridge structure, so that the pixels overlap each other at the smallest difference in length. To judge. Accordingly, the pixels having the smallest difference in length may be connected to each other to merge adjacent images, and the coordinate setting unit 241 performs the above process on the entire captured image.

In an embodiment, the merging of images may be performed in the order of pasting adjacent images from both sides based on an image photographed at the center of the subject (that is, an image having a photographing angle of 0 °). Since the photograph taken at the center of the subject has the lowest distortion rate, the distortion error is the smallest based on the image at the center. The coordinate origin in the merged image is set to the center pixel of the center image before merging. Once the coordinate origin is set, it is possible to represent the coordinates of each sample pixel in the form (x, y) as described above.

Once the coordinates of the sample pixels are set, image distortion according to the relative photographing position of each image should be corrected (S305 to S307). To correct the distortion of the image, data such as the angle of the subject and the camera and the degree of tilting are required. However, since it is difficult to know all of the values included in the image, it is necessary to simplify the correction process through some assumptions described below.

First, when shooting, the camera should be photographed at the position facing the center of the subject. This is because the difference between left and right correction can be reduced when shooting with a camera located in the center. Second, all the photographs should be taken at the same magnification in order to divide the photographed subject and merge the photographed images. Third, the distortion caused by the height difference at the time of photographing is generally not a problem since the distance to the subject is large because the distance to the subject is small when photographing a structure such as a suspension bridge. Therefore, the error due to the vertical tilt angle will not be considered when correcting the image. Fourth, for suspension bridges, it is assumed that the hanger from cable to reinforced type is vertical. This is an assumption for correcting the rotational conversion of the picture according to the rotation of the camera at the time of photographing, and it is possible to calculate the tilt angle of the up and down coordinates based on the verticality of the hanger.

The analysis module 24 corrects the image distortion based on the above assumptions. In detail, the inclination angle calculation unit 242 calculates the pixel inclination angle, the horizontal correction unit 243 corrects the horizontal direction (that is, the x coordinate) distortion by using the pixel inclination angle, and the vertical correction unit 244 in the vertical direction Correct the distortion (i.e. y coordinate). Each error correction process is applied to the coordinates of the sample pixel set on the image, and the coordinates of the sample pixel change according to the correction.

First, the step of calculating the pixel tilt angle in the tilt angle calculator 242 will be described (S305). The pixel tilt angle may be obtained using the vertical distance L from the photographing base point and the distance S between two preset points in the suspension bridge structure that is the subject. The distance from the photographing base point to the subject means the closest distance from the point where the digital camera 21 is located to the subject to be photographed, and can be obtained by actual surveying. In one embodiment of the present invention, the distance from the photographing point to the subject may be obtained using aerial photography.

The two preset points in the subject are points used to calculate a predetermined distance for defining the pixel tilt angle on the subject. In one embodiment, the distance between two preset points in the subject may be the length between one segment of the reinforcement 5 of the suspension bridge structure, which is the subject. In general, the length between one segment of the reinforcement 5 in the suspension bridge structure is constant and precisely constructed, for example it may consist of 9.175m or 9.425m.

, 픽셀 경사각

, L 및 S를 도시한다. 이때, 픽셀 경사각

는 삼각함수를 사용하여 다음 수학식 1에 의하여 계산될 수 있다.FIG. 5A shows the distance L from the photographing base point to the subject and the length between one segment of the reinforcement type 5 in the suspension bridge structure which is the subject, and FIG. 5B shows the photographing angle

, Pixel tilt angle

, L and S are shown. At this time, the pixel tilt angle

May be calculated by Equation 1 using a trigonometric function.

When the pixel tilt angle is obtained, the horizontal correction unit 243 performs a horizontal correction process of the sample pixel coordinates using the pixel tilt angle (S306). Horizontal correction is a correction in which the photographic projection surface photographed at an angle tilted to the side according to the photographing angle matches the surface of the actual subject. Next, this correction method will be described with reference to FIGS. 6A and 6B. 6A and 6B are enlarged views of intersection points between the photographic projection surface and the actual surface illustrated in FIG. 5B. 6A is an enlarged view of the left side of the subject and FIG. 6B is an enlarged view of the right side of the subject, centering on the pixel 60 where the photographic projection surface and the actual plane intersect. The intersection point 60 at which the photographic projection surface and the actual surface intersect corresponds to the right center pixel of the photographed image. First, a process of correcting an image photographing the left side will be described with reference to FIG. 6A.

와 오차

를 사용하여 다음 수학식 2에 의하여 구할 수 있다.In FIG. 6A the position of the sample pixel before correction is indicated by pixel 61. The horizontal corrector 243 calculates the distance from the intersection point 60 to the sample pixel 61 to correct distortion with respect to the distance. The horizontal corrector 243 projects the pixel 61 to the actual surface by using a trigonometric function and moves the pixel 61 to the pixel 62. The horizontal corrector 243 corrects an error generated due to the straightness of the light during the projection process, and moves to the pixel 63. Let's do it. If the distance between the sample pixel 63 and the intersection point 60 is S 'in actual view, S' is the photographing angle.

And error

It can be obtained by using the following equation (2).

의 값은 보정 대상인 촬영 이미지가 교차점(60)을 기준으로 피사체의 어느 측면을 촬영하였는지에 따라 달라지며, 실제면과 사진 투영면 사이의 거리인 h를 사용하여 구할 수 있다. h의 값은 다음 수학식 3에 의하여 계산된다.Used in Equation 2

The value of depends on which side of the subject the photographed image to be corrected is photographed based on the intersection point 60, and can be obtained by using h, which is the distance between the actual surface and the photographic projection surface. The value of h is calculated by the following equation.

의 값은 삼각 함수 공식을 사용하여 하기 수학식 4 및 수학식 5에 의하여 계산된다.Once the value of h is calculated,

The value of is calculated by the following equations (4) and (5) using a trigonometric formula.

의 크기가 계산되면, 상기 수학식 2에 계산된

의 값을 대입하여 다음 수학식을 얻을 수 있다.

When the size of is calculated, calculated in Equation 2

By substituting for, we get

When S 'is calculated by Equation 6, the x-coordinate of the sample pixel 63 is corrected such that the distance from the intersection point 60 to the sample pixel 63 is S'. According to Equations 2 to 6, horizontal correction of an image of the left side of the subject is performed based on the intersection point 60.

Next, a process of correcting an image of photographing the right side of the subject based on the intersection point 60 will be described with reference to FIG. 6B. The position of the sample pixel before correction on the right side of the subject is shown by the pixel 64, and the pixel 64 is moved to the pixel 65 as the photographic projection surface is projected onto the real surface. When the error due to the straightness of light is corrected again, the final pixel position is the pixel 66. As in the case of the left side described above, this is expressed by the following equation.

만큼 빼줘야 하는 것을 알 수 있다. 상기 수학식 7에서

의 값은, 전술한 좌측면의 경우와 마찬가지로 길이 h를 사용하여 다음 수학식 8에 의하여 구할 수 있다.In Equation 7, unlike Equation 2 showing the case of the left side, the length S is projected onto the real surface in the calculation of S '.

As far as I can see. In Equation 7

The value of can be obtained by the following equation (8) using the length h as in the case of the left side described above.

를 상기 수학식 7에 대입하여 보정된 길이를 구하면 다음과 같다.Obtained for the image on the right side

Substituting into Equation 7 to obtain the corrected length is as follows.

Therefore, the x-coordinate of the sample pixel 64 is corrected so that the distance from the sample pixel 64 to the intersection point 60 is S '. The horizontal distortion is corrected in the captured image by the above-described process.

Next, the vertical correcting unit 244 corrects the vertical distortion (S308). First, the vertical corrector 244 proportionally increases the vertical length from the origin to the sample pixel by using the above-described horizontal distortion correction process. Referring to FIG. 7, the length S in the photographic projection surface 71 is increased to the length S 'in the actual surface 72 by the horizontal correction. Pixel A shown in FIG. 7 moves to pixel A 'accordingly. In this case, if the y coordinate of the pixel A is A and the y coordinate of the pixel A 'is A', the y coordinate should increase at the same rate as the x coordinate is increased by the horizontal correction. A 'can be calculated by Equation 10.

를 사용하여 픽셀(A')을 픽셀(A'')으로 보정한다. After the position of the sample pixel is moved to the pixel A ', a process of correcting the difference in focal length due to the characteristics of the lens of the digital camera 21 is additionally required. In the vertical correction unit 244, the distortion of the lens of the digital camera 21 is

To correct the pixel A 'to the pixel A''.

Distortion aberration is a phenomenon in which light through a lens collects at one point, but the magnification varies depending on the size of the image and the image is distorted. The distortion aberration is a characteristic of the lens itself that varies depending on the shooting distance, magnification, and the shooting angle, and the like, and in one embodiment of the present invention, the distortion aberration changes the distance between the subject and the digital camera 21 to repeatedly capture data. Can be obtained through For example, by using the digital camera 21, a square grid paper is repeatedly photographed with different distances and photographing angles, and a value obtained by correcting the coordinates of the vertical direction in the photographed grid paper by the above equation (10). You can get the value of the distortion aberration by experimenting with the graph paper measuring the difference between the length of the grid paper and the known grid paper.

를사용하여 다음의 수학식 11에 의하여 계산된다.H, the difference between the y coordinates of pixel A '' and pixel A ', is a distortion aberration

It is calculated by using the following equation (11).

When the value of h is calculated, by adding h to the y-coordinate of the pixel A 'calculated through the proportional expression, the coordinates of the sample pixel that has been vertically corrected can be obtained. The y coordinate A ″ of the sample pixel A ″ is finally calculated by the following equation (12).

Output module

When the process of correcting the distortion of the image is completed by the above-described equations in the analysis module 24, the output module 25 merges and outputs coordinates of the corrected sample pixels (S308). In one embodiment, the output output from the output module 25 may be output in the form of a text file covering all the coordinates of the pixels of each image. In addition, the output module 25 shows the coordinates of the output pixels in a form that can be easily recognized, so that the structure of the suspension bridge as the subject can be easily grasped.

By analyzing the shape of the suspension bridge structure using the shape simulation system and method according to the present invention by the above-described process, it is possible to accurately measure the shape of the suspension bridge using a digital camera which is less expensive than conventional surveying equipment. In addition, compared with the prior art, which takes a long time to measure the shape of the suspension bridge structure, using the shape simulation system and method according to the present invention has the advantage that it is possible to obtain accurate results in a short time.

While specific embodiments of the present invention have been illustrated and described, the technical spirit of the present invention is not limited to the accompanying drawings and the above description, and various modifications can be made without departing from the spirit of the present invention. It will be apparent to those skilled in the art, and variations of this form will be regarded as belonging to the claims of the present invention without departing from the spirit of the present invention.

1 is a side view showing the shape of a typical suspension bridge.

2 is a block diagram showing the configuration of a shape simulation system according to an embodiment of the present invention.

3 is a flowchart showing each step of the shape simulation method according to an embodiment of the present invention.

4 is a photograph showing an example of positions of sample pixels set on an image of a suspension bridge.

5A is a schematic diagram illustrating a process of obtaining a pixel tilt angle in a shape simulation system according to an embodiment of the present invention.

5B is another schematic diagram illustrating a process of obtaining a pixel tilt angle in a shape simulation system according to an exemplary embodiment of the present invention.

6A is a schematic diagram illustrating a process of correcting horizontal distortion of an image photographing a left side of a subject in a shape simulation system according to an exemplary embodiment of the present invention.

6B is a schematic diagram illustrating a process of correcting horizontal distortion of an image photographing a right side of a subject in a shape simulation system according to an exemplary embodiment of the present invention.

7 is a schematic diagram showing a process of correcting vertical distortion in a shape simulation system according to an embodiment of the present invention.

Claims (9)

A digital camera for taking an image; A photographing module configured to photograph one or more images at different photographing angles by controlling photographing angles of the digital camera; A template module for setting at least one sample pixel for determining a shape of a subject on each image; An analysis module for calculating coordinates of the sample pixel and calculating corrected coordinates of the sample pixel by correcting image distortion according to a photographing angle in the coordinates of the sample pixel; And And an output module for outputting corrected coordinates of the sample pixel. The method of claim 1, The analysis module, A coordinate setting unit configured to calculate coordinates of the sample pixels on the merged image of each image; An inclination angle calculator configured to calculate a pixel inclination angle by using a photographing angle of each image, a distance from the digital camera to a subject, and a distance between two preset points in the subject; A horizontal corrector configured to correct horizontal coordinates of the sample pixel such that the plane projected on the image coincides with the actual plane of the subject by using the photographing angle and the pixel tilt angle; And And a vertical correcting unit configured to correct the vertical coordinates of the sample pixels by using the coordinates calculated by the horizontal correcting unit and the distortion of the digital camera. The method of claim 2, The coordinate setting unit, The merged image is generated by merging the images in the order of the smallest shooting angles to the central image having the shooting angle of 0 degrees among the respective images, and calculating the coordinates of the sample pixels from the center of the center image as the origin. A shape simulation system characterized by the above-mentioned. The method of claim 2, The inclination angle calculator, The shooting angle of each image

L is the distance from the digital camera to the subject, S is the distance between the two preset points S, the pixel tilt angle

If you say,

The shape simulation system, characterized in that for calculating the pixel tilt angle according to the equation. Taking one or more images at different shooting angles using a digital camera; Setting one or more sample pixels to determine a shape of a subject in each of the photographed images; Calculating coordinates of the set sample pixel; Calculating the corrected coordinates by correcting the image distortion in the coordinates of the sample pixel; And And outputting the corrected coordinates of the sample pixel. delete The method of claim 5, Calculating the coordinates of the sample pixel, Generating a merged image by merging the respective images in an order of decreasing shooting angle to a central image having a shooting angle of 0 degrees among the respective images; And And setting coordinates of the sample pixel on the merged image, using the center point of the center image as the origin. The method of claim 5, Computing the corrected coordinates, Calculating a pixel tilt angle using a photographing angle of each image, a distance from a photographing point to a subject, and a distance between two preset points in the subject; Correcting horizontal coordinates of the sample pixel using the photographing angle and the pixel tilt angle so that the plane projected on the image coincides with the actual plane of the subject; And And correcting the vertical coordinates of the sample pixels using the corrected horizontal coordinates and the distortion aberration of the digital camera. The method of claim 8, Calculating the pixel tilt angle, The shooting angle of each image

L is the distance from the digital camera to the subject, S is the distance between the two preset points S, the pixel tilt angle

If you say,

And calculating the pixel inclination angle according to the equation.

Images