Arch bridge morphology detection method and system based on multistage edge double-back projection conical surface

Abstract

The invention discloses an arch bridge morphology detection method and system based on a multi-stage edge double-back projection conical surface, and aims to solve the problem of arch bridge morphology detection based on the multi-stage edge double-back projection conical surface. The arch bridge morphology detection method based on the multi-stage edge double-back projection conical surface mainly comprises edge curve extraction of the arch bridge morphology detection method based on the multi-stage edge double-back projection conical surface and arch bridge morphology double-back projection conical surface calculation of the arch bridge morphology detection method based on the multi-stage edge double-back projection conical surface. Detecting the shape of the arch bridge based on the multi-stage edge double-back projection conical surface; and the like. The arch bridge morphology detection system based on the multi-stage edge double back projection conical surface is mainly composed of an industrial camera (1), a tripod (2) and the like. The arch bridge morphology detection method and system based on the multi-stage edge double back projection conical surface can be used for large-scale arch bridge measurement, and are convenient to solve and stable in performance.

Description

Technical Field

[0001] The invention relates to a method and system for non-contact detection of a bridge, and more specifically, to a method and system for detecting the shape of an arch bridge based on multi-level edge double back-projection cone surfaces. Background Art

[0002] An arch bridge is a bridge that uses an arch structure as the main load-bearing structure of the bridge body. In order to ensure the smooth flow of traffic and the safety of people's lives and property, its structure and shape should meet the design requirements and safety standards. By regularly performing topographic inspections on arch bridges, deformation, cracks, damage and other problems of arch bridges can be discovered and repaired in a timely manner to prevent them from further deteriorating and threatening the safety of the bridge. As an important transportation building, the stability of the arch bridge's shape is of great significance to the safety of vehicles and pedestrians. In addition, the topographic inspection of arch bridges can also help improve the life and sustainability of bridges and reduce the cost of maintenance and replacement. Therefore, in-depth research on the theory and methods of arch bridge topographic visual inspection technology has important engineering application value and social significance. Summary of the invention

[0003] The present invention aims to solve the problems in the detection process of arch bridge shape, such as the complexity of the existing technical devices, the cumbersome long-distance detection process, and the infinite number of solutions of a single projection view of the arch bridge. A method and system for detecting the shape of an arch bridge based on a multi-level edge double-reverse projection cone is proposed to realize non-contact visual detection of the shape of the arch bridge. The method is mainly composed of an industrial camera and a triangulation frame. The edge of the arch bridge is extracted by applying the intersection line of the multi-level edge double-reverse projection cone, and then the non-contact detection of the shape of the arch bridge is realized through the double-reverse projection cone.

[0004] In conjunction with the accompanying drawings of the specification, the present invention is implemented using the following technical solutions:

[0005] The specific steps of the arch bridge shape detection method based on multi-level edge double back-projection cone surface are as follows:

[0006] Step 1: Edge curve extraction based on the arch bridge shape detection method of multi-level edge double back-projection cone surface:

[0007] The industrial camera collects an image containing a complete arch bridge and performs binarization processing on it. The edge points in the image are detected using a multi-level edge detection operator. The curve formed by connecting the edge points of the arch bridge curve extracted by the multi-level edge detection operator is used as the initial value curve of the arch bridge Q(k); the functional of the arch bridge curve is

[0008]

[0009] In the formula and are the internal and external energies of the arch bridge edge Q(k),

[0010]

[0011] Where ρ(k) and b(k) are coefficients, Q′(k) and Q″(k) are the first and second order partial derivatives of Q(k) with respect to k respectively; the external energy of the arch bridge curve determined by the grayscale G(i, j) of the image at (i, j) is for

[0012]

[0013] In the formula, G i , G j are the gradients of the arch bridge image in the horizontal and vertical directions, G ii , G ij , G jj is the second-order gradient of the arch bridge image in the horizontal-horizontal, horizontal-vertical, and vertical-vertical directions, and π1, π2, and π3 are the distribution parameters;

[0014] According to the fact that the partial derivative is 0 when the energy of the arch bridge curve is minimum, the condition for the energy functional of the arch bridge edge curve to take the minimum value is:

[0015]

[0016] Assuming the coefficients of the above formula are constants ρ and b, the necessary discretization condition formula corresponding to the above formula is

[0017]

[0018] The coefficients on the right side of the above formula can be integrated into the matrix H, so the necessary condition formula for discretization is

[0019]

[0020] The arch bridge curve Q(k) is expressed as a function of the number of iterations n, Q(k, n). According to Newton's second law,

[0021]

[0022] Where M is the point mass of the arch bridge edge curve Q(k), μ, is the damping and damping force of the arch bridge edge curve Q(k), L N (Q(k,n)),L W (Q(k, n)) are the internal and external forces on the arch bridge edge curve Q(k), respectively. Let M = 0, then the arch bridge edge satisfies the above equation

[0023]

[0024] The force on the edge point (i, j) of the arch bridge contour is orthogonally decomposed into the force L parallel to the horizontal Oi direction of the image coordinate system Oi The force L in the direction perpendicular to Oj Oj Combining the discretization necessary condition formula and the above formula, the isotropic dynamic equation of the point on the edge curve of the arch bridge can be obtained:

[0025]

[0026] When Δn=1, we can get

[0027] i(n+1)=(H+μI) -1 [μi(n)+L Oi (i(n), j(n))]

[0028] j(n+1)=(H+μl) -1 [μj(n)+L Oj (i(n), j(n))]

[0029] The points on the arch bridge curve are calculated by the above formula, and the curve equation Q*(k) of the arch bridge is determined by further fitting;

[0030] Step 2: Arch bridge shape double back-projection cone surface solution based on the arch bridge shape detection method of multi-level edge double back-projection cone surface:

[0031] According to the arch bridge edge curve equation Q*(k) obtained in the first step, combined with the pre-calibrated intrinsic parameter v of the industrial camera and the following formula, the back-projection cone with the zero vector as the center of the industrial camera can be obtained:

[0032]

[0033] The mobile industrial camera obtains another projection image of the arch bridge within the field of view, and at the same time obtains another arch bridge back-projection cone with the zero vector as the center of the industrial camera.

[0034]

[0035] Step 3: Arch bridge shape detection based on the arch bridge shape detection method of multi-level edge double back-projection cone surface:

[0036] The homography transformation matrix D before and after the industrial camera moves is solved according to the multi-scale feature point matching algorithm. The edge points of the arch bridge are located on the back-projection cone surface of the arch bridge.

[0037]

[0038]

[0039] Solve the arch bridge edge point X that satisfies the above two equations at the same time, further fit the intersection line of the two back-projection cones, and obtain the three-dimensional detection result of the arch bridge in the industrial camera coordinate system, that is, complete the arch bridge shape detection based on the multi-level edge double back-projection cone surface arch bridge shape detection method.

[0040] The arch bridge shape detection system based on the arch bridge shape detection method of multi-level edge double back-projection cone surface includes an industrial camera and a tripod;

[0041] The bottom of the industrial camera is fixedly connected to the stud thread on the top of the tripod.

[0042] The beneficial effects of the present invention are:

[0043] 1. The present invention adopts a multi-level curve edge extraction method to extract the edge of the arch bridge curve, and obtains a continuous and smooth edge curve by optimizing the energy functional of the initial contour curve, thereby avoiding problems such as a long energy functional convergence period when the initial contour is far from the true contour position, and further improving the efficiency of arch bridge detection.

[0044] 2. The present invention proposes a method for solving the edge curve of an arch bridge using a double back-projection cone surface, which avoids the problem of wrong solution caused by the existence of infinite three-dimensional arch bridge curves that meet the projection relationship based on a single two-dimensional image of an arch bridge. By adding the two-dimensional image projection information of the arch bridge to obtain accurate three-dimensional information of the arch bridge curve, it is beneficial to achieve high-reliability detection of the arch bridge curve and improve the accuracy of arch bridge shape detection.

[0045] 3. The detection system designed by the present invention has a simple structure, which solves the problems of the existing measurement methods being cumbersome, complex in structure, and requiring a lot of manpower and material resources. The edge curve of the arch bridge can be solved through the two-dimensional image of the arch bridge, further realizing the contactless detection of the arch bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the arch bridge shape detection method based on multi-level edge double back-projection cone surface;

[0047] Figure 2 It is an axonometric diagram of the arch bridge shape detection system based on multi-level edge double back-projection cone surface;

[0048] Figure 3 It is an axonometric view of industrial camera 1 in the arch bridge shape detection system based on multi-level edge double back-projection cone surface;

[0049] Figure 4 It is an axonometric diagram of tripod 2 in the arch bridge shape detection system based on multi-level edge double back-projection cone surface;

[0050] Figure 5This is the result of multi-level edge detection of the first arch bridge based on the arch bridge shape detection method of multi-level edge double back-projection cone surface;

[0051] Figure 6 This is a multi-level edge detection result diagram of the second arch bridge based on the arch bridge shape detection method using multi-level edge double back-projection cone surfaces;

[0052] In the picture: 1. Industrial camera, 2. Tripod. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0054] See also Figure 1 The arch bridge shape detection method based on multi-level edge double back-projection cone surface provided by the present invention can be divided into the following three steps:

[0055] Step 1: Edge curve extraction based on the arch bridge shape detection method of multi-level edge double back-projection cone surface:

[0056] Industrial camera 1 collects an image containing a complete arch bridge and performs binarization processing on it. A multi-level edge detection operator is used to detect edge points in the image. The curve formed by connecting the edge points of the arch bridge curve extracted by the multi-level edge detection operator is used as the initial value curve Q(k) of the arch bridge. The functional of the arch bridge curve is

[0057]

[0058] In the formula and are the internal and external energies of the arch bridge edge Q(k),

[0059]

[0060] Where ρ(k) and b(k) are coefficients, Q′(k) and Q″(k) are the first and second order partial derivatives of Q(k) with respect to k respectively; the external energy of the arch bridge curve determined by the grayscale G(i, j) of the image at (i, j) is for

[0061]

[0062] In the formula, G i , G j are the gradients of the arch bridge image in the horizontal and vertical directions, G ii , G ij , G jj is the second-order gradient of the arch bridge image in the horizontal-horizontal, horizontal-vertical, and vertical-vertical directions, and π1, π2, and π3 are the distribution parameters;

[0063] According to the fact that the partial derivative is 0 when the energy of the arch bridge curve is minimum, the condition for the energy functional of the arch bridge edge curve to take the minimum value is:

[0064]

[0065] Assuming the coefficients of the above formula are constants ρ and b, the necessary discretization condition formula corresponding to the above formula is

[0066]

[0067] The coefficients on the right side of the above formula can be integrated into the matrix H, so the necessary condition formula for discretization is

[0068]

[0069] The arch bridge curve Q(k) is expressed as a function of the number of iterations n, Q(k, n). According to Newton's second law,

[0070]

[0071] Where M is the point mass of the arch bridge edge curve Q(k), μ, is the damping and damping force of the arch bridge edge curve Q(k), L N (Q(k,n)),L W (Q(k, n)) are the internal and external forces on the arch bridge edge curve Q(k), respectively. Let M = 0, then the arch bridge edge satisfies the above equation

[0072]

[0073] The force on the edge point (i, j) of the arch bridge contour is orthogonally decomposed into the force L parallel to the horizontal Oi direction of the image coordinate system Oi The force L in the direction perpendicular to Oj Oj Combining the discretization necessary condition formula and the above formula, the isotropic dynamic equation of the point on the edge curve of the arch bridge can be obtained:

[0074]

[0075]

[0076] When Δn=1, we can get

[0077] i(n+1)=(H+μI) -1 [μi(n)+L Oi (i(n), j(n))]

[0078] j(n+1)=(H+μI) -1 [μj(n)+L Oj (i(n), j(n))]

[0079] The points on the arch bridge curve are calculated by the above formula, and the curve equation Q*(k) of the arch bridge is determined by further fitting;

[0080] Step 2: Arch bridge shape double back-projection cone surface solution based on the arch bridge shape detection method of multi-level edge double back-projection cone surface:

[0081] According to the arch bridge edge curve equation Q*(k) obtained in the first step, combined with the pre-calibrated intrinsic parameter V of industrial camera 1 and the following formula, the back-projection cone with the zero vector as the center of industrial camera 1 can be obtained.

[0082]

[0083] The mobile industrial camera 1 obtains another projection image of the arch bridge within the field of view, and at the same time obtains another arch bridge back-projection cone surface with the zero vector being the center of the industrial camera 1.

[0084]

[0085] Step 3: Arch bridge shape detection based on the arch bridge shape detection method of multi-level edge double back-projection cone surface:

[0086] The homography transformation matrix D before and after the industrial camera 1 moves is solved according to the multi-scale feature point matching algorithm. The edge points of the arch bridge are located on the back-projection cone surface of the arch bridge.

[0087]

[0088]

[0089] Solve the arch bridge edge point X that satisfies the above two equations at the same time, further fit the intersection line of the two back-projection cones, and obtain the three-dimensional detection result of the arch bridge in the coordinate system of industrial camera 1, that is, complete the arch bridge morphology detection based on the multi-level edge double back-projection cone surface arch bridge morphology detection method.

[0090] See also Figures 2 to 4 The arch bridge shape detection system based on the arch bridge shape detection method of multi-level edge double back-projection cone surface includes an industrial camera 1 and a tripod 2;

[0091] The tripod 2 is placed on a horizontal ground, and the bottom of the industrial camera 1 is fixedly connected to the stud on the top of the tripod 2 by thread.

Claims

1. A method for detecting the shape of an arch bridge based on a multi-level edge double back-projection cone surface, characterized in that: The specific steps of the arch bridge shape detection method based on multi-level edge double back-projection cone surface are as follows: Step 1: Edge curve extraction based on the arch bridge shape detection method of multi-level edge double back-projection cone surface: The industrial camera (1) collects an image containing a complete arch bridge and performs binarization processing on it. The edge points in the image are detected using a multi-level edge detection operator. The curve formed by connecting the edge points of the arch bridge curve extracted by the multi-level edge detection operator is used as the initial value curve Q(k) of the arch bridge. The functional of the arch bridge curve is: In the formula and are the internal and external energies of the arch bridge edge Q(k), Where ρ(k) and b(k) are coefficients, Q′(k) and Q″(k) are the first and second order partial derivatives of Q(k) with respect to k respectively; the external energy of the arch bridge curve determined by the grayscale G(i, j) of the image at (i, j) is for In the formula, G i , G j are the gradients of the arch bridge image in the horizontal and vertical directions, G ii , G ij , G jj is the second-order gradient of the arch bridge image in the horizontal-horizontal, horizontal-vertical, and vertical-vertical directions, and π1, π2, and π3 are the distribution parameters; According to the fact that the partial derivative is 0 when the energy of the arch bridge curve is minimum, the condition for the energy functional of the arch bridge edge curve to take the minimum value is: Assuming the coefficients of the above formula are constants ρ and b, the necessary discretization condition formula corresponding to the above formula is The coefficients on the right side of the above formula can be integrated into the matrix H, so the necessary condition formula for discretization is The arch bridge curve Q(k) is expressed as a function of the number of iterations n, Q(k, n). According to Newton's second law, Where M is the point mass of the arch bridge edge curve Q(k), μ, is the damping and damping force of the arch bridge edge curve Q(k), L N (Q(k,n)),L W (Q(k, n)) are the internal and external forces on the arch bridge edge curve Q(k), respectively. Let M = 0, then the arch bridge edge satisfies the above equation The force on the edge point (i, j) of the arch bridge contour is orthogonally decomposed into the force L parallel to the horizontal Oi direction of the image coordinate system Oi The force L in the direction perpendicular to Oj Oj Combining the discretization necessary condition formula and the above formula, the isotropic dynamic equation of the point on the edge curve of the arch bridge can be obtained: When Δn=1, we can get i(n+1)=(H+μI) -1 [μi(n)+L Oi (i(n),j(n))] j(n+1)=(H+μI) -1 [μj(n)+L Oj (i(n),j(n))] The points on the arch bridge curve are calculated by the above formula, and the curve equation Q*(k) of the arch bridge is determined by further fitting; Step 2: Arch bridge shape double back-projection cone surface solution based on the arch bridge shape detection method of multi-level edge double back-projection cone surface: According to the arch bridge edge curve equation Q*(k) obtained in the first step, combined with the pre-calibrated internal parameter V of the industrial camera (1) and the following formula, the back-projection cone with the zero vector as the center of the industrial camera (1) can be obtained: The mobile industrial camera (1) obtains another projection image of the arch bridge within the field of view, and at the same time obtains another arch bridge back-projection cone surface with a zero vector as the center of the industrial camera (1). Step 3: Arch bridge shape detection based on the arch bridge shape detection method of multi-level edge double back-projection cone surface: The homography transformation matrix D of the industrial camera (1) before and after movement is solved according to the multi-scale feature point matching algorithm. The edge points of the arch bridge are located on the back-projection cone surface of the arch bridge. Solve the arch bridge edge point X that satisfies the above two equations at the same time, further fit the intersection line of the two back-projection cone surfaces, and obtain the three-dimensional detection result of the arch bridge in the coordinate system of the industrial camera (1), that is, complete the arch bridge shape detection based on the multi-level edge double back-projection cone surface arch bridge shape detection method.

2. The detection system of the arch bridge shape detection method based on multi-level edge double back-projection cone surface according to claim 1 is characterized in that: The detection system of the arch bridge shape detection method based on multi-level edge double back-projection cone surface comprises an industrial camera (1) and a tripod (2); The tripod (2) is placed on a horizontal ground, and the bottom of the industrial camera (1) is fixedly connected to the stud thread on the top of the tripod (2).

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