Bridge crack detection method and system

Abstract

The invention discloses a bridge crack detection method and system, and relates to the technical field of bridge detection, high-precision three-dimensional point cloud data P is generated through laser radar scanning, the three-dimensional point cloud data P is registered, and a three-dimensional model C of a crack is established, so that the spatial precision of crack evaluation is further improved; the method can accurately evaluate the influence of a crack on the stability of a bridge structure, calculates a comprehensive evaluation index ZRZ, compares the comprehensive evaluation index ZRZ with a preset servicing evaluation threshold Tth, helps to determine whether repair is needed or not and how to repair, solves the problems of insufficient precision and information splitting of a traditional single detection means through the whole-process detection method, and improves the detection efficiency. The crack evaluation result is more comprehensive and reliable, more powerful technical support is provided for bridge safety, potential crack risks can be found in advance, the service life of the bridge can be prolonged through scientific evaluation and repair schemes, and it is ensured that the bridge safety is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge detection, and in particular to a bridge crack detection method and system. Background Art

[0002] With the continuous development and aging of global infrastructure construction, bridges, as key transportation facilities, are facing increasingly serious structural problems. Bridge health monitoring has become an important research direction in the engineering field. Bridge cracks are one of the common causes of structural failure, and their occurrence and expansion directly affect the service life and safety of bridges. Therefore, the detection and evaluation of bridge cracks has become a core task in bridge maintenance and safety management.

[0003] In the invention of Chinese patent application No. CN202010174163.7, a bridge crack detection method is disclosed, which belongs to the field of bridge detection technology and aims to improve the detection accuracy and efficiency of bridge crack detection. The method comprises the following steps: crack segmentation of a set of collected bridge images; based on the crack segmentation results, detecting and classifying bridge cracks using a pre-built bridge crack classification model. The segmentation process of bridge cracks adopts an improved GAC algorithm model, which can segment visible cracks in the image of the bottom of the bridge captured by the high-definition camera of the drone; the construction of the bridge crack classification model adopts a deep learning method, and a deep convolutional neural network model is designed for bridge identification; the three-dimensional reconstruction of bridge cracks and crack information detection adopts a moving cube algorithm to determine the number of cracks, average width, geometric properties and their spatial relationship with the whole, so that professionals can perform qualitative or quantitative analysis of the cracks. The present invention realizes the use of computer detection technology based on deep learning to solve corresponding crack detection and other construction problems.

[0004] It can be seen that image detection technology has been widely used in the preliminary detection of cracks on bridge surfaces due to its convenient operation and low cost. However, relying solely on image processing technology also has significant limitations. Image detection can usually only provide surface morphological information of cracks, and cannot effectively evaluate the depth and extension of cracks and the damage inside the bridge. In addition, image detection technology is easily affected by lighting conditions, weather factors and image resolution, resulting in unstable detection results. Especially in complex environments, noise and fuzzy factors in the image may make the crack detection results inaccurate. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a bridge crack detection method and system, which solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A bridge crack detection method comprises the following steps:

[0007] S1, using a camera to shoot the bridge surface, preprocessing the acquired bridge surface image, and then detecting and extracting the processed bridge surface image to obtain a crack position feature vector V;

[0008] S2. Scanning the crack area of ​​the bridge using a laser radar device according to the obtained crack position feature vector V, obtaining the three-dimensional coordinates of the bridge surface, and generating three-dimensional point cloud data P. Performing a preliminary analysis based on the three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, and obtaining a determination result R;

[0009] S3, triggering a crack verification mechanism according to the judgment result R, including registering the three-dimensional point cloud data P and the judgment result R, and then establishing a three-dimensional model C of the crack;

[0010] S4. Perform risk assessment of cracks on bridge results based on the established three-dimensional crack model C, and obtain a comprehensive assessment index ZR of the impact of cracks on bridge stability;

[0011] S5. Compare the obtained comprehensive evaluation index ZR with the preset maintenance evaluation threshold Tthe to generate a maintenance repair plan and a detection plan for bridge cracks.

[0012] Preferably, said S1 includes S11, S12 and S13;

[0013] S11. Use a high-definition camera and an infrared camera to capture images of the bridge surface to obtain a bridge surface image set Iraw. Then perform image preprocessing operations on the bridge surface image set Iraw, including performing denoising preprocessing using a Gaussian filter to obtain a denoised image set Ipre after the denoising preprocessing. Perform image enhancement preprocessing using histogram equalization to improve image contrast to obtain an enhanced image set Ib after the image enhancement preprocessing.

[0014] Preferably, S12, performing crack detection on the enhanced image set Ib by using an edge detection algorithm to obtain a crack edge region Iedges;

[0015] The crack edge region Iedges is specifically obtained through a gradient calculation step, a non-maximum suppression step and an edge connection step;

[0016] The gradient calculation step calculates the gradient of each pixel in the image, including using the Sobel operator to calculate the gradient magnitude and direction of the image;

[0017] The non-maximum suppression step is used to refine the edge. Each pixel is detected along the gradient direction to see if it is a local maximum. If the pixel is not a local maximum, it is set to 0, otherwise the gradient value of the pixel is retained.

[0018] The edge connection step uses a dual threshold method to connect edges and distinguish whether the edge is a strong edge or a weak edge. Specifically, the distinction is made by setting a distinction threshold T1 and a distinction threshold T2, and the distinction threshold T1 is less than the distinction threshold T2.

[0019] When the gradient amplitude is greater than the distinction threshold T2, the pixel value is determined to be a strong edge;

[0020] When the gradient amplitude is less than the distinction threshold T1, the pixel value is determined to be non-edge;

[0021] When the distinction threshold T1 < gradient amplitude < distinction threshold T2, the pixel value is determined to be a weak edge. If it is connected to a strong edge, it is determined to be a valid edge.

[0022] S13, extracting features from the crack edge region Iedges, obtaining the spatial position features of the crack, and constructing a feature vector V, including the center coordinates (x, y), length Lmax, and width Wmax of the crack;

[0023] Among them, the center coordinates (x, y) are obtained by calculating the geometric center of the crack area, and x and y represent the horizontal and vertical coordinate values ​​of the center point respectively; the length Lmax is obtained by calculating the maximum extension length of the crack; and the width Wmax is obtained by calculating the regional histogram of the crack.

[0024] Preferably, S2 includes S21 and S22;

[0025] S21. Generate a laser radar scanning area Rscan according to the length Lmax and width Wmax information of the center coordinates (x, y) in the crack position feature vector V, and then use the laser radar device to scan the crack area of ​​the bridge to obtain the three-dimensional coordinates (x, y, z) of the bridge surface, where x, y and z represent the horizontal axis, vertical axis and longitudinal axis coordinate values ​​of the three-dimensional coordinates, respectively, and integrate several three-dimensional coordinates (x, y, z) to form a three-dimensional point cloud data P;

[0026] The laser radar scanning area Rscan is obtained by the following calculation formula:

[0027]

[0028] The three-dimensional point cloud data P is specifically P={(x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn)}.

[0029] Preferably, S22, denoising the generated three-dimensional point cloud data P, including using a statistical outlier removal method and a voxel grid filtering method to perform denoising, and then performing a preliminary analysis on the denoised three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, including using a plane fitting method to identify structural faults in the crack area, and obtaining a determination result R;

[0030] The judgment result R is obtained by the following analysis formula:

[0031]

[0032] When the judgment result R=1, it indicates that there are structural plane faults and crack features;

[0033] When the judgment result R = 0, it means that there are no structural plane faults and cracks;

[0034] Where FE(P) represents the fitting error index, Fthe represents the preset deviation evaluation threshold between the cloud data and the fitting surface; Ki represents the local curvature of the i-th cloud point of the three-dimensional point cloud data P, and Kthe represents the preset cloud point curvature evaluation threshold;

[0035] Among them, the fitting error index FE(P) fits the crack area in the three-dimensional point cloud data P by the least square method to obtain the plane, and the plane fitting is performed specifically by the following calculation formula:

[0036] ax+by+cz+d=0;

[0037] Where a, b and c represent the normal vector components of the horizontal axis x, vertical axis y and longitudinal axis z in the plane respectively, and d represents the distance between the plane and the origin;

[0038] Then, the fitting error index FE(P) is obtained by using the square error calculation for the fitted plane to reflect the degree of deviation between the point cloud point and the plane. It is obtained by fitting using the following calculation formula:

[0039]

[0040] Wherein, (xi, yi, zi) represents the i-th point cloud in the three-dimensional point cloud data P, and n represents the total number of point clouds in the three-dimensional point cloud data P.

[0041] Preferably, said S3 includes S31 and S32;

[0042] S31, triggering a crack verification mechanism according to a judgment result R, triggering the crack verification mechanism when the judgment result R=1, registering the three-dimensional point cloud data P through the crack position feature vector V, merging point cloud data from different perspectives and at different times into the same coordinate system, and obtaining the registered point cloud data Pa, including merging using a rigid transformation method;

[0043] The point cloud data Pa is obtained by the calculation formula Pa=XZ*P+PY;

[0044] The rigid transformation method includes a rotation matrix XZ and a translation vector PY, wherein the rotation matrix XZ represents the rotation state of the point cloud data in the three-dimensional point cloud data P, and the translation vector PY represents the translation information of the point cloud data in the three-dimensional point cloud data P.

[0045] Preferably, S32, using a clustering algorithm and a point cloud filtering method to extract a point cloud area related to the crack on the registered point cloud data Pa, including using voxel grid filtering and a region growing method for extraction, and then using a Poisson reconstruction algorithm to construct a three-dimensional model C of the crack based on the extracted point cloud area, and performing a geometric analysis to obtain the length CD, width KD and depth SD of the crack in the three-dimensional model C, and marking the crack in the three-dimensional model C;

[0046] The three-dimensional model C is obtained by a Poisson reconstruction algorithm formula;

[0047] The length CD is obtained by the calculation formula CD=max(||Ci-Cj||), i, j∈{1, 2, 3, ..., n};

[0048] Wherein, max represents the maximum value function, ||Ci-Cj|| represents the Euclidean distance, specifically the straight-line distance between point cloud i and point cloud j in the three-dimensional model C;

[0049] The width KD is obtained by the calculation formula KD=max(||Ci-Cj||), Ci, Cj⊥CD;

[0050] Where Ci, Cj⊥CD represents two points of point cloud i and point cloud j perpendicular to the length CD direction in the three-dimensional model C;

[0051] The depth SD is obtained by the calculation formula SD=max(||Ci-Cbottom||), i∈{1, 2, 3, ..., n};

[0052] Where Cbottom represents the bottom limit point of the three-dimensional model C.

[0053] Preferably, the S4 includes S41;

[0054] S41, performing risk assessment of cracks on bridge results based on the established three-dimensional crack model C, and obtaining a comprehensive assessment index ZR of the impact of cracks on bridge stability;

[0055] The comprehensive evaluation index ZR is obtained by the following calculation formula:

[0056] ZR=z1*CD+z2*KD+z3*SD+z4*QYL+z5*QCL;

[0057] Wherein, QYL represents bridge stress, which is obtained by bridge stress state monitoring sensor. QCL represents bridge material property, which is determined according to the material and physical properties used in the bridge, including material test and standard value marked in the design manual. z1, z2, z3, z4 and z5 represent the preset weight values ​​of length CD, width KD, depth SD, bridge stress QYL and bridge material property QCL, respectively. And z1+z2+z3+z4+z5=1. The specific value is set by the user.

[0058] Preferably, the bridge crack repair plan and detection plan are obtained by the following comparison method:

[0059] When the comprehensive evaluation index ZR ≥ the preparation evaluation threshold Tthe, a preparation and repair plan for bridge cracks is generated, including sending the bridge crack location information to the relevant preparation and repair department's pending list for waiting for processing, and simultaneously generating a detection plan, including adjusting the detection frequency of the bridge crack location to twice the preset frequency;

[0060] When the comprehensive evaluation index ZR is less than the maintenance evaluation threshold Tthe, no maintenance and repair plan for the bridge cracks is generated, and the detection plan is initialized, including adjusting the current detection frequency of the bridge crack position to the preset frequency.

[0061] A bridge crack detection system includes a surface image acquisition module, a laser scanning module, a verification module, a risk assessment module and a decision generation module;

[0062] The surface image acquisition module uses a camera to shoot the bridge surface, pre-processes the acquired bridge surface image, and then detects and extracts the processed bridge surface image to obtain a crack position feature vector V;

[0063] The laser scanning module uses a laser radar device to scan the crack area of ​​the bridge according to the obtained crack position feature vector V, obtains the three-dimensional coordinates of the bridge surface, and generates three-dimensional point cloud data P, and performs preliminary analysis based on the three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, and obtains a judgment result R;

[0064] The verification module triggers the crack verification mechanism according to the judgment result R, including registering the three-dimensional point cloud data P and the judgment result R, and then establishing a three-dimensional model C of the crack;

[0065] The risk assessment module performs risk assessment of cracks on bridge results based on the established three-dimensional model C of cracks, and obtains a comprehensive assessment index ZR of the impact of cracks on bridge stability;

[0066] The decision-making module compares the obtained comprehensive evaluation index ZR with a preset maintenance evaluation threshold Tthe to generate a maintenance repair plan and a detection plan for bridge cracks.

[0067] The present invention provides a bridge crack detection method and system, which have the following beneficial effects:

[0068] (1) High-precision three-dimensional point cloud data P is generated through laser radar scanning. By aligning the three-dimensional point cloud data P and establishing a three-dimensional model C of the crack, the spatial accuracy of crack assessment is further improved, and the impact of cracks on the stability of the bridge structure can be accurately assessed. The comprehensive assessment index ZRZ is calculated and compared with the preset maintenance assessment threshold Tthe to help determine whether repair is needed and how to repair it. This full-process detection method solves the problems of insufficient accuracy and information fragmentation in traditional single detection methods, making the crack assessment results more comprehensive and reliable, providing more powerful technical support for bridge safety, and being able to detect potential crack risks in advance. It can also extend the service life of the bridge through scientific assessment and repair plans, ensuring that the safety of the bridge is greatly improved, thereby effectively preventing structural failures and safety accidents caused by cracks.

[0069] (2) The crack verification mechanism is activated by judging the result R = 1, and the crack position feature vector V is combined with the three-dimensional point cloud data P to obtain the registered point cloud data Pa. This step effectively avoids data inconsistency caused by point cloud data misalignment or perspective differences, ensuring that the geometric features of the crack can be accurately restored after multiple scans. The Poisson reconstruction algorithm is used to successfully construct the three-dimensional model C of the crack. Through accurate crack geometric feature extraction, data support can be provided for bridge reinforcement and repair, significantly improving the reliability and timeliness of bridge safety assessment and reducing the uncertainty caused by manual inspection and experience judgment.

[0070] (3) Based on the comparison between the comprehensive evaluation index ZR and the preset maintenance evaluation threshold Tthe, the maintenance and repair plan can be automatically generated or the detection plan can be adjusted. Dynamic feedback is effectively established between crack repair and monitoring, which can reduce unnecessary waste of maintenance resources while ensuring bridge safety, achieve efficient resource allocation and management, and improve the intelligence level of crack detection and repair decisions. It also optimizes the utilization of monitoring resources by dynamically adjusting the monitoring frequency, effectively reducing maintenance costs. At the same time, the quantitative evaluation based on the comprehensive evaluation index ensures the scientific nature and pertinence of the maintenance and repair plan, thereby providing more accurate and real-time decision support in ensuring bridge safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of the steps of a bridge crack detection method of the present invention;

[0072] Figure 2 The present invention is a schematic diagram of a bridge crack detection system block diagram. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0074] Example 1

[0075] The present invention provides a bridge crack detection method, please refer to Figure 1 , including the following steps:

[0076] S1, using a camera to shoot the bridge surface, preprocessing the acquired bridge surface image, and then detecting and extracting the processed bridge surface image to obtain a crack position feature vector V;

[0077] S2. Scanning the crack area of ​​the bridge using a laser radar device according to the obtained crack position feature vector V, obtaining the three-dimensional coordinates of the bridge surface, and generating three-dimensional point cloud data P. Performing a preliminary analysis based on the three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, and obtaining a determination result R;

[0078] S3, triggering a crack verification mechanism according to the judgment result R, including registering the three-dimensional point cloud data P and the judgment result R, and then establishing a three-dimensional model C of the crack;

[0079] S4. Perform risk assessment of cracks on bridge results based on the established three-dimensional crack model C, and obtain a comprehensive assessment index ZR of the impact of cracks on bridge stability;

[0080] S5. Compare the obtained comprehensive evaluation index ZR with the preset maintenance evaluation threshold Tthe to generate a maintenance repair plan and a detection plan for bridge cracks.

[0081] In this embodiment, a camera is used to capture the bridge surface image and perform preprocessing, which can effectively extract the crack position feature vector V, and generate high-precision three-dimensional point cloud data P through laser radar scanning, providing more abundant crack information. This process not only ensures that the crack is more accurately located, but also combines the multi-source information fusion of image data and three-dimensional point cloud data to provide a more accurate basis for the depth, morphology and topological structure analysis of the crack. Then, by registering the three-dimensional point cloud data P and establishing a three-dimensional model C of the crack, the spatial accuracy of the crack assessment is further improved, and the impact of the crack on the stability of the bridge structure can be accurately assessed, and the comprehensive assessment index ZRZ can be calculated and compared with the preset maintenance assessment threshold Tthe to help determine whether repair is needed and how to repair it. This full-process detection method solves the problems of insufficient accuracy and information fragmentation in traditional single detection methods, making the crack assessment results more comprehensive and reliable, providing more powerful technical support for bridge safety, and being able to detect potential crack risks in advance. It can also extend the service life of the bridge through scientific evaluation and repair plans, ensuring that the safety of the bridge is greatly improved, thereby effectively preventing structural failures and safety accidents caused by cracks.

[0082] Example 2

[0083] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11, S12 and S13;

[0084] S11. Use a high-definition camera and an infrared camera to capture images of the bridge surface to obtain a bridge surface image set Iraw. Then perform image preprocessing operations on the bridge surface image set Iraw, including performing denoising preprocessing using a Gaussian filter to obtain a denoised image set Ipre after the denoising preprocessing. Perform image enhancement preprocessing using histogram equalization to improve image contrast to obtain an enhanced image set Ib after the image enhancement preprocessing.

[0085] S12, performing crack detection on the enhanced image set Ib by using an edge detection algorithm to obtain a crack edge region Iedges;

[0086] The crack edge region Iedges is specifically obtained through a gradient calculation step, a non-maximum suppression step and an edge connection step;

[0087] The gradient calculation step calculates the gradient of each pixel in the image, including using the Sobel operator to calculate the gradient magnitude and direction of the image;

[0088] The non-maximum suppression step is used to refine the edge. Each pixel is detected along the gradient direction to see if it is a local maximum. If the pixel is not a local maximum, it is set to 0, otherwise the gradient value of the pixel is retained.

[0089] The edge connection step uses a dual threshold method to connect edges and distinguish whether the edge is a strong edge or a weak edge. Specifically, the distinction is made by setting a distinction threshold T1 and a distinction threshold T2, and the distinction threshold T1 is less than the distinction threshold T2.

[0090] When the gradient amplitude is greater than the distinction threshold T2, the pixel value is determined to be a strong edge;

[0091] When the gradient amplitude is less than the distinction threshold T1, the pixel value is determined to be non-edge;

[0092] When the distinction threshold T1 < gradient amplitude < distinction threshold T2, the pixel value is determined to be a weak edge. If it is connected to a strong edge, it is determined to be a valid edge.

[0093] S13, extracting features from the crack edge region Iedges, obtaining the spatial position features of the crack, and constructing a feature vector V, including the center coordinates (x, y), length Lmax, and width Wmax of the crack;

[0094] Among them, the center coordinates (x, y) are obtained by calculating the geometric center of the crack area, and x and y represent the horizontal and vertical coordinate values ​​of the center point respectively; the length Lmax is obtained by calculating the maximum extension length of the crack; and the width Wmax is obtained by calculating the regional histogram of the crack.

[0095] In this embodiment, the bridge surface image set Iraw taken by a high-definition camera and an infrared camera is subjected to Gaussian filtering denoising and histogram equalization to enhance contrast, and the denoised image set Ipre and the enhanced image set Ib are obtained, which provide a high-quality image basis for subsequent crack detection. Through the edge detection algorithm, the edge area Iedges of the crack is further extracted, and the steps of gradient calculation, non-maximum suppression and edge connection are adopted to effectively eliminate noise and ensure that the edge features of the crack are accurately identified. In particular, the double threshold method is used to finely distinguish strong edges from weak edges, which enhances the robustness of crack detection. Finally, based on the crack edge area Iedges, we extract the spatial position characteristics of the crack and construct the crack feature vector V, including the center coordinates (x, y) of the crack, the maximum extension length Lmax and the maximum width Wmax. These feature vectors provide accurate crack location information for subsequent three-dimensional modeling and risk assessment. In turn, the accuracy and reliability of crack detection are greatly improved, and the health status of the bridge structure can be more comprehensively evaluated, avoiding potential safety risks caused by inaccurate crack location or missed detection. This method can not only ensure the accuracy of crack detection, but also effectively reduce the impact of environmental factors or equipment problems on the results, providing reliable technical support for the long-term monitoring and maintenance of bridges.

[0096] Example 3

[0097] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0098] S21. Generate a laser radar scanning area Rscan according to the length Lmax and width Wmax information of the center coordinates (x, y) in the crack position feature vector V, and then use the laser radar device to scan the crack area of ​​the bridge to obtain the three-dimensional coordinates (x, y, z) of the bridge surface, where x, y and z represent the horizontal axis, vertical axis and longitudinal axis coordinate values ​​of the three-dimensional coordinates, respectively, and integrate several three-dimensional coordinates (x, y, z) to form a three-dimensional point cloud data P;

[0099] The laser radar scanning area Rscan is obtained by the following calculation formula:

[0100]

[0101] The three-dimensional point cloud data P is specifically P={(x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn)}.

[0102] S22, performing denoising on the generated three-dimensional point cloud data P, including using a statistical outlier removal method and a voxel grid filtering method to perform denoising, and then performing a preliminary analysis on the denoised three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, including using a plane fitting method to identify structural faults in the crack area, and obtaining a determination result R;

[0103] The judgment result R is obtained by the following analysis formula:

[0104]

[0105] When the judgment result R=1, it indicates that there are structural plane faults and crack features;

[0106] When the judgment result R = 0, it means that there are no structural plane faults and cracks;

[0107] Where FE(P) represents the fitting error index, Fthe represents the preset deviation evaluation threshold between the cloud data and the fitting surface; Ki represents the local curvature of the i-th cloud point of the three-dimensional point cloud data P, and Kthe represents the preset cloud point curvature evaluation threshold;

[0108] Among them, the fitting error index FE(P) fits the crack area in the three-dimensional point cloud data P by the least square method to obtain the plane, and the plane fitting is performed specifically by the following calculation formula:

[0109] ax+by+cz+d=0;

[0110] Where a, b and c represent the normal vector components of the horizontal axis x, vertical axis y and longitudinal axis z in the plane respectively, and the normal vector is a vector perpendicular to the plane, specifically indicating the position and orientation of the plane, and d represents the distance between the plane and the origin;

[0111] Then, the fitting error index FE(P) is obtained by using the square error calculation for the fitted plane to reflect the degree of deviation between the point cloud point and the plane. It is obtained by fitting using the following calculation formula:

[0112]

[0113] Wherein, (xi, yi, zi) represents the i-th point cloud in the three-dimensional point cloud data P, and n represents the total number of point clouds in the three-dimensional point cloud data P.

[0114] In this embodiment, the laser radar scanning area Rscan is determined according to the center coordinates (x, y), the maximum length Lmax and the maximum width Wmax of the crack, and then the laser radar scanning is performed in the area to obtain the complete three-dimensional coordinate data (x, y, z). This process ensures that the spatial information of the crack area is fully captured. Then, the generated three-dimensional point cloud data P is denoised, including the use of statistical outlier removal method and voxel grid filtering method, thereby removing noise points and irrelevant data, ensuring the accuracy and reliability of the point cloud data. On this basis, the plane fitting method is used to analyze the three-dimensional point cloud in the crack area to identify whether there is a structural plane fault or crack feature, and obtain the judgment result R. When the judgment result R=1, it means that there is a structural plane fault and crack feature in the crack area. Through this judgment method based on three-dimensional point cloud data, not only the spatial accuracy of crack detection is improved, but also the crack and its structural characteristics can be effectively identified, avoiding the misjudgment and missed judgment problems that may occur in traditional two-dimensional image analysis. Through accurate three-dimensional scanning and data analysis, more reliable basic data can be provided for subsequent risk assessment and repair, thereby improving the safety and scientificity of bridge detection.

[0115] Example 4

[0116] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0117] S31, triggering a crack verification mechanism according to a judgment result R, triggering the crack verification mechanism when the judgment result R=1, registering the three-dimensional point cloud data P through the crack position feature vector V, merging point cloud data from different perspectives and at different times into the same coordinate system, and obtaining the registered point cloud data Pa, including merging using a rigid transformation method;

[0118] The point cloud data Pa is obtained by the calculation formula Pa=XZ*P+PY;

[0119] The rigid transformation method includes a rotation matrix XZ and a translation vector PY, wherein the rotation matrix XZ represents the rotation state of the point cloud data in the three-dimensional point cloud data P. Specifically, each point in the three-dimensional point cloud data P is rotated by the least squares method. After each point of the point cloud data is rotated, multiple point cloud data are aligned in the same coordinate system for acquisition. The translation vector PY represents the translation information of the point cloud data in the three-dimensional point cloud data P. Specifically, the least squares method is used to translate the entire point cloud along a certain direction so that the point cloud can be aligned with the target position to obtain a three-dimensional vector, which specifically represents the translation amount in the three-dimensional coordinates (x, y, z).

[0120] S32, using a clustering algorithm and a point cloud filtering method to extract a point cloud area related to the crack on the registered point cloud data Pa, including extracting using a voxel grid filter and a region growing method, and then constructing a three-dimensional model C of the crack based on the extracted point cloud area using a Poisson reconstruction algorithm, and performing a geometric analysis to obtain a length CD, a width KD, and a depth SD of the crack in the three-dimensional model C, and marking the crack in the three-dimensional model C;

[0121] The three-dimensional model C is obtained by a Poisson reconstruction algorithm formula;

[0122] The length CD is obtained by the calculation formula CD=max(||Ci-Cj||), i, j∈{1, 2, 3, ..., n};

[0123] Wherein, max represents the maximum value function, ||Ci-Cj|| represents the Euclidean distance, specifically the straight-line distance between point cloud i and point cloud j in the three-dimensional model C;

[0124] The width KD is obtained by the calculation formula KD=max(||Ci-Cj||), Ci, Cj⊥CD;

[0125] Where Ci, Cj⊥CD represents two points of point cloud i and point cloud j perpendicular to the length CD direction in the three-dimensional model C;

[0126] The depth SD is obtained by the calculation formula SD=max(||Ci-Cbottom||), i∈{1, 2, 3, ..., n};

[0127] Wherein, Cbottom represents the bottom limit point of the three-dimensional model C, specifically the minimum point of the z-axis in the three-dimensional model C.

[0128] In this embodiment, the crack verification mechanism is activated by judging the result R=1, the crack position feature vector V is combined with the three-dimensional point cloud data P, and the point cloud data is registered using the rigid transformation method to ensure that the point cloud data from different perspectives and different times can be accurately aligned to the same coordinate system, and the registered point cloud data Pa is obtained. This step effectively avoids data inconsistency caused by point cloud data dislocation or perspective differences, ensures that the geometric features of the crack can be accurately restored under multiple scans, and then uses clustering algorithms and point cloud filtering methods to further extract crack-related point cloud areas, and combines voxel grid filtering and regional growth methods to filter out noise and irrelevant areas, retaining only the effective point cloud of the crack. Based on these accurately extracted point cloud areas, the Poisson reconstruction algorithm is used to successfully construct a three-dimensional model C of the crack. In this model, the geometric features of the crack, including length CD, width KD and depth SD, are accurately calculated and marked, which not only improves the accuracy of crack detection, but also provides detailed spatial data for subsequent bridge stability assessment and repair planning. Through accurate extraction of crack geometry features, data support can be provided for bridge reinforcement and repair, which significantly improves the reliability and timeliness of bridge safety assessment and reduces the uncertainty caused by manual inspection and experience-based judgment.

[0129] Example 5

[0130] This embodiment is explained in Example 4. Please refer to Figure 1 Specifically: S4 includes S41;

[0131] S41, performing risk assessment of cracks on bridge results based on the established three-dimensional crack model C, and obtaining a comprehensive assessment index ZR of the impact of cracks on bridge stability;

[0132] The comprehensive evaluation index ZR is obtained by the following calculation formula:

[0133] ZR=z1*CD+z2*KD+z3*SD+z4*QYL+z5*QCL;

[0134] Wherein, QYL represents bridge stress, which is obtained by bridge stress state monitoring sensor. QCL represents bridge material property, which is determined according to the material and physical properties used in the bridge, including material test and standard value marked in the design manual. z1, z2, z3, z4 and z5 represent the preset weight values ​​of length CD, width KD, depth SD, bridge stress QYL and bridge material property QCL, respectively. And z1+z2+z3+z4+z5=1. The specific value is set by the user.

[0135] The preparation and repair scheme and detection scheme for the bridge cracks are obtained by the following comparison method:

[0136] When the comprehensive evaluation index ZR ≥ the preparation evaluation threshold Tthe, a preparation and repair plan for bridge cracks is generated, including sending the bridge crack location information to the relevant preparation and repair department's pending list for waiting for processing, and simultaneously generating a detection plan, including adjusting the detection frequency of the bridge crack location to twice the preset frequency;

[0137] When the comprehensive evaluation index ZR is less than the maintenance evaluation threshold Tthe, no maintenance and repair plan for the bridge cracks is generated, and the detection plan is initialized, including adjusting the current detection frequency of the bridge crack position to the preset frequency.

[0138] In this embodiment, based on the comparison between the comprehensive evaluation index ZR and the preset maintenance evaluation threshold Tthe, a maintenance and repair plan or an adjustment detection plan can be automatically generated. Dynamic feedback is effectively established between crack repair and monitoring, which can reduce unnecessary waste of maintenance resources while ensuring bridge safety, achieve efficient resource allocation and management, and improve the intelligence level of crack detection and repair decisions. It also optimizes the utilization of monitoring resources by dynamically adjusting the monitoring frequency, effectively reducing maintenance costs. At the same time, the quantitative evaluation based on the comprehensive evaluation index ensures the scientificity and pertinence of the maintenance and repair plan, thereby providing more accurate and real-time decision support in ensuring bridge safety.

[0139] Example 6

[0140] A bridge crack detection system, please refer to Figure 2 ,Specifically: including surface image acquisition module, laser scanning module, verification module, risk assessment module and decision making module;

[0141] The surface image acquisition module uses a camera to shoot the bridge surface, pre-processes the acquired bridge surface image, and then detects and extracts the processed bridge surface image to obtain a crack position feature vector V;

[0142] The laser scanning module uses a laser radar device to scan the crack area of ​​the bridge according to the obtained crack position feature vector V, obtains the three-dimensional coordinates of the bridge surface, and generates three-dimensional point cloud data P, and performs preliminary analysis based on the three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, and obtains a judgment result R;

[0143] The verification module triggers the crack verification mechanism according to the judgment result R, including registering the three-dimensional point cloud data P and the judgment result R, and then establishing a three-dimensional model C of the crack;

[0144] The risk assessment module performs risk assessment of cracks on bridge results based on the established three-dimensional model C of cracks, and obtains a comprehensive assessment index ZR of the impact of cracks on bridge stability;

[0145] The decision-making module compares the obtained comprehensive evaluation index ZR with a preset maintenance evaluation threshold Tthe to generate a maintenance repair plan and a detection plan for bridge cracks.

[0146] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A bridge crack detection method, characterized in that: The following steps are involved: S1, using a camera to shoot the bridge surface, preprocessing the acquired bridge surface image, and then detecting and extracting the processed bridge surface image to obtain a crack position feature vector V; S2. Scanning the crack area of ​​the bridge using a laser radar device according to the obtained crack position feature vector V, obtaining the three-dimensional coordinates of the bridge surface, and generating three-dimensional point cloud data P. Performing a preliminary analysis based on the three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, and obtaining a determination result R; S3, triggering a crack verification mechanism according to the judgment result R, including registering the three-dimensional point cloud data P and the judgment result R, and then establishing a three-dimensional model C of the crack; S4. Perform risk assessment of cracks on bridge results based on the established three-dimensional crack model C, and obtain a comprehensive assessment index ZR of the impact of cracks on bridge stability; S5. Compare the obtained comprehensive evaluation index ZR with the preset maintenance evaluation threshold Tthe to generate a maintenance repair plan and a detection plan for bridge cracks.

2. A bridge crack detection method according to claim 1, characterized in that: Said S1 includes S11, S12 and S13; S11. Use a high-definition camera and an infrared camera to capture images of the bridge surface to obtain a bridge surface image set Iraw. Then perform image preprocessing operations on the bridge surface image set Iraw, including performing denoising preprocessing using a Gaussian filter to obtain a denoised image set Ipre after the denoising preprocessing. Perform image enhancement preprocessing using histogram equalization to improve image contrast to obtain an enhanced image set Ib after the image enhancement preprocessing.

3. A bridge crack detection method according to claim 2, characterized in that: S12, performing crack detection on the enhanced image set Ib by using an edge detection algorithm to obtain a crack edge region Iedges; The crack edge region Iedges is specifically obtained through a gradient calculation step, a non-maximum suppression step and an edge connection step; The gradient calculation step calculates the gradient of each pixel in the image, including using the Sobel operator to calculate the gradient magnitude and direction of the image; The non-maximum suppression step is used to refine the edge. Each pixel is detected along the gradient direction to see if it is a local maximum. If the pixel is not a local maximum, it is set to 0, otherwise the gradient value of the pixel is retained. The edge connection step uses a dual threshold method to connect edges and distinguish whether the edge is a strong edge or a weak edge. Specifically, the distinction is made by setting a distinction threshold T1 and a distinction threshold T2, and the distinction threshold T1 is less than the distinction threshold T2. When the gradient amplitude is greater than the distinction threshold T2, the pixel value is determined to be a strong edge; When the gradient amplitude is less than the distinction threshold T1, the pixel value is determined to be non-edge; When the distinction threshold T1 < gradient amplitude < distinction threshold T2, the pixel value is determined to be a weak edge. If it is connected to a strong edge, it is determined to be a valid edge. S13, extracting features from the crack edge region Iedges, obtaining the spatial position features of the crack, and constructing a feature vector V, including the center coordinates (x, y), length Lmax, and width Wmax of the crack; Among them, the center coordinates (x, y) are obtained by calculating the geometric center of the crack area, and x and y represent the horizontal and vertical coordinate values ​​of the center point respectively; the length Lmax is obtained by calculating the maximum extension length of the crack; and the width Wmax is obtained by calculating the regional histogram of the crack.

4. A bridge crack detection method according to claim 1, characterized in that: The S2 includes S21 and S22; S21. Generate a laser radar scanning area Rscan according to the length Lmax and width Wmax information of the center coordinates (x, y) in the crack position feature vector V, and then use the laser radar device to scan the crack area of ​​the bridge to obtain the three-dimensional coordinates (x, y, z) of the bridge surface, where x, y and z represent the horizontal axis, vertical axis and longitudinal axis coordinate values ​​of the three-dimensional coordinates, respectively, and integrate several three-dimensional coordinates (x, y, z) to form a three-dimensional point cloud data P; The laser radar scanning area Rscan is obtained by the following calculation formula: The three-dimensional point cloud data P is specifically P={(x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn)}.

5. A bridge crack detection method according to claim 4, characterized in that: S22, performing denoising on the generated three-dimensional point cloud data P, including using a statistical outlier removal method and a voxel grid filtering method to perform denoising, and then performing a preliminary analysis on the denoised three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, including using a plane fitting method to identify structural faults in the crack area, and obtaining a determination result R; The judgment result R is obtained by the following analysis formula: When the judgment result R=1, it indicates that there are structural plane faults and crack features; When the judgment result R = 0, it means that there are no structural plane faults and cracks; Where FE(P) represents the fitting error index, Fthe represents the preset deviation evaluation threshold between the cloud data and the fitting surface; Ki represents the local curvature of the i-th cloud point of the three-dimensional point cloud data P, and Kthe represents the preset cloud point curvature evaluation threshold; Among them, the fitting error index FE(P) fits the crack area in the three-dimensional point cloud data P by the least square method to obtain the plane, and the plane fitting is performed specifically by the following calculation formula: ax+by+cz+d=0; Where a, b and c represent the normal vector components of the horizontal axis x, vertical axis y and longitudinal axis z in the plane respectively, and d represents the distance between the plane and the origin; Then, the fitting error index FE(P) is obtained by using the square error calculation for the fitted plane to reflect the degree of deviation between the point cloud point and the plane. It is obtained by fitting through the following calculation formula: Wherein, (xi, yi, zi) represents the i-th point cloud in the three-dimensional point cloud data P, and n represents the total number of point clouds in the three-dimensional point cloud data P.

6. A bridge crack detection method according to claim 1, characterized in that: The S3 includes S31 and S32; S31, triggering a crack verification mechanism according to a judgment result R, triggering the crack verification mechanism when the judgment result R=1, registering the three-dimensional point cloud data P through the crack position feature vector V, merging point cloud data from different perspectives and at different times into the same coordinate system, and obtaining the registered point cloud data Pa, including merging using a rigid transformation method; The point cloud data Pa is obtained by the calculation formula Pa=XZ*P+PY; The rigid transformation method includes a rotation matrix XZ and a translation vector PY, wherein the rotation matrix XZ represents the rotation state of the point cloud data in the three-dimensional point cloud data P, and the translation vector PY represents the translation information of the point cloud data in the three-dimensional point cloud data P.

7. A bridge crack detection method according to claim 6, characterized in that: S32, using a clustering algorithm and a point cloud filtering method to extract a point cloud area related to the crack on the registered point cloud data Pa, including extracting using a voxel grid filter and a region growing method, and then constructing a three-dimensional model C of the crack based on the extracted point cloud area using a Poisson reconstruction algorithm, and performing a geometric analysis to obtain a length CD, a width KD, and a depth SD of the crack in the three-dimensional model C, and marking the crack in the three-dimensional model C; The three-dimensional model C is obtained by a Poisson reconstruction algorithm formula; The length CD is obtained by the calculation formula CD=max(||Ci-Cj||), i, j∈{1, 2, 3, ..., n}; Wherein, max represents the maximum value function, ||Ci-Cj|| represents the Euclidean distance, specifically the straight-line distance between point cloud i and point cloud j in the three-dimensional model C; The width KD is obtained by the calculation formula KD=max(||Ci-Cj||), Ci, Cj⊥CD; Where Ci, Cj⊥CD represents two points of point cloud i and point cloud j perpendicular to the length CD direction in the three-dimensional model C; The depth SD is obtained by the calculation formula SD=max(||Ci-Cbottom||), i∈{1, 2, 3, ..., n}; Where Cbottom represents the lower limit point of the three-dimensional model C.

8. A bridge crack detection method according to claim 7, characterized in that: The S4 includes S41; S41, performing risk assessment of cracks on bridge results based on the established three-dimensional crack model C, and obtaining a comprehensive assessment index ZR of the impact of cracks on bridge stability; The comprehensive evaluation index ZR is obtained by the following calculation formula: ZR=z1*CD+z2*KD+z3*SD+z4*QYL+z5*QCL; Wherein, QYL represents bridge stress, which is obtained by bridge stress state monitoring sensor. QCL represents bridge material property, which is determined according to the material and physical properties used in the bridge, including material test and standard value marked in the design manual. z1, z2, z3, z4 and z5 represent the preset weight values ​​of length CD, width KD, depth SD, bridge stress QYL and bridge material property QCL, respectively. And z1+z2+z3+z4+z5=1. The specific value is set by the user.

9. A bridge crack detection method according to claim 1, characterized in that: The preparation and repair scheme and detection scheme for the bridge cracks are obtained by the following comparison method: When the comprehensive evaluation index ZR ≥ the preparation evaluation threshold Tthe, a preparation and repair plan for bridge cracks is generated, including sending the bridge crack location information to the relevant preparation and repair department's pending list for waiting for processing, and simultaneously generating a detection plan, including adjusting the detection frequency of the bridge crack location to twice the preset frequency; When the comprehensive evaluation index ZR is less than the maintenance evaluation threshold Tthe, no maintenance and repair plan for the bridge cracks is generated, and the detection plan is initialized, including adjusting the current detection frequency of the bridge crack position to the preset frequency.

10. A bridge crack detection system, applied to a bridge crack detection method according to any one of claims 1 to 9, characterized in that: It includes a surface image acquisition module, a laser scanning module, a verification module, a risk assessment module and a decision-making module; The surface image acquisition module uses a camera to shoot the bridge surface, pre-processes the acquired bridge surface image, and then detects and extracts the processed bridge surface image to obtain a crack position feature vector V; The laser scanning module uses a laser radar device to scan the crack area of ​​the bridge according to the obtained crack position feature vector V, obtains the three-dimensional coordinates of the bridge surface, and generates three-dimensional point cloud data P, and performs preliminary analysis based on the three-dimensional point cloud data P to determine whether there are structural plane faults and crack features, and obtains a judgment result R; The verification module triggers the crack verification mechanism according to the judgment result R, including registering the three-dimensional point cloud data P and the judgment result R, and then establishing a three-dimensional model C of the crack; The risk assessment module performs risk assessment of cracks on bridge results based on the established three-dimensional model C of cracks, and obtains a comprehensive assessment index ZR of the impact of cracks on bridge stability; The decision generation module compares the obtained comprehensive evaluation index ZR with a preset maintenance evaluation threshold Tthe to generate a maintenance repair plan and a detection plan for bridge cracks.

Images