A method for tunnel cross-section identification and deformation detection based on laser scanning point cloud data

Abstract

This invention provides a method for tunnel cross-section identification and deformation detection based on laser scanning point cloud data. It extracts the central axis by directly processing the point cloud data in three-dimensional space through polar coordinate transformation and local weighted averaging, thus completely preserving the three-dimensional information of the point cloud data and more accurately reflecting the true spatial morphology of the tunnel. This avoids the loss of geometric feature information at the top and bottom of the tunnel due to projection. Based on this, tunnel cross-section point cloud data is extracted and determined. Adaptive shape fitting is used for global and local deformation discrimination, enabling precise monitoring and discrimination of tunnel cross-section deformation. This method can keenly capture minute deformations that may occur in local areas of the tunnel, and can detect potential safety hazards in advance, even when the overall deformation is not yet obvious. It achieves comprehensive and precise monitoring and discrimination of tunnel cross-section deformation, providing strong technical support for the long-term safe operation of tunnels.

Description

Technical Field

[0001] This invention relates to the field of non-destructive testing technology for tunnel defects, and more specifically to a method for tunnel cross-section identification and deformation detection based on laser scanning point cloud data. Background Technology

[0002] Tunnel engineering plays a vital role in modern transportation. With the development and expansion of cities and transportation, the depth and width of tunnels such as subway tunnels, high-speed rail tunnels, underground tunnels, and underwater tunnels are constantly expanding. Due to the influence of traffic volume, travel time, changes in soil pressure of the tunnel surrounding rock, concrete shrinkage, rainwater erosion, and the quality of the tunnel lining itself (such as uneven thickness, misalignment, voids, etc.), the monitoring of defects such as deformation, cracks, and spalling in tunnels is particularly important. Accurate, rapid, and automated defect monitoring of tunnels through automated detection methods is the key to ensuring traffic safety.

[0003] Currently, tunnel cross-sectional deformation monitoring mainly relies on tunnel point cloud data to analyze and provide early warnings about tunnel deformation. For example, based on laser scanning point cloud data, cross-sections are extracted using the central axis, and then image processing is performed on the cross-sections. Existing research focuses on cutting cross-sections based on the central axis, but extracting the central axis itself is a technical challenge. The tunnel's internal axial direction is not a straight line; its height variations and angle changes all pose challenges to mathematical methods for extracting the spatial central axis.

[0004] Existing methods, such as those proposed by Du Liming et al., involve projecting tunnel point cloud data onto a horizontal plane and calculating the projection point set of the central axis on that plane. The final spatial central axis is then obtained by searching for high and low points based on this point set. However, as mentioned earlier, the internal orientation of a tunnel is a complex nonlinear structure, such as a non-circular tunnel, a tunnel with curves, or a tunnel with a complex internal structure. When the tunnel point cloud data is projected onto a horizontal plane, the depth information in three-dimensional space is lost. In reality, tunnels are complex geometric bodies with a three-dimensional spatial structure. The projection operation causes the loss or deformation of geometric feature information of the tunnel's top and bottom in the vertical direction (depth direction). For tunnels with different slopes at the top or bottom, these features are "flattened" onto the plane after projection, making the central axis calculated based on the projected point set on the plane unable to accurately reflect the true shape of the tunnel in the vertical direction. Summary of the Invention

[0005] To address the problems in tunnel cross-section identification and deformation monitoring, this invention aims to provide a method for tunnel cross-section identification and deformation detection based on laser scanning point cloud data. This method extracts the central axis by directly processing the point cloud data in three-dimensional space through polar coordinate transformation and local weighted averaging, thus completely preserving the three-dimensional information of the point cloud data and avoiding the loss of geometric feature information of the tunnel top and bottom caused by projection. Based on this, the method extracts and determines the tunnel cross-section point cloud data, and combines adaptive shape fitting to perform global and local deformation discrimination, achieving accurate monitoring and discrimination of tunnel cross-section deformation.

[0006] According to a first aspect of the present invention, a method for tunnel cross-section identification and deformation detection based on laser scanning point cloud data is proposed, comprising the following steps:

[0007] Step 1: Obtain point cloud data from any two adjacent stations within the tunnel. Complete the registration of point cloud data from all laser scanning stations through point cloud registration to obtain tunnel point cloud data.

[0008] Step 2: Denoise the entire tunnel point cloud data and simplify the point cloud data space to obtain voxelized point cloud data;

[0009] Step 3: Extract the local centerline points of the tunnel based on polar coordinate transformation, and obtain the tunnel centerline after global centerline fitting and optimization;

[0010] Step 4: Based on the obtained tunnel centerline, determine the plane perpendicular to the centerline as the cross-section cutting plane according to the spacing, and calculate the equation of the cross-section cutting plane.

[0011] Step 5: Segment the points located within a certain distance range on both sides of each cross-sectional cutting plane in the point cloud data, and use them as the original point cloud data of that cross-sectional cutting plane. After removing isolated points, sort and number them to obtain the cross-sectional point cloud data.

[0012] Step 6: Perform adaptive shape analysis on the cross-sectional point cloud data and adaptive shape fitting to obtain the shape and size parameters of the fitted tunnel cross-section.

[0013] Step 7: Based on the shape and size parameters of the tunnel cross-section obtained from scanning at the same location at different times, conduct a deformation assessment of the tunnel cross-section to determine the magnitude and direction of the deformation change;

[0014] Step 8: Divide the tunnel cross section locally and monitor local deformation based on the changes in the divided sub-regions.

[0015] In a further embodiment, step 1, which involves registering the point cloud data of all laser scanning stations through point cloud registration to obtain tunnel point cloud data, includes:

[0016] Step 1.1: Based on the feature points, solve for the rigid transformation rotation matrix and translation vector between stations, transform the point cloud data, and complete the coarse registration;

[0017] Step 1.2: Based on the coarse registration, optimization is performed using the ICP algorithm, adopting a point-to-point distance metric, and the LM algorithm is used to iteratively optimize and solve the transformation matrix to minimize the error function.

[0018] In a further embodiment, step 2 involves denoising the entire tunnel point cloud data and spatially simplifying the point cloud data to obtain voxelized point cloud data, including:

[0019] Step 2.1: Perform statistical filtering and radius filtering on the tunnel point cloud data to remove marked noise points;

[0020] Step 2.2: Divide the point cloud data space into voxel grids. For each voxel grid, retain only one representative point. Use the point closest to the center of the voxel grid as the representative point to obtain voxelized point cloud data.

[0021] In a further embodiment, step 3, extracting the local centerline points of the tunnel based on polar coordinate transformation, and obtaining the tunnel centerline after global centerline fitting optimization, includes:

[0022] Step 3.1: Transform the voxelized point cloud data from the Cartesian coordinate system to the polar coordinate system, and determine the polar radius and polar angle of any point in the point cloud data;

[0023] Step 3.2: Within each polar angle interval, select points within a certain range to the left and right of the polar angle and calculate the local centerline point based on the polar radius of the selected points. Use a weighted average to calculate the polar radius of the local centerline point.

[0024] Step 3.3: Convert the local midline points calculated for each polar angle interval back to the Cartesian coordinate system to obtain a set of discrete local midline points;

[0025] Step 3.4: Use the spline curve fitting method to fit the local centerline point set to obtain a smooth curve as the preliminary centerline of the tunnel;

[0026] Step 3.5: Optimize the preliminary centerline to obtain the tunnel centerline.

[0027] In a further embodiment, the step of selecting points within a certain range to the left and right of the polar angle within each polar angle interval, calculating the local midline point based on the polar radius of the selected points, and calculating the polar radius of the local midline point using a weighted average includes:

[0028] Within each polar angle interval, extend Δθ to the left and right of the polar angle as the center. ext Choose Δθ ext For points within the extended range, the extreme radius of the local centerline point is calculated using a weighted average:

[0029] Let Δθ ext Any point P within the extended range i Its polar radius is ρ i Its weight ω i According to point P i The distance to the center ray of the polar angle is used to determine:

[0030]

[0031] In the formula, d i Point P i The distance to the ray at the center of the polar angle, ∈ represents a minimum constant used to prevent the denominator from being 0;

[0032] The extreme radius of the local midline point The calculation formula is:

[0033]

[0034] In a further embodiment, optimizing the preliminary centerline to obtain the tunnel centerline includes:

[0035] For each point in the point cloud data within the defined boundary range around the initial centerline, the distance deviation between each point and the initial centerline is calculated;

[0036] Points whose distance deviation exceeds the set distance threshold are marked as deviation points;

[0037] The point cloud data of the initial central axis and its surrounding area are divided into local regions according to equal length intervals.

[0038] Count the number of marked deviation points in each local area, and mark the local areas with more than a certain number of deviation points as problem segments;

[0039] For the central axis segment of the marked problem segment, take the two endpoints of the problem segment as the center, extend a certain distance to both sides, and select point cloud data within a certain width in the direction perpendicular to the central axis as candidate point cloud for recalculating the local central axis;

[0040] Within the selected candidate point cloud range, the local centerline segment is recalculated using a weighted average and replaced with the centerline segment of the problem segment to update the initial centerline.

[0041] Update the centerline segments of all problematic sections and smoothly replace the endpoints of the locations to obtain the tunnel centerline.

[0042] In a further embodiment, step 6 involves performing adaptive shape analysis and adaptive shape fitting on the cross-sectional point cloud data to obtain the shape and size parameters of the fitted tunnel cross-section, including:

[0043] Step 6.1: Fit a circular cross-section using the weighted least squares method based on the cross-sectional point cloud data, and determine the radius and coordinates of the circle through iterative optimization;

[0044] Step 6.2: Estimate the directions and initial lengths of the major and minor axes of the ellipse based on the covariance matrix of the cross-sectional point cloud data, and determine the major and minor axes of the ellipse through iterative optimization.

[0045] In a further embodiment, step 7 involves evaluating the deformation of the tunnel cross-section based on the shape and size parameters of the tunnel cross-section obtained from scans at different times, and determining the magnitude and direction of the deformation change, including:

[0046] For tunnel cross-sections at the same location obtained from scans at different times, the same method was used to extract the cross-section parameters: for circular cross-sections, the center coordinates and radius were recorded at different times; for elliptical cross-sections, the major axis length, minor axis length, center coordinates, and the angle between the major axis and the coordinate axis were recorded at different times.

[0047] For circular cross-sections, the differences in radius and circular coordinates at different times are compared with a preset circular cross-section difference threshold. If the difference exceeds the circular cross-section difference threshold, it is determined that the circular tunnel cross-section has global deformation.

[0048] For elliptical cross-sections, the differences in the length of the major or minor axis and the differences in the included angle of the major axis at different times are compared with the preset elliptical cross-section difference threshold. If the difference exceeds the elliptical cross-section difference threshold, it is determined that the elliptical tunnel cross-section has global deformation.

[0049] In a further embodiment, step 8, which involves locally dividing the tunnel cross-section and monitoring local deformation based on changes in the divided sub-regions, includes:

[0050] For a circular cross-section, the circular cross-section is divided into several sector regions with equal angles. For each sector region, the local parameters of the sector are calculated: point cloud density and average radius. Then, the changes in point cloud density and average radius of the same sector region at different times are compared. If the rate of change of point cloud density exceeds the set density threshold or the change in average radius exceeds the radius deviation threshold, it is determined that there is local deformation in the sector region.

[0051] For each circular cross-section, the number of points within the sector is counted as the point cloud density, and the average distance from the point to the center of the circle is calculated as the average radius.

[0052] In a further embodiment, for an elliptical cross-section, the elliptical cross-section is divided into multiple rectangular sub-regions along the major and minor axes. Within each rectangular sub-region, the distribution characteristics of the point cloud are calculated. Then, the changes in the distribution characteristics of the point cloud in the same rectangular sub-region at different times are compared. If the distribution characteristics of the point cloud exceed a set deviation threshold, it is determined that there is local deformation in the rectangular sub-region.

[0053] For each elliptical cross section, the standard deviation of the coordinates of the points in the major and minor axes is calculated as the distribution characteristics of the point cloud, representing the degree of dispersion in the major and minor axes. By comparing the changes in the standard deviations of the major and minor axes in the same rectangular sub-region at different times, if the change in the standard deviation of the major axis or the change in the standard deviation of the minor axis exceeds the set deviation threshold, it is determined that there is local deformation in the rectangular sub-region.

[0054] The tunnel cross-section identification and deformation detection method based on laser scanning point cloud data described in the above embodiments of the present invention extracts the central axis by directly processing the point cloud data in three-dimensional space through polar coordinate transformation and local weighted averaging. This preserves the three-dimensional information of the point cloud data completely, more accurately reflecting the true spatial morphology of the tunnel and avoiding the loss of geometric feature information of the tunnel top and bottom caused by projection. Based on this, tunnel cross-section point cloud data is extracted and determined, and global and local deformation discrimination is performed by adaptive shape fitting. This enables precise monitoring and discrimination of tunnel cross-section deformation, keenly capturing minute deformations that may occur in local areas of the tunnel. Even when the overall deformation is not yet obvious, potential safety hazards can be detected in advance. This achieves comprehensive and precise monitoring and discrimination of tunnel cross-section deformation, providing strong technical support for the long-term safe operation of tunnels.

[0055] Meanwhile, the method proposed in this invention, based on polar coordinates and local weighted averaging, can calculate the local centerline based on the natural distribution of point cloud in the curved area. Furthermore, through subsequent global centerline fitting and optimization steps, it better adapts to the variations in complex tunnel shapes such as curves and non-circular tunnels. For tunnels with multi-layered structures or irregular cavities, because this invention directly processes point cloud data in three-dimensional space, it can consider the point cloud distribution around different layers and cavities. By adjusting parameters such as the range and weight of local calculations, it can more flexibly obtain the centerline that represents the complex tunnel structure, control the accuracy of centerline extraction, and has better applicability to tunnels with complex shapes, providing a more reliable foundation for subsequent cross-section identification and deformation analysis.

[0056] Compared with the prior art, the significant advantages of the present invention are:

[0057] (1) The method of this invention processes point cloud data directly in three-dimensional space, avoiding the problem of losing vertical direction (depth information) as traditional methods project point cloud data onto a horizontal plane. This invention allows for the complete utilization of the three-dimensional geometric features of point cloud data for operations such as tunnel centerline extraction, cross-section identification, and deformation detection, thereby more accurately reflecting the true spatial morphology of the tunnel. By combining local weighted averaging after polar coordinate transformation and curve fitting, it does not rely solely on local features such as high and low points, but also comprehensively utilizes the overall distribution of point cloud data. This enables better extraction of useful information from the data when dealing with complex tunnel structures and irregular point cloud distributions.

[0058] (2) During the extraction of the central axis, the weighted average method was used to consider factors such as the distance from the point to the polar angle center ray, which reduced the impact of abnormal points (such as noise points caused by scanning equipment errors, environmental interference, or irregular points caused by local unevenness of the tunnel wall, auxiliary facilities, etc.) on the extraction of the central axis, making the acquisition of the central axis more robust.

[0059] (3) For cross-section recognition, by accurately determining the cross-section cutting plane (based on the tangent direction vector of the central axis) and using adaptive fitting methods (such as weighted least squares fitting of circular and elliptical cross-sections), the fitting error caused by interference factors in point cloud data (such as noise and local irregular shapes) can be reduced, thereby improving the accuracy and reliability of cross-section recognition.

[0060] It should be understood that all combinations of the foregoing concepts and the additional concepts described in more detail below may be considered part of the inventive subject matter of this disclosure, provided that such concepts do not contradict each other. Furthermore, all combinations of the claimed subject matter are considered part of the inventive subject matter of this disclosure.

[0061] The foregoing and other aspects, embodiments, and features of the teachings of the present invention will be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the invention, such as features and / or beneficial effects of exemplary embodiments, will become apparent from the following description or may be learned through practice of specific embodiments according to the teachings of the present invention. Attached Figure Description

[0062] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component shown in the various figures may be denoted by the same reference numeral. For clarity, not every component is labeled in each figure. Embodiments of various aspects of the invention will now be described by way of example and with reference to the accompanying drawings.

[0063] Figure 1 This is a flowchart illustrating a tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to an embodiment of the present invention.

[0064] Figure 2 This is a schematic diagram of the process for obtaining the tunnel centerline based on polar coordinate transformation and processing according to an embodiment of the present invention.

[0065] Figure 3 This is a schematic diagram of the process for global optimization of the initial centerline according to an embodiment of the present invention.

[0066] Figure 4 This is a schematic diagram of the tunnel cross-section global deformation discrimination process according to an embodiment of the present invention.

[0067] Figure 5 This is a schematic diagram of the process for determining local deformation of tunnel cross sections according to an embodiment of the present invention. Detailed Implementation

[0068] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0069] Various aspects of the invention are described in this disclosure with reference to the accompanying drawings, which illustrate numerous illustrative embodiments. The embodiments of this disclosure are not necessarily intended to encompass all aspects of the invention. It should be understood that the various concepts and embodiments described above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed herein are not limited to any particular implementation. Furthermore, some aspects of the invention disclosed may be used alone or in any suitable combination with other aspects of the invention disclosed.

[0070] {Example 1}

[0071] Combination Figure 1 As shown, the tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to an embodiment of the present invention includes the following steps:

[0072] Step 1: Obtain point cloud data from any two adjacent stations within the tunnel. Complete the registration of point cloud data from all laser scanning stations through point cloud registration to obtain tunnel point cloud data.

[0073] Step 2: Denoise the entire tunnel point cloud data and simplify the point cloud data space to obtain voxelized point cloud data;

[0074] Step 3: Extract the local centerline points of the tunnel based on polar coordinate transformation, and obtain the tunnel centerline after global centerline fitting and optimization;

[0075] Step 4: Based on the obtained tunnel centerline, determine the plane perpendicular to the centerline as the cross-section cutting plane according to the spacing, and calculate the equation of the cross-section cutting plane.

[0076] Step 5: Segment the points located within a certain distance range on both sides of each cross-sectional cutting plane in the point cloud data, and use them as the original point cloud data of that cross-sectional cutting plane. After removing isolated points, sort and number them to obtain the cross-sectional point cloud data.

[0077] Step 6: Perform adaptive shape analysis on the cross-sectional point cloud data and adaptive shape fitting to obtain the shape and size parameters of the fitted tunnel cross-section.

[0078] Step 7: Based on the shape and size parameters of the tunnel cross-section obtained from scanning at the same location at different times, conduct a deformation assessment of the tunnel cross-section to determine the magnitude and direction of the deformation change;

[0079] Step 8: Divide the tunnel cross section locally and monitor local deformation based on the changes in the divided sub-regions.

[0080] In a further embodiment, in step 1, point cloud registration is performed to register the point cloud data of all laser scanning stations to obtain tunnel point cloud data, including:

[0081] Step 1.1: Based on the feature points, solve for the rigid transformation rotation matrix and translation vector between stations, transform the point cloud data, and complete the coarse registration;

[0082] Step 1.2: Based on the coarse registration, optimization is performed using the ICP algorithm, adopting a point-to-point distance metric, and the LM algorithm is used to iteratively optimize and solve the transformation matrix to minimize the error function.

[0083] In a further embodiment, in step 2, the entire tunnel point cloud data is denoised and spatially simplified to obtain voxelized point cloud data, including:

[0084] Step 2.1: Perform statistical filtering and radius filtering on the tunnel point cloud data to remove marked noise points;

[0085] Step 2.2: Divide the point cloud data space into voxel grids. For each voxel grid, retain only one representative point. Use the point closest to the center of the voxel grid as the representative point to obtain voxelized point cloud data.

[0086] In a further embodiment, combined with Figure 2 As shown, in step 3, local centerline points of the tunnel are extracted based on polar coordinate transformation, and after global centerline fitting optimization, the tunnel centerline is obtained, including:

[0087] Step 3.1: Transform the voxelized point cloud data from the Cartesian coordinate system to the polar coordinate system, and determine the polar radius and polar angle of any point in the point cloud data;

[0088] Step 3.2: Within each polar angle interval, select points within a certain range to the left and right of the polar angle and calculate the local centerline point based on the polar radius of the selected points. Use a weighted average to calculate the polar radius of the local centerline point.

[0089] Step 3.3: Convert the local midline points calculated for each polar angle interval back to the Cartesian coordinate system to obtain a set of discrete local midline points;

[0090] Step 3.4: Use the spline curve fitting method to fit the local centerline point set to obtain a smooth curve as the preliminary centerline of the tunnel;

[0091] Step 3.5: Optimize the preliminary centerline to obtain the tunnel centerline.

[0092] In a further embodiment, within each polar angle interval, points within a certain range are selected to the left and right of the polar angle, and the local midline point is calculated based on the polar radius of the selected points. A weighted average is then used to calculate the polar radius of the local midline point, including:

[0093] Within each polar angle interval, extend Δθ to the left and right of the polar angle as the center. ext Choose Δθ ext For points within the extended range, the extreme radius of the local centerline point is calculated using a weighted average:

[0094] Let Δθ ext Any point P within the extended range i Its polar radius is ρ i Its weight ω i According to point P i The distance to the center ray of the polar angle is used to determine:

[0095]

[0096] In the formula, d i Point P i The distance to the ray at the center of the polar angle, ∈ represents a minimum constant used to prevent the denominator from being 0;

[0097] The extreme radius of the local midline point The calculation formula is:

[0098]

[0099] In a further embodiment, combined with Figure 3 As shown, the initial centerline is optimized to obtain the tunnel centerline, including:

[0100] For each point in the point cloud data within the defined boundary range around the initial centerline, the distance deviation between each point and the initial centerline is calculated;

[0101] Points whose distance deviation exceeds the set distance threshold are marked as deviation points;

[0102] The point cloud data of the initial central axis and its surrounding area are divided into local regions according to equal length intervals.

[0103] Count the number of marked deviation points in each local area, and mark the local areas with more than a certain number of deviation points as problem segments;

[0104] For the central axis segment of the marked problem segment, take the two endpoints of the problem segment as the center, extend a certain distance to both sides, and select point cloud data within a certain width in the direction perpendicular to the central axis as candidate point cloud for recalculating the local central axis;

[0105] Within the selected candidate point cloud range, the local centerline segment is recalculated using a weighted average and replaced with the centerline segment of the problem segment to update the initial centerline.

[0106] Update the centerline segments of all problematic sections and smoothly replace the endpoints of the locations to obtain the tunnel centerline.

[0107] In a further embodiment, combined with Figure 4 As shown, in step 6, adaptive shape analysis and adaptive shape fitting are performed on the cross-sectional point cloud data to obtain the shape and size parameters of the fitted tunnel cross-section, including:

[0108] Step 6.1: Fit a circular cross-section using the weighted least squares method based on the cross-sectional point cloud data, and determine the radius and coordinates of the circle through iterative optimization;

[0109] Step 6.2: Estimate the directions and initial lengths of the major and minor axes of the ellipse based on the covariance matrix of the cross-sectional point cloud data, and determine the major and minor axes of the ellipse through iterative optimization.

[0110] In a further embodiment, combined with Figure 4 As shown, in step 7, based on the shape and size parameters of the tunnel cross-section obtained from scans at different times at the same location, the deformation of the tunnel cross-section is evaluated to determine the magnitude and direction of the deformation change, including:

[0111] For tunnel cross-sections at the same location obtained from scans at different times, the same method was used to extract the cross-section parameters: for circular cross-sections, the center coordinates and radius were recorded at different times; for elliptical cross-sections, the major axis length, minor axis length, center coordinates, and the angle between the major axis and the coordinate axis were recorded at different times.

[0112] For circular cross-sections, the differences in radius and circular coordinates at different times are compared with a preset circular cross-section difference threshold. If the difference exceeds the circular cross-section difference threshold, it is determined that the circular tunnel cross-section has global deformation.

[0113] For elliptical cross-sections, the differences in the length of the major or minor axis and the differences in the included angle of the major axis at different times are compared with the preset elliptical cross-section difference threshold. If the difference exceeds the elliptical cross-section difference threshold, it is determined that the elliptical tunnel cross-section has global deformation.

[0114] In a further embodiment, combined with Figure 5 As shown, in step 8, the tunnel cross-section is locally divided, and local deformation is monitored based on the changes in the divided sub-regions, including:

[0115] For a circular cross-section, the circular cross-section is divided into several sector regions with equal angles. For each sector region, the local parameters of the sector are calculated: point cloud density and average radius. Then, the changes in point cloud density and average radius of the same sector region at different times are compared. If the rate of change of point cloud density exceeds the set density threshold or the change in average radius exceeds the radius deviation threshold, it is determined that there is local deformation in the sector region.

[0116] For each circular cross-section, the number of points within the sector is counted as the point cloud density, and the average distance from the point to the center of the circle is calculated as the average radius.

[0117] In a further embodiment, for an elliptical cross-section, the elliptical cross-section is divided into multiple rectangular sub-regions along the major and minor axes. Within each rectangular sub-region, the distribution characteristics of the point cloud are calculated. Then, the changes in the distribution characteristics of the point cloud in the same rectangular sub-region at different times are compared. If the distribution characteristics of the point cloud exceed a set deviation threshold, it is determined that there is local deformation in the rectangular sub-region.

[0118] For each elliptical cross section, the standard deviation of the coordinates of the points in the major and minor axes is calculated as the distribution characteristics of the point cloud, representing the degree of dispersion in the major and minor axes. By comparing the changes in the standard deviations of the major and minor axes in the same rectangular sub-region at different times, if the change in the standard deviation of the major axis or the change in the standard deviation of the minor axis exceeds the set deviation threshold, it is determined that there is local deformation in the rectangular sub-region.

[0119] {Example 2}

[0120] In this embodiment, the tunnel cross-section identification and deformation detection method based on laser scanning point cloud data of the foregoing embodiments of the present invention will be further illustrated with specific examples.

[0121] (I) Raw Data Collection

[0122] In this embodiment, a 3D laser scanner, such as the high-precision FARO Focus 3D series, is selected. The scanning accuracy is ±2mm and the angular resolution is 0.01°. The scanning range and resolution are reasonably set according to the tunnel length and width to ensure that sufficiently detailed point cloud data can be obtained.

[0123] A scanning station is set up at regular intervals (e.g., 20-30 meters) inside the tunnel, and each station performs a 360° omnidirectional scan. During scanning, ensure the 3D laser scanner is placed horizontally and secured with a tripod for stability. Record the coordinate information of each station, including the X, Y, and Z axes, for subsequent data registration.

[0124] The scanned point cloud data is transmitted and stored in the computer system in LAS format, and classified and stored according to information such as date and site to form a point cloud database.

[0125] (II) Point Cloud Data Registration

[0126] In this embodiment, a coarse registration + fine registration approach is adopted, which achieves high-precision registration based on a combination of feature points and an improved ICP algorithm.

[0127] As an example, the coarse registration process is as follows:

[0128] In the point cloud data of adjacent sites, reliable feature points are selected using manual or automatic feature extraction algorithms (such as those based on curvature features). Taking n selected feature points as an example, for feature point i, its coordinates in site 1 are (x... 1i y 1i , z 1i The coordinates of ) in station 2 are (x 2i y 2i , z 2i ).

[0129] The initial rigid transformation matrix R0 and translation vector T0 from station 1 to station 2 are solved using the Singular Value Decomposition (SVD) method. The calculation process is as follows:

[0130] First, calculate the centroid of the feature point:

[0131]

[0132] Then calculate the relative coordinates:

[0133]

[0134] Construct the covariance matrix H:

[0135]

[0136] Perform SVD decomposition on the covariance matrix H: H = U∑V T Then the rotation matrix R0 = VU T Translation vector

[0137] The point cloud data of site 1 is transformed by rotation matrix and translation vector to complete coarse registration.

[0138] As an example, the fine registration process is as follows:

[0139] Based on the coarse registration, the ICP algorithm is used to further optimize the registration results. For point P(x, y, z) in station 1 after coarse registration, its nearest point Q(x′, y′, z′) is found in the point cloud of station 2;

[0140] Define the error function:

[0141]

[0142] Where m is the number of points involved in the registration.

[0143] A point-to-point distance metric is used, and the Levenberg-Marquardt algorithm is employed to optimize the solution of transformation matrices R and T, minimizing the error function. During the iteration process, a termination condition is set, such as the change in the transformation matrix between two adjacent iterations being less than a certain threshold or the number of iterations exceeding a certain value (e.g., 50 times), at which point the iteration ends.

[0144] (III) Point Cloud Data Denoising

[0145] In this step, the tunnel point cloud data is denoised and spatially simplified to obtain voxelized point cloud data.

[0146] First, statistical filtering and radius filtering are performed on the tunnel point cloud data to remove the marked noise points.

[0147] As an example, the statistical filtering process is as follows:

[0148] Calculate the average distance d between each point and its neighbors. i Let the neighborhood radius be r1 (e.g., 0.2 meters). For a point P in the point cloud data, its neighborhood point set is N(P), and the average distance is calculated using the following formula:

[0149]

[0150] Then, based on the overall distribution of the point cloud data, a distance threshold D1 is set. If the average distance d of a certain point... i If the value is greater than D1, it is initially marked as a noise point.

[0151] As an example, the radius filtering process is as follows:

[0152] Based on statistical filtering, radius filtering is performed: For each point, a sphere with radius r² (e.g., 0.1 meters) is defined with that point as the center, and the number of points within the sphere is counted. If the count is less than a set threshold N (N can be determined based on the tunnel point cloud density, e.g., N=5), then the point is marked as a noise point.

[0153] Then, by combining the results of the two filtering operations, the marked noise points are removed.

[0154] Finally, based on the noise-removed point cloud data, the point cloud data space is divided into voxel grids. For each voxel grid, only one representative point is retained. The point closest to the center of the voxel grid is used as the representative point to obtain voxelized point cloud data.

[0155] (iv) Extraction of the tunnel centerline

[0156] Combination Figure 3 As shown, local centerline points of the tunnel are extracted based on polar coordinate transformation, and after global centerline fitting and optimization, the tunnel centerline is obtained, including:

[0157] Step 3.1: Transform the voxelized point cloud data from the Cartesian coordinate system to the polar coordinate system, and determine the polar radius and polar angle of any point in the point cloud data;

[0158] Step 3.2: Within each polar angle interval, select points within a certain range to the left and right of the polar angle and calculate the local centerline point based on the polar radius of the selected points. Use a weighted average to calculate the polar radius of the local centerline point.

[0159] Step 3.3: Convert the local midline points calculated for each polar angle interval back to the Cartesian coordinate system to obtain a set of discrete local midline points;

[0160] Step 3.4: Use the spline curve fitting method to fit the local centerline point set to obtain a smooth curve as the preliminary centerline of the tunnel;

[0161] Step 3.5: Optimize the preliminary centerline to obtain the tunnel centerline.

[0162] In step 3.1, for any point P in the voxelized point cloud data, P = (x, y, z), its polar coordinates are converted to:

[0163] Polar diameter:

[0164] Polar angle:

[0165] Then, to reduce the discreteness of the data, the polar coordinate data is discretized. The polar angle range [0, 2π] is divided into multiple polar angle intervals according to a certain angular interval (such as 0.5°).

[0166] Further, in step 3.2, within each polar angle interval, points within a certain range are selected to the left and right of the polar angle, and the local midline point is calculated based on the polar radius of the selected points. A weighted average is used to calculate the polar radius of the local midline point, including:

[0167] Within each polar angle interval, extend Δθ to the left and right of the polar angle as the center. ext (In this example, the value is 2°), choose Δθ ext For points within the extended range, the extreme radius of the local centerline point is calculated using a weighted average:

[0168] Let Δθ ext Any point P within the extended range i Its polar radius is ρ i Its weight ω i According to point P i The distance to the center ray of the polar angle is used to determine:

[0169]

[0170] In the formula, d i Point P i The distance to the ray at the center of the polar angle, ∈ represents a minimum constant used to prevent the denominator from being 0;

[0171] The extreme radius of the local midline point The calculation formula is:

[0172]

[0173] In the example of this invention, in step 3.4, a cubic spline curve is fitted to obtain a smooth curve as the initial centerline of the tunnel.

[0174] Combination Figure 3 As shown, in step 3.5, the preliminary centerline is optimized to obtain the tunnel centerline, including:

[0175] For each point in the point cloud data within a defined boundary range around the initial centerline, the distance deviation between each point and the initial centerline is calculated; for example, since the initial centerline consists of a series of discrete points, Euclidean distance is used to calculate the distance from any point to the nearest discrete point on the centerline as the distance deviation.

[0176] Points whose distance deviation exceeds a set distance threshold (e.g., 0.1m) are marked as deviation points;

[0177] The point cloud data of the initial central axis and its surrounding area are divided into local regions according to equal length intervals.

[0178] Count the number of deviation points marked in each local area, and mark local areas with more than a certain percentage (e.g., a percentage of 20%) as problem segments;

[0179] For the central axis segment of the marked problem segment, take the two endpoints of the problem segment as the center, extend a certain distance (e.g., 2m) to both sides, and select point cloud data within a certain width (e.g., 1m) in the direction perpendicular to the central axis as candidate point cloud for recalculating the local central axis;

[0180] Within the selected candidate point cloud range, the local centerline segment is recalculated using a weighted average and replaced with the centerline segment of the problem segment to update the initial centerline.

[0181] Update the centerline segments of all problematic sections and smoothly replace the endpoints of the locations to obtain the tunnel centerline.

[0182] This allows for the optimization of the initial centerline, enabling it to better reflect the actual shape of the tunnel and providing an accurate benchmark for subsequent tunnel cross-section identification and deformation detection.

[0183] (V) Point Cloud Segmentation and Sort

[0184] Points located within a certain distance (e.g., ±0.2 meters) on both sides of each intercepting plane in the point cloud data are segmented and used as the original cross-sectional point cloud data for that cross section.

[0185] The original cross-sectional point cloud data is further processed to remove isolated points that are too far from the plane (determined by setting a comparison distance, such as 0.15 meters). These isolated points may be noise or incorrectly segmented points. The remaining point cloud data is then sorted and numbered.

[0186] (vi) Adaptive shape fitting of cross-sectional point cloud data

[0187] In this step, adaptive shape fitting is performed on the cross-sectional point cloud data to obtain the shape and size parameters of the fitted tunnel cross-section.

[0188] As an optional embodiment, fitting is performed using typical elliptical and circular tunnel designs as examples.

[0189] Combination Figure 4 As shown, the process of adaptive shape fitting for cross-sectional point cloud data includes:

[0190] Step 6.1: Fit a circular cross-section using the weighted least squares method based on the cross-sectional point cloud data, and determine the radius and coordinates of the circle through iterative optimization;

[0191] Step 6.2: Estimate the directions and initial lengths of the major and minor axes of the ellipse based on the covariance matrix of the cross-sectional point cloud data, and determine the major and minor axes of the ellipse through iterative optimization.

[0192] Further, in step 6.1, a circular cross-section is fitted using the weighted least squares method based on the cross-sectional point cloud data, and the radius and coordinates of the circle are determined through iterative optimization, including:

[0193] Obtain the preprocessed and segmented cross-sectional point cloud data, denoted as set S;

[0194] Initial center estimation: Calculate the centroid coordinates of the cross-sectional point cloud data. As the initial estimate of the circle center, the calculation formula is:

[0195]

[0196] Distance Calculation and Weight Assignment: For each point in set S, calculate its distance d to the initial center of the circle. i The calculation formula is:

[0197]

[0198] According to distance d i A Gaussian weighting function is used to assign weights to each point, that is:

[0199]

[0200] Here, σ is a weighting parameter, which can be adjusted according to the distribution of the point cloud data and the required fitting accuracy. Generally speaking, the larger σ is, the greater the weight of distant points; the smaller σ is, the greater the weight of nearby points.

[0201] Weighted Least Squares Fitting: Fitting the equation of a circle using the weighted least squares method. Let the equation of the circle be:

[0202] (x-α) 2 +(yb) 2 =r 2 ;

[0203] Define the error function as:

[0204]

[0205] The Levenberg-Marquardt algorithm is used to solve for the center coordinates and radius by minimizing the error function;

[0206] Iterative optimization: Finally, using the center coordinates and radius obtained by the Levenberg-Marquardt algorithm as new initial values, the weight allocation and weighted least squares fitting are repeated until the change in the center coordinates and radius is less than a set threshold (e.g., 10). -6 Alternatively, the maximum number of iterations can be reached (e.g., 50 times), at which point the center coordinates and radius obtained are the final fitting results.

[0207] Further, in step 6.2, a circular cross-section is fitted using the weighted least squares method based on the cross-sectional point cloud data, and the radius and coordinates of the circle are determined through iterative optimization, including:

[0208] Initial ellipse parameter estimation: Calculate the centroid coordinates of the point cloud data The initial estimated value for the center of the elliptical cross-section is calculated in the same way as the centroid coordinate calculation process in the circular cross-section described above.

[0209] Major and minor axis orientation estimation: The orientations of the major and minor axes are estimated by calculating the covariance matrix of the point cloud data, specifically as follows:

[0210] Calculate the covariance matrix C:

[0211] Eigenvalue decomposition of the covariance matrix C yields two eigenvalues ​​λ1 and λ2 and their corresponding eigenvectors. Then the direction of the major axis and the eigenvector Parallel, minor axis direction and eigenvector parallel;

[0212] Major and minor axis length estimation: The lengths of the major and minor axes are estimated based on the projection lengths of the point cloud data in the major and minor axis directions. The point cloud data is projected onto the major and minor axis directions, and the range of the projection points is calculated respectively to obtain the initial length estimates l0 and s0 of the major and minor axes.

[0213] Transform the point cloud data from the original coordinate system to a new coordinate system with the center of the ellipse as the origin and the major and minor axes as the coordinate axes: For point P i (x i y i ), its coordinates in the new coordinate system (x′) i y′ i )for:

[0214]

[0215] In the new coordinate system, for each point P′ i (x′ i y′ i ), calculate its distance to the center of the ellipse, the formula is:

[0216]

[0217] Similarly, according to the distance d′ i A Gaussian weighting function is used to assign a weight w to each point. i :

[0218]

[0219] Then, the ellipse equation is fitted, and the equation of the ellipse in the new coordinate system is:

[0220]

[0221] Define the error function E(a,b) as follows:

[0222]

[0223] The major semi-axis a and minor semi-axis b of the ellipse are solved by minimizing the error function using a nonlinear optimization algorithm.

[0224] Finally, the fitted ellipse parameters are transformed back from the new coordinate system to the original coordinate system, yielding the center coordinates, major and minor axis lengths, and angles between the major axis and the coordinate axes of the elliptical cross-section in the original coordinate system. These parameters are then used as new initial values, and the coordinate transformation-weight allocation-ellipse fitting process is repeated until the parameter changes are less than a set threshold, resulting in the final elliptical cross-section fitting result.

[0225] (vii) Global deformation judgment of cross section

[0226] Combination Figure 4 As shown, in step 7, based on the shape and size parameters of the tunnel cross-section obtained from scans at different times at the same location, the deformation of the tunnel cross-section is evaluated to determine the magnitude and direction of the deformation change, including:

[0227] For tunnel cross-sections at the same location obtained from scans at different times, the same method was used to extract the cross-section parameters: for circular cross-sections, the center coordinates and radius were recorded at different times; for elliptical cross-sections, the major axis length, minor axis length, center coordinates, and the angle between the major axis and the coordinate axis were recorded at different times.

[0228] For circular cross-sections, the differences in radius and circular coordinates at different times are compared with a preset circular cross-section difference threshold. If the difference exceeds the circular cross-section difference threshold, it is determined that the circular tunnel cross-section has global deformation.

[0229] For elliptical cross-sections, the differences in the length of the major or minor axis and the differences in the included angle of the major axis at different times are compared with the preset elliptical cross-section difference threshold. If the difference exceeds the elliptical cross-section difference threshold, it is determined that the elliptical tunnel cross-section has global deformation.

[0230] (viii) Global deformation judgment of cross section

[0231] Combination Figure 5 As shown, in step 8, the tunnel cross-section is locally divided, and local deformation is monitored based on the changes in the divided sub-regions, including:

[0232] For a circular cross-section, the circular cross-section is divided into several sector regions with equal angles. For example, according to the central angle of each sector being 30°, it is divided into 12 sector regions. For each sector region, the local parameters of the sector are calculated: point cloud density and average radius. Then, the changes in point cloud density and average radius of the same sector region at different times are compared. If the rate of change of point cloud density exceeds the set density threshold or the change in average radius exceeds the radius deviation threshold, it is determined that there is local deformation in the sector region.

[0233] For each circular cross-section, the number of points within the sector is counted as the point cloud density, and the average distance from the point to the center of the circle is calculated as the average radius.

[0234] In a further embodiment, for an elliptical cross-section, the elliptical cross-section is divided into multiple rectangular sub-regions along the major and minor axes. Within each rectangular sub-region, the distribution characteristics of the point cloud are calculated. Then, the changes in the distribution characteristics of the point cloud in the same rectangular sub-region at different times are compared. If the distribution characteristics of the point cloud exceed a set deviation threshold, it is determined that there is local deformation in the rectangular sub-region.

[0235] For each elliptical cross section, the standard deviation of the coordinates of the points in the major and minor axes is calculated as the distribution characteristics of the point cloud, representing the degree of dispersion in the major and minor axes. By comparing the changes in the standard deviations of the major and minor axes in the same rectangular sub-region at different times, if the change in the standard deviation of the major axis or the change in the standard deviation of the minor axis exceeds the set deviation threshold, it is determined that there is local deformation in the rectangular sub-region.

[0236] Based on this, the number and distribution of areas exhibiting localized deformation can be further analyzed. If the localized deformation areas are concentrated in a certain direction or in a continuous area, the cause needs further analysis, such as uneven local stress or changes in geological conditions. Simultaneously, localized deformation information should be combined with global deformation information to comprehensively assess the deformation state of the tunnel cross-section. For example, if the global parameters change relatively little, but multiple areas exhibit significant deformation, it indicates early signs of damage to the tunnel structure in a localized area, requiring close monitoring.

[0237] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for tunnel cross-section identification and deformation detection based on laser scanning point cloud data, characterized in that, include: Step 1: Obtain point cloud data from any two adjacent stations within the tunnel. Complete the registration of point cloud data from all laser scanning stations through point cloud registration to obtain tunnel point cloud data. Step 2: Denoise the entire tunnel point cloud data and simplify the point cloud data space to obtain voxelized point cloud data; Step 3: Extract the local centerline points of the tunnel based on polar coordinate transformation, and obtain the tunnel centerline after global centerline fitting and optimization; Step 4: Based on the obtained tunnel centerline, determine the plane perpendicular to the centerline as the cross-section cutting plane according to the spacing, and calculate the equation of the cross-section cutting plane. Step 5: Segment the points located within a certain distance range on both sides of each cross-sectional cutting plane in the point cloud data, and use them as the original point cloud data of that cross-sectional cutting plane. After removing isolated points, sort and number them to obtain the cross-sectional point cloud data. Step 6: Perform adaptive shape analysis on the cross-sectional point cloud data and adaptive shape fitting to obtain the shape and size parameters of the fitted tunnel cross-section. Step 7: Based on the shape and size parameters of the tunnel cross-section obtained from scanning at the same location at different times, conduct a deformation assessment of the tunnel cross-section to determine the magnitude and direction of the deformation change; Step 8: Divide the tunnel cross-section locally and monitor local deformation based on the changes in the divided sub-regions; In step 3, the local centerline points of the tunnel are extracted based on polar coordinate transformation, and the tunnel centerline is obtained after global centerline fitting and optimization, including: Step 3.1: Transform the voxelized point cloud data from the Cartesian coordinate system to the polar coordinate system, and determine the polar radius and polar angle of any point in the point cloud data; Step 3.2: Within each polar angle interval, select points within a certain range to the left and right of the polar angle and calculate the local centerline point based on the polar radius of the selected points. Use a weighted average to calculate the polar radius of the local centerline point. Step 3.3: Convert the local midline points calculated for each polar angle interval back to the Cartesian coordinate system to obtain a set of discrete local midline points; Step 3.4: Use the spline curve fitting method to fit the local centerline point set to obtain a smooth curve as the preliminary centerline of the tunnel; Step 3.5: Optimize the preliminary centerline to obtain the tunnel centerline; Specifically, within each polar angle interval, points within a certain range are selected to the left and right of the polar angle, and the local midline point is calculated based on the polar radius of the selected points. A weighted average is then used to calculate the polar radius of the local midline point, including: Within each polar angle interval, the radius extends Δ to the left and right of the polar angle. θ ext Choose in Δ θ ext For points within the extended range, the extreme radius of the local centerline point is calculated using a weighted average: Let Δ θ ext any point within the extended range P i Its polar diameter is ρ i Its weight ω i According to the point P i The distance to the center ray of the polar angle is used to determine: ； In the formula, d i Point P i The distance to the center ray of the polar angle; This represents a minimum constant used to prevent the denominator from being zero; The formula for calculating the extreme radius of the local centerline point is: 。 2. The tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to claim 1, characterized in that, In step 1, point cloud registration is performed to register the point cloud data of all laser scanning stations, thereby obtaining tunnel point cloud data, including: Step 1.1: Based on the feature points, solve for the rigid transformation rotation matrix and translation vector between stations, transform the point cloud data, and complete the coarse registration; Step 1.2: Based on the coarse registration, optimization is performed using the ICP algorithm, adopting a point-to-point distance metric, and the LM algorithm is used to iteratively optimize and solve the transformation matrix to minimize the error function.

3. The tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to claim 1, characterized in that, In step 2, the entire tunnel point cloud data is denoised and spatially simplified to obtain voxelized point cloud data, including: Step 2.1: Perform statistical filtering and radius filtering on the tunnel point cloud data to remove marked noise points; Step 2.2: Divide the point cloud data space into voxel grids. For each voxel grid, retain only one representative point. Use the point closest to the center of the voxel grid as the representative point to obtain voxelized point cloud data.

4. The tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to claim 1, characterized in that, The optimization of the preliminary centerline to obtain the tunnel centerline includes: For each point in the point cloud data within the defined boundary range around the initial centerline, the distance deviation between each point and the initial centerline is calculated; Points whose distance deviation exceeds the set distance threshold are marked as deviation points; The point cloud data of the initial central axis and its surrounding area are divided into local regions according to equal length intervals. Count the number of marked deviation points in each local area, and mark the local areas with more than a certain number of deviation points as problem segments; For the central axis segment of the marked problem segment, take the two endpoints of the problem segment as the center, extend a certain distance to both sides, and select point cloud data within a certain width in the direction perpendicular to the central axis as candidate point cloud for recalculating the local central axis; Within the selected candidate point cloud range, the local centerline segment is recalculated using a weighted average and replaced with the centerline segment of the problem segment to update the initial centerline. Update the centerline segments of all problematic sections and smoothly replace the endpoints of the locations to obtain the tunnel centerline.

5. The tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to claim 1, characterized in that, In step 6, adaptive shape analysis and adaptive shape fitting are performed on the cross-sectional point cloud data to obtain the shape and size parameters of the fitted tunnel cross-section, including: Step 6.1: Fit a circular cross-section using the weighted least squares method based on the cross-sectional point cloud data, and determine the radius and coordinates of the circle through iterative optimization; Step 6.2: Estimate the directions and initial lengths of the major and minor axes of the ellipse based on the covariance matrix of the cross-sectional point cloud data, and determine the major and minor axes of the ellipse through iterative optimization.

6. The tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to claim 1, characterized in that, In step 7, based on the shape and size parameters of the tunnel cross-section obtained from scans at the same location at different times, a deformation assessment of the tunnel cross-section is performed to determine the magnitude and direction of the deformation change, including: For tunnel cross-sections at the same location obtained from scans at different times, the same method was used to extract the cross-section parameters: for circular cross-sections, the center coordinates and radius were recorded at different times; for elliptical cross-sections, the major axis length, minor axis length, center coordinates, and the angle between the major axis and the coordinate axis were recorded at different times. For circular cross-sections, the differences in radius and circular coordinates at different times are compared with a preset circular cross-section difference threshold. If the difference exceeds the circular cross-section difference threshold, it is determined that the circular tunnel cross-section has global deformation. For elliptical cross-sections, the differences in the length of the major or minor axis and the differences in the included angle of the major axis at different times are compared with the preset elliptical cross-section difference threshold. If the difference exceeds the elliptical cross-section difference threshold, it is determined that the elliptical tunnel cross-section has global deformation.

7. The tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to claim 1, characterized in that, In step 8, the tunnel cross-section is locally divided, and local deformation is monitored based on changes in the divided sub-regions, including: For a circular cross-section, the circular cross-section is divided into several sector regions with equal angles. For each sector region, the local parameters of the sector are calculated: point cloud density and average radius. Then, the changes in point cloud density and average radius of the same sector region at different times are compared. If the rate of change of point cloud density exceeds the set density threshold or the change in average radius exceeds the radius deviation threshold, it is determined that there is local deformation in the sector region. For each circular cross-section, the number of points within the sector is counted as the point cloud density, and the average distance from the point to the center of the circle is calculated as the average radius.

8. The tunnel cross-section identification and deformation detection method based on laser scanning point cloud data according to claim 1, characterized in that, For an elliptical cross section, the elliptical cross section is divided into multiple rectangular sub-regions along the major and minor axes. Within each rectangular sub-region, the distribution characteristics of the point cloud are calculated. Then, the changes in the distribution characteristics of the point cloud in the same rectangular sub-region at different times are compared. If the distribution characteristics of the point cloud exceed the set deviation threshold, it is determined that there is local deformation in the rectangular sub-region. For each elliptical cross section, the standard deviation of the coordinates of the points in the major and minor axes is calculated as the distribution characteristics of the point cloud, representing the degree of dispersion in the major and minor axes. By comparing the changes in the standard deviations of the major and minor axes in the same rectangular sub-region at different times, if the change in the standard deviation of the major axis or the change in the standard deviation of the minor axis exceeds the set deviation threshold, it is determined that there is local deformation in the rectangular sub-region.

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