A concrete defect volume quantification method based on improved point cloud projection

Abstract

The present invention discloses a method for quantifying concrete defect volumes using improved point cloud projection, relating to the technical fields of three-dimensional point cloud data processing and civil engineering nondestructive testing. The method uses point cloud data acquisition equipment to scan the surface area of ​​dam concrete, preprocesses the data to filter out noise and debris, and obtains better point cloud data of the dam concrete. The curvature and geometric distance characteristics of the point cloud data are weighted to extract the concrete defect area. The ray parity method is used to determine whether there is point cloud occlusion at the points in the concrete defect area. The volume validity method is used to determine the validity of the projected volume of the point cloud of the occluded portion, thereby improving the accuracy and reliability of defect volume quantification and providing a scientific basis for the safety assessment, repair, and reinforcement of concrete dams.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional point cloud data processing and civil engineering non-destructive testing, and in particular to a concrete defect volume quantification method using improved point cloud projection. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] As key infrastructure in water conservancy projects, dams play a vital role in flood control, irrigation, power generation, water supply, and other fields. However, due to the long-term combined influence of complex external environmental factors and internal material properties, defects in dam concrete are inevitable. These defects can significantly reduce the structural strength and durability of the dam, weaken its impermeability, and in severe cases, may even cause the dam to fail, leading to catastrophic consequences. Therefore, accurately quantifying the volume of dam concrete defects is of great practical significance for assessing the safety status of dams, formulating scientific and reasonable maintenance and reinforcement plans, and ensuring the long-term stable operation of dams.

[0004] Traditional methods for detecting and quantifying defects in concrete dams rely primarily on manual on-site inspections and limited local measurements. These methods are not only inefficient and labor-intensive, but also subject to significant human influence, making it difficult to comprehensively and accurately obtain precise information on defect volumes. With the continuous advancement of science and technology, 3D laser scanning technology has been gradually applied to concrete dam inspections, providing an effective means of obtaining detailed information on dam surfaces. These technologies enable the rapid and accurate acquisition of 3D point cloud data of dams, providing the data foundation for quantifying defect volumes.

[0005] While volume calculation methods based on point cloud data have achieved some success, their practical application in quantifying the volume of concrete dam defects still faces numerous challenges. For example, dam point cloud data is characterized by complex defect morphology and occlusion. Accurately extracting point cloud information from massive amounts of point cloud data and calculating its volume using appropriate methods are pressing challenges. Furthermore, due to the irregularity and diversity of dam defects, existing volume calculation methods may not be fully applicable to all defect types, necessitating further exploration and improvement. Summary of the Invention

[0006] The purpose of the present invention is to address the problems existing in the prior art and provide a concrete defect volume quantification method based on an improved point cloud projection. The method uses a point cloud data acquisition device to scan the surface area of ​​the dam concrete, preprocesses the data to filter out noise and debris, and obtains better dam concrete point cloud data. The curvature and geometric distance characteristics of the point cloud data are weighted to extract the concrete defect area. The ray parity method is used to determine whether there is point cloud occlusion for the points in the concrete defect area. The volume validity method is used to determine the validity of the projected volume of the point cloud of the occluded part, thereby improving the accuracy and reliability of the defect volume quantification and providing a scientific basis for the safety assessment and maintenance and reinforcement of concrete dams.

[0007] The technical solutions of the present invention are as follows:

[0008] A concrete defect volume quantification method based on improved point cloud projection includes:

[0009] Step S1: Scanning the surface area of ​​the dam concrete using a point cloud data acquisition device, and pre-processing the scanned point cloud data;

[0010] Step S2: weighting the curvature and geometric distance characteristics of the pre-processed point cloud data to extract the concrete defect area and obtain point cloud data of the concrete defect area;

[0011] Step S3: For the point cloud data of the concrete defect area, the ray parity method is used to determine whether there is point cloud occlusion, and the volume validity of the projected volume is used to determine the validity of the point cloud of the occluded part.

[0012] Furthermore, the preprocessing includes:

[0013] Step S11: Use the point cloud processing software Cloud Compare to evaluate noise characteristics;

[0014] Step S12: removing outliers;

[0015] Step S13: Smoothing the point cloud data.

[0016] Furthermore, the step S11 includes:

[0017] Use Cloud Compare, a point cloud processing software, to import point cloud data. Use visualization tools to examine the overall shape of the point cloud, identify the distribution and scale of outliers and small areas of abnormal concentration, understand the basic properties of the dam concrete point cloud data, and evaluate noise characteristics.

[0018] The step S12 includes:

[0019] Statistical filtering is used to select several neighborhood points based on the density and distribution of the point cloud. The average and standard deviation of the distance from each point to the neighboring points are calculated. The average plus N times the standard deviation is used as the threshold, and points with distances exceeding the threshold are eliminated.

[0020] The step S13 includes:

[0021] By setting the spatial domain filter radius and the value domain filter standard deviation, bilateral filtering is used to smooth the point cloud while retaining edge details.

[0022] Furthermore, to remove noise from small areas with concentrated abnormalities, the density-based DBSCAN clustering algorithm is used, and the neighborhood radius and minimum number of points parameters are set for clustering. Abnormal clusters are identified and deleted based on density characteristics.

[0023] Furthermore, the step S2 includes:

[0024] Step S21: Statistically filter the pre-processed point cloud data to remove outliers that may not have been removed in step S1;

[0025] Step S22: Calculating point cloud features of local curvature and relative distance;

[0026] Step S23: performing Gaussian weighted summation on the calculated point cloud features;

[0027] Step S24: setting a threshold to extract point cloud data of the dam concrete defect area.

[0028] Furthermore, the step S22 includes:

[0029] The local area size of the point is set, and the quadratic surface is fitted by the least squares method, and the average curvature is obtained through the second fundamental form of the surface; then the principal component analysis method is used on the point cloud data of the dam concrete to obtain a relatively stable plane fitting result, and the relative distance of each point to the fitting plane is calculated.

[0030] Furthermore, the step S23 includes:

[0031] Step S231: performing Gaussian weight calculation on the local curvature and relative distance respectively;

[0032] Step S232: Based on the weights calculated in step S231, calculate the weighted relative distance from the point to the plane and the weighted local curvature ;

[0033] Step S233: Relative distance and local curvature Comprehensively weighted together to form a new comprehensive eigenvalue .

[0034] Furthermore, the step S3 includes:

[0035] The point cloud data of the concrete defect area is rotated and translated according to the fitting plane; the volume of the defect area is calculated according to the point cloud projection method.

[0036] Furthermore, the step S3 specifically includes the following steps:

[0037] Step S31: Data parameter preparation; take the normal vector of the fitting plane as the projection direction, and rotate and translate the point cloud of the concrete defect area to the XOY plane based on the plane equation, and set all defect point cloud parameters ;

[0038] Step S32: Slice region fitting; point The surrounding area is sliced, and the slicing direction is the projection direction; the quadratic surface is fitted to the slice area, and the point Generate rays along the projection direction;

[0039] Step S33: The intersection of the ray generated by the point along the projection direction and the fitted quadratic surface can be obtained by the ray parity method, thereby constructing the volume validity;

[0040] Step S34: Statistical point effective height; based on volume validity, if point There is an intersection with the quadratic surface fitted in the slice area, and the height of the first intersection is recorded as ,point The original height is , and set If there is no intersection point, the first intersection point is assumed to be the best fit plane. parameter ;

[0041] Step S35: triangulation projection; triangulate the entire defect area and then project it to the intersection height recorded at each point to form an irregular triangular prism;

[0042] Step S36: Simplify the triangular prism structure; divide the projected triangular prism into The irregular triangular prism and the The irregular triangular prism contains The irregular triangular prism is simplified to a combination of pyramid + prism + pyramid; only The irregular triangular prism is simplified to a combination of pyramid + prism;

[0043] Step S37: Perform volume calculation based on the combination formed in step S36.

[0044] Furthermore, the step S37 includes:

[0045] Calculate the volume of each pyramid or prism in the combination separately and add them together to get the total volume.

[0046] Compared with the existing technology, the beneficial effects of the present invention are:

[0047] The present invention can improve the accuracy and reliability of defect volume quantification and provide a scientific basis for safety assessment and repair and reinforcement of concrete dams. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A block diagram of a concrete defect volume quantification method based on improved point cloud projection;

[0049] Figure 2 Schematic diagram of dam concrete point cloud denoising;

[0050] Figure 3 Extract flow charts for defective areas;

[0051] Figure 4 Schematic diagram of point cloud extraction of dam concrete defects;

[0052] Figure 5 Calculate volume for traditional point cloud projection method;

[0053] Figure 6 To improve the point cloud projection method. DETAILED DESCRIPTION

[0054] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0055] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0056] Example 1

[0057] See also Figure 1 , a concrete defect volume quantification method based on improved point cloud projection, comprising:

[0058] Step S1: Scanning the surface area of ​​the dam concrete using a point cloud data acquisition device, and pre-processing the scanned point cloud data;

[0059] Step S2: weighting the curvature and geometric distance characteristics of the pre-processed point cloud data to extract the concrete defect area and obtain point cloud data of the concrete defect area;

[0060] Step S3: For the point cloud data of the concrete defect area, the ray parity method is used to determine whether there is point cloud occlusion. For the point cloud of the occluded part, the volume validity is used to judge the validity of the projected volume, thereby improving the accuracy and reliability of defect volume quantification and providing a scientific basis for the safety assessment and repair and reinforcement of concrete dams.

[0061] In this embodiment, it should be noted that 3D reconstruction technology combining binocular vision with structured light is currently one of the most effective means of acquiring point cloud data for irregular objects. This invention utilizes a high-precision depth camera and 3D reconstruction technology that integrates binocular vision with a structured light array, providing the data foundation for subsequent high-precision volume quantification.

[0062] In this embodiment, it should be noted that, during the re-scanning process, the scanning angle and position can be adjusted according to the scanned dam concrete area.

[0063] In this embodiment, specifically, the preprocessing includes:

[0064] Step S11: Use the point cloud processing software Cloud Compare to evaluate noise characteristics;

[0065] Step S12: removing outliers;

[0066] Step S13: Smoothing the point cloud data.

[0067] In this embodiment, specifically, step S11 includes:

[0068] Use Cloud Compare, a point cloud processing software, to import point cloud data. Use visualization tools to examine the overall shape of the point cloud, identify the distribution and scale of outliers and small areas of abnormal concentration, understand the basic properties of the dam concrete point cloud data, and evaluate noise characteristics.

[0069] The step S12 includes:

[0070] Statistical filtering is used to select several neighborhood points based on the density and distribution of the point cloud. The average and standard deviation of the distance from each point to the neighboring points are calculated. The average plus N times the standard deviation is used as the threshold, and points with distances exceeding the threshold are eliminated.

[0071] The step S13 includes:

[0072] By setting the spatial domain filter radius and the value domain filter standard deviation, bilateral filtering is used to smooth the point cloud while retaining edge details.

[0073] In this embodiment, specifically, for noise removal in small abnormal concentrated areas, the density-based DBSCAN clustering algorithm is used, the neighborhood radius and the minimum number of points parameters are set for clustering, and abnormal clusters are identified and deleted based on density features.

[0074] In this example, when processing concrete defect point cloud data, a series of rigorous data processing steps are required to effectively remove noise and clutter while preserving the key features of the original data. First, the point cloud data is imported using the professional point cloud processing software Cloud Compare. Visualization tools are used to examine the overall shape of the point cloud, clarifying the distribution and scale of outliers and small areas of concentrated anomalies. This allows the basic properties of the dam concrete point cloud data to be understood, and the noise characteristics are assessed, particularly the distribution of outliers and the differences in point cloud density and shape boundaries between abnormal and normal areas.

[0075] After analyzing the noise characteristics of the point cloud data, outliers are removed. First, statistical filtering is used to select 10-30 neighborhood points based on the point cloud density and distribution. The average and standard deviation of the distance from each point to the neighborhood point are calculated. The average plus 2-3 times the standard deviation is used as the threshold, and points with a distance exceeding the threshold are deleted. For noise removal in small areas with concentrated abnormalities, the density-based DBSCAN clustering algorithm is used. The neighborhood radius and the minimum number of points are set for clustering, and abnormal clusters are identified and deleted based on density characteristics. Finally, the point cloud data is smoothed. By setting the spatial domain filter radius and the value domain filter standard deviation, bilateral filtering is used to retain edge details while smoothing the point cloud. Figure 2 ,The denoising effect of the collected point cloud data of the dam concrete area, removes the areas around the original point cloud data with different densities from the original data and outliers.

[0076] In this embodiment, please refer to Figure 3 , the step S2 comprises:

[0077] Step S21: Statistically filter the pre-processed point cloud data to remove outliers that may not have been removed in step S1; if the range of the set value is smaller than the aforementioned filtering range, the outliers that may not have been removed in the aforementioned step are removed;

[0078] Step S22: Calculating point cloud features of local curvature and relative distance;

[0079] Step S23: performing Gaussian weighted summation on the calculated point cloud features;

[0080] Step S24: Set the threshold to extract the point cloud data of the dam concrete defect area. The specific defect extraction effect is as follows: Figure 4 shown.

[0081] In this embodiment, specifically, step S22 includes:

[0082] The local area size of the point is set, and the quadratic surface is fitted by the least squares method, and the average curvature is obtained through the second fundamental form of the surface; then the principal component analysis method is used on the point cloud data of the dam concrete to obtain a relatively stable plane fitting result, and the relative distance of each point to the fitting plane is calculated.

[0083] In this embodiment, specifically, step S23 includes:

[0084] Step S231: performing Gaussian weight calculation on the local curvature and relative distance respectively;

[0085] Step S232: Based on the weights calculated in step S231, calculate the weighted relative distance from the point to the plane and the weighted local curvature ;

[0086] Step S233: Relative distance and local curvature Comprehensively weighted together to form a new comprehensive eigenvalue ; According to multiple experiments in this embodiment, the value .

[0087] In this embodiment, specifically, As the center, calculate the local neighborhood according to the Gaussian distribution midpoint Weight ; The Gaussian distribution function is expressed as follows:

[0088]

[0089] By calculation To get the weighted relative distance from the point to the plane;

[0090] calculate Get the weighted local curvature;

[0091] in Represented as all points in the domain, for point The distance to the fitting plane and the local curvature, is the standard deviation of the Gaussian distribution.

[0092] In this embodiment, specifically, step S3 includes:

[0093] The point cloud data of the concrete defect area is rotated and translated according to the fitting plane; the volume of the defect area is calculated according to the point cloud projection method.

[0094] In this embodiment, specifically, step S3 includes the following steps:

[0095] Step S31: Data parameter preparation; take the normal vector of the fitting plane as the projection direction, and rotate and translate the point cloud of the concrete defect area to the XOY plane based on the plane equation, and set all defect point cloud parameters ;

[0096] Step S32: Slice region fitting; point The surrounding area is sliced, and the slicing direction is the projection direction; the quadratic surface is fitted to the slice area, and the point Generate rays along the projection direction;

[0097] Step S33: The intersection of the ray generated by the point along the projection direction and the fitted quadratic surface can be obtained by the ray parity method, thereby constructing the volume validity;

[0098] Step S34: Statistical point effective height; based on volume validity, if point There is an intersection with the quadratic surface fitted in the slice area, and the height of the first intersection is recorded as ,point The original height is , and set If there is no intersection point, the first intersection point is assumed to be the best fit plane. parameter ;

[0099] Step S35: triangulation projection; triangulate the entire defect area and then project it to the intersection height recorded at each point to form an irregular triangular prism;

[0100] Step S36: Simplify the triangular prism structure; divide the projected triangular prism into The irregular triangular prism and the The irregular triangular prism contains The irregular triangular prism is simplified to a combination of pyramid + prism + pyramid; only The irregular triangular prism is simplified to a combination of pyramid + prism;

[0101] Step S37: Perform volume calculation based on the combination formed in step S36.

[0102] In this embodiment, specifically, step S37 includes:

[0103] Calculate the volume of each pyramid or prism in the combination separately and add them together to get the total volume.

[0104] In this embodiment, after obtaining the defect area point cloud, the defect point cloud data is rotated and translated according to the previously fitted plane. The volume of the defect area is calculated according to the point cloud projection method as follows Figure 5 As shown, assuming that the projection is onto the XOY plane, each point is vertically projected, and then we get Figure 5 (b) Figure 5 (b) The irregular triangular prisms are divided to obtain Figure 5 (c) At this time, the volumes of the irregular triangular prisms obtained by each projection are calculated and summed to obtain the concrete defect volume.

[0105] However, the defects of dam concrete are relatively complicated, and projection occlusion may occur during projection, as shown below Figure 6 As shown, the vertical projection is directly projected onto the best fit plane, and the Figure 6 It can be seen from the figure that when projecting the point cloud of a dam concrete defect, the downward projection should stop when the vertical projection first hits the inner wall of the defect. However, the traditional method continues to project downward, resulting in an overestimation of the volume when using the point cloud projection method. To address the situation where the measured volume is increased due to projection occlusion, this embodiment proposes an improved method for quantifying the volume of dam concrete defects based on the projection method. The specific steps are as follows:

[0106] 1. Data parameter preparation: According to the best fitting plane equation obtained above , the normal vector of this plane The defect point cloud is rotated and translated to the XOY plane based on the plane equation to facilitate subsequent projection calculation and set all defect point cloud parameters. ;

[0107] 2. Slice area fitting: set points Slice the area of ​​the point with an area of ​​plus or minus 1 mm (depending on the density of the point cloud, which can be reduced for high-precision instruments), and the slicing direction is the projection direction. Figure 6 As shown in (b); a quadratic surface is fitted to the slice area, and a ray is generated from the point along the projection direction;

[0108] 3. Volume validity construction: The intersection of the ray generated by the point along the projection direction and the fitted quadratic surface can be obtained by the ray parity method. Figure 6(c) It can be seen that when the projection ray intersects the quadratic surface for the first time, the volume of the irregular triangular prism generated by the projection is valid. When it exceeds the first intersection point, the volume of the irregular triangular prism generated is invalid, thus constructing the volume validity;

[0109] 4. Effective height of statistical points: Based on volume validity, if the point There is an intersection with the quadratic surface fitted in the slice area, and the height of the first intersection is recorded as ,point The original height is , and set If there is no intersection, the first intersection is assumed to be the best fit plane. Set the point parameter ;

[0110] 5. Triangulated projection: After the fourth step of the concrete defect point cloud data, each point of the concrete point cloud has its own height. , and also has the first intersection , triangulate the entire defect area and then project it. Project it to the intersection height recorded at each point to form an irregular triangular prism, such as Figure 6 (c)

[0111] 6. Simplify the triangular prism structure: Divide the projected triangular prism into The irregular triangular prism and the The irregular triangular prism contains The irregular triangular prism can be simplified to a combination of "pyramid + prism + pyramid", such as Figure 6 (d), only The irregular triangular prism is simplified to "pyramid + prism", such as Figure 5 (c).

[0112] 7. Volume calculation: Assume that the projection of the triangular prism is , including only The volume of the irregular triangular prism is , including The volume of an irregular triangular prism is expressed as . It is composed of three volumes: pyramid + prism + pyramid. The volume is , the volume of the prism is , pyramid The volume is , known projection point , because the defect point cloud was rotated and translated in step 1, and the projection was vertical The negative direction of the axis, so the coordinates of each point in the figure can be used to and their respective first intersections ,set up Represents the side length of the two points. Using the distance relationship between the two points and Heron's formula, we can find the point to the edge The distance is , as follows:

[0113]

[0114] Where: ;

[0115] at this time Pyramid If It can be expressed as follows:

[0116]

[0117] Where: .

[0118] The volume is as follows:

[0119]

[0120] In the formula , The volume of similar, ,but , and only contains The volume of an irregular triangular prism The process of obtaining the volume is similar to the above, and the total volume is expressed as follows:

[0121]

[0122] The above-described embodiments merely represent specific implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection of the present application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the technical concept of the present application, and all such variations and improvements fall within the scope of protection of the present application.

[0123] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of the description in this section that did not constitute prior art at the time of filing are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. A concrete defect volume quantification method based on improved point cloud projection, characterized in that: include: Step S1: Scanning the surface area of ​​the dam concrete using a point cloud data acquisition device, and pre-processing the scanned point cloud data; Step S2: weighting the curvature and geometric distance characteristics of the pre-processed point cloud data to extract the concrete defect area and obtain point cloud data of the concrete defect area; Step S3: For the point cloud data of the concrete defect area, the ray parity method is used to determine whether there is point cloud occlusion, and the volume validity of the projected volume is determined for the point cloud of the occluded part using the volume validity method; The step S3 specifically includes the following steps: Step S31: Data parameter preparation; take the normal vector of the fitting plane as the projection direction, and rotate and translate the point cloud of the concrete defect area to the XOY plane based on the plane equation, and set all defect point cloud parameters ; Step S32: Slice region fitting; point The surrounding area is sliced, and the slicing direction is the projection direction; the quadratic surface is fitted to the slice area, and the point Generate rays along the projection direction; Step S33: The intersection of the ray generated by the point along the projection direction and the fitted quadratic surface can be obtained by the ray parity method, thereby constructing the volume validity; Step S34: Counting the effective heights of the points; Based on volume validity, if the point There is an intersection with the quadratic surface fitted in the slice area, and the height of the first intersection is recorded as ,point The original height is , and set If there is no intersection point, the first intersection point is assumed to be the best fit plane. parameter ; Step S35: triangulation projection; triangulate the entire defect area and then project it to the intersection height recorded at each point to form an irregular triangular prism; Step S36: Simplify the triangular prism structure; divide the projected triangular prism into The irregular triangular prism and the The irregular triangular prism contains The irregular triangular prism is simplified to a combination of pyramid + prism + pyramid; only contains The irregular triangular prism is simplified to a combination of pyramid + prism; Step S37: performing volume calculation based on the combination formed in step S36; The step S37 includes: Calculate the volume of each pyramid or prism in the combination separately and add them together to get the total volume.

2. The method for quantifying concrete defect volume based on improved point cloud projection according to claim 1, characterized in that: The preprocessing comprises: Step S11: Use the point cloud processing software Cloud Compare to evaluate noise characteristics; Step S12: removing outliers; Step S13: Smoothing the point cloud data.

3. The method for quantifying concrete defect volume based on improved point cloud projection according to claim 2, characterized in that: The step S11 includes: Use Cloud Compare, a point cloud processing software, to import point cloud data. Use visualization tools to examine the overall shape of the point cloud, identify the distribution and scale of outliers and small areas of abnormal concentration, understand the basic properties of the dam concrete point cloud data, and evaluate noise characteristics. The step S12 includes: Statistical filtering is used to select several neighborhood points based on the density and distribution of the point cloud. The average and standard deviation of the distance from each point to the neighboring points are calculated. The average plus N times the standard deviation is used as the threshold, and points with distances exceeding the threshold are eliminated. The step S13 includes: By setting the spatial domain filter radius and the value domain filter standard deviation, bilateral filtering is used to smooth the point cloud while retaining edge details.

4. The method for quantifying concrete defect volume based on improved point cloud projection according to claim 3, characterized in that: To remove noise in small areas with concentrated abnormalities, the density-based DBSCAN clustering algorithm is used. The neighborhood radius and minimum number of points are set for clustering, and abnormal clusters are identified and deleted based on density characteristics.

5. The method for quantifying concrete defect volume based on improved point cloud projection according to claim 1, characterized in that: The step S2 includes: Step S21: Statistically filter the pre-processed point cloud data to remove outliers that may not have been removed in step S1; Step S22: Calculating point cloud features of local curvature and relative distance; Step S23: performing Gaussian weighted summation on the calculated point cloud features; Step S24: setting a threshold to extract point cloud data of the dam concrete defect area.

6. The method for quantifying concrete defect volume based on improved point cloud projection according to claim 5, characterized in that: The step S22 includes: The local area size of the point is set, and the quadratic surface is fitted by the least squares method, and the average curvature is obtained through the second fundamental form of the surface; then the principal component analysis method is used on the point cloud data of the dam concrete to obtain a relatively stable plane fitting result, and the relative distance of each point to the fitting plane is calculated.

7. The method for quantifying concrete defect volume based on improved point cloud projection according to claim 5, characterized in that: The step S23 includes: Step S231: performing Gaussian weight calculation on the local curvature and relative distance respectively; Step S232: Based on the weights calculated in step S231, calculate the weighted relative distance from the point to the plane and the weighted local curvature ; Step S233: Relative distance and local curvature Comprehensively weighted together to form a new comprehensive eigenvalue .

8. The method for quantifying concrete defect volume based on improved point cloud projection according to claim 6, characterized in that: The step S3 comprises: The point cloud data of the concrete defect area is rotated and translated according to the fitting plane; the volume of the defect area is calculated according to the point cloud projection method.

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