Concrete defect volume quantification method for improving point cloud projection

Abstract

The invention discloses a concrete defect volume quantification method based on improved point cloud projection, and relates to the technical field of three-dimensional point cloud data processing and civil engineering nondestructive testing. According to the invention, the apparent area of the dam concrete is scanned through the point cloud data acquisition device, and the data is preprocessed to filter noise and miscellaneous points, so that good dam concrete point cloud data is obtained; the curvature and geometric distance characteristics of the point cloud data are weighted, and a concrete defect area is extracted; whether point cloud shielding exists or not is judged for points in a concrete defect area by adopting a ray odd-even method, and effectiveness judgment is performed on the projected volume by adopting volume effectiveness for the point cloud of the shielded part, so that the precision and reliability of defect volume quantification are improved, and a scientific basis is provided for safety assessment, maintenance and reinforcement of a concrete dam.

Description

Technical Field

[0001] The present invention relates to the technical fields of three-dimensional point cloud data processing and non-destructive testing in civil engineering, and particularly relates to a method for quantifying the volume of concrete defects by improving point cloud projection. Background Art

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

[0003] As a key infrastructure in water conservancy projects, dams play a crucial role in multiple fields such as flood control, irrigation, power generation, and water supply. However, due to the combined influence of long-term complex external environmental factors and internal material properties, defects inevitably occur in the concrete of dams. The existence of these defects will significantly reduce the structural strength and durability of the dams, weaken their impermeability, and may even lead to the failure of the dams in severe cases, resulting in catastrophic consequences. Therefore, accurately quantifying the volume of concrete defects in dams is of great practical significance for evaluating the safety status of dams, formulating scientific and reasonable repair and reinforcement plans, and ensuring the long-term stable operation of dams.

[0004] Traditional methods for detecting and quantifying the volume of concrete dam defects mainly rely on manual on-site inspections and limited local measurements. These methods are not only inefficient and labor-intensive but also greatly affected by human factors, making it difficult to comprehensively and accurately obtain precise information on the defect volume. With the continuous progress of science and technology, three-dimensional laser scanning technology has gradually been applied to the detection of concrete dams, providing an effective means for obtaining detailed surface information of dams. Through these technologies, three-dimensional point cloud data of dams can be quickly and accurately obtained, providing a data basis for realizing the quantification of the defect volume.

[0005] Currently, certain research results have been achieved in volume calculation methods based on point cloud data. However, when actually applied to the quantification of concrete dam defect volumes, many challenges still remain. For example, the point cloud data of dams has the characteristics of complex defect morphology and point cloud occlusion. How to accurately extract the point cloud information of the defect area from the massive point cloud data and use a suitable method to calculate its volume is an urgent problem to be solved currently. In addition, due to the irregularity and diversity of dam defects, existing volume calculation methods may not be fully applicable to all types of defects and further exploration and improvement are needed. Summary of the Invention

[0006] The object of the present invention is to provide an improved method for quantifying the volume of concrete defects by point cloud projection in view of the problems existing in the prior art. The method scans the apparent area of dam concrete through a point cloud data acquisition device, preprocesses the data to filter out noise and miscellaneous points, and obtains better point cloud data of dam concrete; weights the curvature and geometric distance characteristics of the point cloud data to extract the concrete defect area; uses the ray parity method to judge whether there is point cloud occlusion for the points in the concrete defect area, and uses volume validity to judge the validity of the projected volume for the occluded part of the point cloud, so as to improve the accuracy and reliability of defect volume quantification and provide a scientific basis for the safety assessment, repair and reinforcement of concrete dams.

[0007] The technical solution of the present invention is as follows: An improved method for quantifying the volume of concrete defects by point cloud projection, comprising: Step S1: Scan the apparent area of dam concrete through a point cloud data acquisition device and preprocess the scanned point cloud data; Step S2: Weight the curvature and geometric distance characteristics of the preprocessed point cloud data to extract the concrete defect area and obtain the point cloud data of the concrete defect area; Step S3: For the point cloud data of the concrete defect area, use the ray parity method to judge whether there is point cloud occlusion, and use volume validity to judge the validity of the projected volume for the occluded part of the point cloud.

[0008] Further, the preprocessing includes: Step S11: Use the point cloud processing software Cloud Compare to evaluate the noise characteristics; Step S12: Remove the outlier points; Step S13: Smooth the point cloud data.

[0009] Further, the step S11 includes: Use the point cloud processing software Cloud Compare to import the point cloud data, view the overall shape of the point cloud with the help of a visualization tool, clarify the distribution and scale of the outlier points and small abnormal concentration areas, master the basic attributes of the point cloud data of dam concrete, and evaluate the noise characteristics; The step S12 includes: Adopt statistical filtering, select several neighborhood points according to the point cloud density and distribution, calculate the average value and standard deviation of the distance from each point to the neighborhood points, and use the average value plus N times the standard deviation as the threshold to remove the points whose distance exceeds the threshold; The step S13 includes: By setting the spatial domain filtering radius and the range domain filtering standard deviation, use bilateral filtering to smooth the point cloud while retaining the edge details.

[0010] Furthermore, for noise removal in the small abnormal concentration areas, the density-based DBSCAN clustering algorithm is used to perform clustering by setting the neighborhood radius and the minimum number of points parameters, and the abnormal clusters are identified and deleted based on the density characteristics.

[0011] Furthermore, step S2 includes: Step S21: Perform statistical filtering on the preprocessed point cloud data to remove the outlier points that may not have been removed in step S1; Step S22: Calculate the point cloud features of local curvature and relative distance; Step S23: Perform Gaussian weighted summation on the calculated point cloud features respectively; Step S24: Set a threshold to extract the point cloud data of the dam concrete defect area.

[0012] Furthermore, step S22 includes: Set the local neighborhood size of the points, fit the quadratic surface by the least squares method, and obtain the average curvature through the second fundamental form of the surface; then the principal component analysis method is used for the point cloud data of the dam concrete to obtain a relatively stable plane fitting result, and the relative distance from each point to the fitting plane is calculated.

[0013] Furthermore, step S23 includes: Step S231: Perform 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 of the points to the plane and the weighted local curvature ; Step S233: Combine the relative distance and the local curvature and weight them together to form a new comprehensive eigenvalue .

[0014] Furthermore, step S3 includes: Rotate and translate the point cloud data of the concrete defect area according to the fitting plane; calculate the volume of the defect area according to the point cloud projection method.

[0015] Furthermore, step S3 specifically includes the following steps: Step S31: Prepare data parameters; use 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 the defect point cloud parameters ; Step S32: Fit the sliced area; for the points Slice the surrounding area, and the slicing direction is the projection direction; fit a quadratic surface to the sliced area, and from the point Generate rays along the projection direction; Step S33: The intersection situation between the rays generated from the point along the projection direction and the fitted quadratic surface can be obtained by the ray parity method, and the volume validity is constructed therefrom; Step S34: Statistically calculate the effective height of the points; based on the volume validity, if the point has an intersection with the fitted quadratic surface in the sliced area, record the height of the first intersection as , the original height of the point is , and set ; if there is no intersection, by default, the first intersection is the best-fitting plane, and set the parameter of the point ; Step S35: Triangulate the projection; triangulate the entire defect area and then project it; project it onto the intersection height recorded for each point to form an irregular triangular prism; Step S36: Simplify the triangular prism structure; divide the projected triangular prism into irregular triangular prisms that only contain and irregular triangular prisms that contain , simplify the irregular triangular prism that contains into a combination of a pyramid + a prism + a pyramid; simplify the irregular triangular prism that only contains into a combination of a pyramid + a prism; Step S37: Based on the combination formed in Step S36, perform volume calculation.

[0016] Furthermore, the said Step S37 includes: Calculate the volumes of the respective pyramids or prisms in the combination respectively, and then sum them up to obtain the total volume.

[0017] Compared with the existing technology, the beneficial effects of the present invention are: The present invention can improve the accuracy and reliability of defect volume quantification, and provide a scientific basis for the safety assessment and repair and reinforcement of concrete dams. Description of the Drawings

[0018] Figure 1 It is a block diagram of a method for quantifying the volume of concrete defects by improving point cloud projection; Figure 2 It is a schematic diagram of denoising the point cloud of dam concrete; Figure 3 It is a flow chart for extracting the defect area; Figure 4 It is a schematic diagram of extracting the point cloud of concrete defects in the dam; Figure 5 Calculating the volume by the traditional point cloud projection method; Figure 6 For the improved point cloud projection method. Specific implementation manners

[0019] It should be noted that relational terms such as "first" and "second" are only used 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 "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0020] The features and performance of the present invention will be further described in detail below in conjunction with the embodiments.

[0021] Embodiment 1 Please refer to Figure 1 , an improved method for quantifying the volume of concrete defects by point cloud projection, comprising: Step S1: Scanning the apparent area of the dam concrete by a point cloud data acquisition device, and preprocessing the scanned point cloud data; Step S2: Weighting the curvature and geometric distance characteristics of the preprocessed point cloud data, extracting the concrete defect area, and obtaining the point cloud data of the concrete defect area; Step S3: For the point cloud data of the concrete defect area, use the ray parity method to judge whether there is point cloud occlusion, and use the volume validity to judge the validity of the projected volume for the occluded part of the point cloud, so as to improve the accuracy and reliability of defect volume quantification, and provide a scientific basis for the safety assessment and repair and reinforcement of the concrete dam.

[0022] In this embodiment, it should be noted that the three-dimensional reconstruction technology combining binocular vision and structured light is one of the most effective means to obtain the point cloud data of irregular objects at present. The present invention adopts a high-precision depth camera and uses the three-dimensional reconstruction technology integrating binocular vision and structured light array to provide a data basis for subsequent high-precision volume quantification.

[0023] In this embodiment, it should be noted that during the rescan process, the scanning angle and position can be adjusted according to the scanned dam concrete area.

[0024] In this embodiment, specifically, the preprocessing includes: 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.

[0025] In this embodiment, specifically, 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.

[0026] 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.

[0027] 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.

[0028] After analyzing the noise characteristics of the point cloud data, outlier removal is performed. First, statistical filtering is used. Based on the point cloud density and distribution, 10 - 30 neighborhood points are selected. The average value and standard deviation of the distances from each point to the neighborhood points are calculated. Taking the average value plus 2 - 3 times the standard deviation as the threshold, the points with distances exceeding the threshold are deleted. For the noise removal in small areas with abnormal concentration, the density - based DBSCAN clustering algorithm is used. The neighborhood radius and minimum number of points parameters are set for clustering, and the abnormal clusters are identified and deleted according to the density characteristics. Finally, smoothing processing is carried out on the point cloud data. By setting the spatial domain filtering radius and range domain filtering standard deviation, bilateral filtering is used to smooth the point cloud while retaining the edge details. As Figure 2 , for the denoising effect of the point cloud data of the dam concrete area collected, the areas around the original point cloud data with different densities from the original data and the outliers are removed.

[0029] In this embodiment, specifically, please refer to Figure 3 , the step S2 includes: Step S21: Perform statistical filtering on the pre - processed point cloud data to remove the outliers that may not have been removed in step S1; the set value range is smaller than the filtering range in the previous step, and the outliers that may not have been removed in the previous step are removed; Step S22: Calculate the point cloud features of local curvature and relative distance; Step S23: Perform Gaussian weighted summation on the calculated point cloud features respectively; Step S24: Set a threshold to extract the point cloud data of the dam concrete defect area. The specific defect extraction effect is as Figure 4 shown.

[0030] In this embodiment, specifically, the step S22 includes: Set the local neighborhood size of the point, fit the quadratic surface by the least - squares method, and obtain the average curvature through the second fundamental form of the surface; then, the principal component analysis method is used for the point cloud data of the dam concrete to obtain a relatively stable plane fitting result, and the relative distance from each point to the fitting plane is calculated.

[0031] In this embodiment, specifically, the step S23 includes: Step S231: Perform 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 of the point to the plane and the weighted local curvature ; Step S233: Combine the relative distance and the local curvature and weight them together to form a new comprehensive eigenvalue ; According to multiple experiments, the values in this embodiment .

[0032] In this embodiment, specifically, taking the point as the center, calculate the local neighborhood according to the Gaussian distribution midpoint weight ; The Gaussian distribution function is expressed as the following formula:

[0033] By calculating to obtain the relative distance from the weighted point to the plane; Calculate to obtain the weighted local curvature; Where represents all points in the neighborhood, is the point the distance to the fitting plane and the local curvature, is the standard deviation of the Gaussian distribution.

[0034] In this embodiment, specifically, the step S3 includes: Rotate and translate the point cloud data of the concrete defect area according to the fitting plane; calculate the volume of the defect area according to the point cloud projection method.

[0035] In this embodiment, specifically, the step S3 specifically includes the following steps: Step S31: Data parameter preparation; taking the normal vector of the fitting plane as the projection direction, and rotating and translating the point cloud of the concrete defect area to the XOY plane based on the plane equation, and setting all defect point cloud parameters ; Step S32: Fitting of the sliced area; slice the area around the point , the slicing direction is the projection direction; fit a quadratic surface to the sliced area, and generate a ray from the point along the projection direction; Step S33: By the ray parity method, the intersection situation between the ray generated by the point along the projection direction and the fitted quadratic surface can be obtained, and thus the volume validity is constructed; Step S34: Statistically calculate the effective height of the point; based on the volume validity, if the point has an intersection with the fitted quadratic surface in the sliced area, record the height of the first intersection as , the original height of the point is , and set ; If there is no intersection, default the first intersection as the best fitting plane, and set the point parameters ; Step S35: Triangulation projection; triangulate the entire defect area and then project; project onto the intersection heights recorded for each point to form an irregular triangular prism; Step S36: Simplification of the triangular prism structure; divide the projected triangular prism into irregular triangular prisms that only contain and irregular triangular prisms that contain . The irregular triangular prism that contains is simplified to a combination of a pyramid + a prism + a pyramid; the irregular triangular prism that only contains is simplified to a combination of a pyramid + a prism; Step S37: Based on the combination formed in Step S36, perform volume calculation.

[0036] In this embodiment, specifically, Step S37 includes: Calculate the volumes of the pyramids or prisms in the combination respectively, and then add them up to obtain the total volume.

[0037] In this embodiment, after obtaining the point cloud of the defect area, rotate and translate the defect point cloud data according to the previously fitted plane. Calculate the volume of the defect area according to the point cloud projection method, as follows Figure 5 shown. Assume that it is projected onto the XOY plane, project each point vertically, and then obtain Figure 5 (b). Divide the irregular triangular prisms in Figure 5 (b) to obtain Figure 5 (c). At this time, calculate the sum of the volumes of the irregular triangular prisms obtained by each projection to obtain the volume of the concrete defect.

[0038] However, the situation of concrete defects in the dam is relatively complex, and there may be projection occlusion during projection, as follows Figure 6 shown. Since the vertical projection is directly projected onto the best-fitting plane, and it can be seen from Figure 6 that when calculating the volume by projecting the point cloud of the concrete defect in the dam, the vertical projection should stop projecting downward when it first touches the inner wall of the defect for the first time, while the traditional method continues to project downward, resulting in an overestimated volume when using the point cloud projection method to calculate the volume. In view of the situation where the measured volume increases due to projection occlusion, this embodiment proposes a method for quantifying the volume of concrete defects in the dam based on the improved projection method. The specific steps are as follows: 1. Data parameter preparation: According to the best-fitting plane equation obtained above, use the normal vector and of this plane as the projection direction, and rotate and translate the defect point cloud to the XOY plane based on the plane equation to facilitate subsequent projection calculations and set all defect point cloud parameters ; 2. Fitting of the sliced area: Let the point Slice the area of plus or minus 1 mm (depending on the density of the point cloud, which can be reduced by high-precision instruments) around this point, and the slicing direction is the projection direction as Figure 6 (b) shows; fit a quadratic surface to the sliced area and generate a ray along the projection direction from this point; 3. Volume validity construction: The intersection situation between the ray generated by the point along the projection direction and the fitted quadratic surface can be obtained by the ray parity method. As Figure 6 (c) shows, when the projection ray first intersects the quadratic surface, the volume of the irregular triangular prism generated by the projection is valid at this time. When it exceeds the first intersection point, the volume of the generated irregular triangular prism is invalid. Thus, the volume validity is constructed; 4. Statistic of the effective height of points: Based on the volume validity, if the point has an intersection with the quadratic surface fitted in the sliced area, record the height of the first intersection point as , the original height of the point is , and set . If there is no intersection point, default the first intersection point as the best-fitting plane, and set the parameter of the point ; 5. Triangulation projection: After performing the fourth step on all the concrete defect point cloud data, each point in the concrete point cloud, in addition to its own height , also has the first intersection point . Triangulate the entire defect area and then perform projection. Project it onto the intersection height recorded by each point to form an irregular triangular prism, as Figure 6 (c) shows; 6. Simplification of the triangular prism structure: Divide the projected triangular prism into an irregular triangular prism that only contains and an irregular triangular prism that contains . The irregular triangular prism that contains can be simplified into a combination of "pyramid + prism + pyramid", as Figure 6 (d). The irregular triangular prism that only contains is simplified into "pyramid + prism", as Figure 5 (c).

[0039] 7. Volume calculation: Assume that there are a total of triangular prism projections. The volume of the irregular triangular prism that only contains is . The volume of the irregular triangular prism that contains is expressed as . It is composed of the volumes of three parts: "pyramid + prism + pyramid". Assume that the volume of the pyramid 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 get the point to the edge The distance is , as follows:

[0040] Where: ; at this time Pyramid If It can be expressed as follows:

[0041] Where: .

[0042] The volume is as follows:

[0043] In the formula , The volume of similar, ,but , and only contains The volume of an irregular triangular prism The calculation process is similar to the above, and the total volume is expressed as follows:

[0044] 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.

[0045] This Background of the Invention section is provided to generally present the context of the present invention. Work of the presently named inventors, to the extent it is described in this Background of the Invention section, and work that is not prior art to the aspects of the present invention described in this section, is not, either expressly or implicitly, admitted to be prior art to the present invention.

Claims

1. An improved method for quantifying the volume of concrete defects by point cloud projection, characterized in that, Including: Step S1: Scan the apparent area of the dam concrete by a point cloud data acquisition device and preprocess the scanned point cloud data. Step S2: Weight the curvature and geometric distance characteristics of the preprocessed point cloud data, extract the concrete defect area, and obtain the point cloud data of the concrete defect area. Step S3: For the point cloud data of the concrete defect area, use the ray parity method to judge whether there is point cloud occlusion, and use volume validity to judge the validity of the projected volume for the occluded part of the point cloud.

2. An improved point cloud projection-based concrete defect volume quantification method according to claim 1, characterized in that, The preprocessing includes: Step S11: Use the point cloud processing software Cloud Compare to evaluate the noise characteristics. Step S12: Remove the outlier points. Step S13: Perform smoothing processing on the point cloud data.

3. An improved point cloud projection-based concrete defect volume quantification method according to claim 2, characterized in that, The step S11 includes: Use the point cloud processing software Cloud Compare to import the point cloud data, view the overall shape of the point cloud with the help of a visualization tool, clarify the distribution and scale of the outlier points and small abnormal concentrated areas, master the basic attributes of the dam concrete point cloud data, and evaluate the noise characteristics. The step S12 includes: Adopt statistical filtering, select several neighborhood points according to the point cloud density and distribution, calculate the average value and standard deviation of the distance from each point to the neighborhood points, and use the average value plus N times the standard deviation as the threshold to remove the points with distances exceeding the threshold. The step S13 includes: By setting the spatial domain filtering radius and the range domain filtering standard deviation, use bilateral filtering to smooth the point cloud while retaining the edge details.

4. An improved point cloud projection-based concrete defect volume quantification method according to claim 3, characterized in that For the noise removal of small abnormal concentrated areas, use the density-based DBSCAN clustering algorithm, set the neighborhood radius and minimum number of points parameters for clustering, and identify and delete abnormal clusters according to the density characteristics.

5. An improved point cloud projection-based concrete defect volume quantification method according to claim 1, characterized in that The step S2 includes: Step S21: Perform statistical filtering on the preprocessed point cloud data to remove the outlier points that may not have been removed in step S1. Step S22: Calculate the point cloud features of local curvature and relative distance. Step S23: Perform Gaussian weighted summation on the calculated point cloud features respectively. Step S24: Set a threshold to extract the point cloud data of the dam concrete defect area.

6. An improved point cloud projection-based concrete defect volume quantification method according to claim 5, characterized in that, The step S22 includes: Set the local neighborhood size of the point, fit the quadratic surface by the least squares method, and obtain the average curvature through the second fundamental form of the surface; then use the principal component analysis method for the point cloud data of the dam concrete to obtain a relatively stable plane fitting result, and calculate the relative distance from each point to the fitting plane.

7. An improved point cloud projection-based concrete defect volume quantification method according to claim 5, characterized in that, The step S23 includes: Step S231: Perform Gaussian weight calculation on local curvature and relative distance respectively. Step S232: Calculate the weighted relative distance from the point to the plane and the weighted local curvature based on the weights calculated in Step S231 and the weighted local curvature ; Step S233: Combine the relative distance and the local curvature by comprehensive weighting to form a new comprehensive eigenvalue .

8. An improved point cloud projection-based concrete defect volume quantification method according to claim 6, characterized in that The step S3 includes: Rotate and translate the point cloud data of the concrete defect area according to the fitting plane; calculate the volume of the defect area according to the point cloud projection method.

9. An improved point cloud projection-based concrete defect volume quantification method according to claim 8, characterized in that The step S3 specifically includes the following steps: Step S31: Data parameter preparation; taking the normal vector of the fitting plane as the projection direction, rotating and translating the point cloud of the concrete defect area to the XOY plane based on the plane equation, and setting all defect point cloud parameters ; Step S32: Fitting of the sliced region; For the points in the surrounding region, perform slicing with the slicing direction being the projection direction; Fit a quadratic surface to the sliced region, and generate a ray from the points along the projection direction; Step S33: The ray parity method can obtain the intersection situation of the ray generated by the point along the projection direction and the fitted quadratic surface, and thus construct the volume validity. Step S34: Count the effective height of the points. Based on volume validity, if the point intersects with the quadratic surface fitted in the slice region, record the height of the first intersection as , the original height of the point is , and set ; if there is no intersection, default the first intersection as the best-fitting plane, and set the parameter of the point ; Step S35: Triangulation projection; triangulate the entire defect area and then perform projection; project onto the intersection heights recorded for each point to form irregular triangular prisms; Step S36: Simplification of the triangular prism structure; the projected triangular prism is divided into an irregular triangular prism that only contains and an irregular triangular prism that contains . The irregular triangular prism that contains is simplified into a combination of a pyramid + a prism + a pyramid; the irregular triangular prism that only contains is simplified into a combination of a pyramid + a prism; Step S37: Based on the combination formed in step S36, perform volume calculation.

10. An improved point cloud projection-based concrete defect volume quantification method according to claim 9, characterized in that, The said step S37 includes: Calculate the volumes of the individual pyramids or prisms in the combination respectively and then sum them up to obtain the total volume.

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