CN116935250B - Building template size estimation method based on unmanned aerial vehicle shooting - Google Patents
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Abstract
The invention discloses a building template size estimation method based on unmanned aerial vehicle shooting, which comprises the following steps of: step 1: obtaining a template image of a target building based on unmanned aerial vehicle shooting, and carrying out three-dimensional point cloud reconstruction on the template image to obtain three-dimensional point cloud data; step 2: dividing design point cloud data of a template based on a target design drawing of a target building to obtain template point cloud data of the template divided according to floors, and marking the template point cloud data as first target point cloud data; step 3: and estimating the beam column part size of each floor slab region in the target building template based on the corner features in the first target point cloud data. The invention can realize the automatic data collection of large-area template engineering, the high-efficiency segmentation of large-area template point cloud data, the intelligent prediction of beam size, the obvious improvement of template acceptance efficiency and the contribution to the global quality control of the template engineering.
Description
Technical Field
The invention relates to the technical field of building engineering construction, in particular to a building template size estimation method based on unmanned aerial vehicle shooting.
Background
Many buildings, particularly large public buildings, often require a large number of templates for cast-in-place formation of beam, slab and other components, and the roof area (i.e., size) of the entire building is often large, i.e., in these buildings, the templates are used over a large area. This means that the form quality will directly influence the component quality and thus the building quality. When the size of the template is not consistent with the design value, not only the potential safety hazard on the building structure is brought, but also the thickness of the protective layer of the cast-in-situ formed member on the template is insufficient to expose the reinforcing steel bars (the reinforcing steel bars in the cast-in-situ member are exposed), and the service performance and durability of the building structure are seriously impression. Therefore, it is necessary to confirm the size of the building form so as to be able to compare with the design value, thereby judging whether the size of the building form meets the design requirement.
In the prior art, in order to confirm the size of the template, a manual acceptance mode is often adopted, and the mode is time-consuming and labor-consuming, depends on the experience value of a detector, and has a dominant factor. In addition, the manual acceptance mode is adopted, and half of the modes are sampling inspection modes, so that all the template sizes cannot be confirmed, namely the global quality of the template cannot be controlled. In addition to the manual acceptance method, a land type three-dimensional scanning technology is also adopted at present, and although the land type three-dimensional scanning technology can acquire global information of templates, the land type three-dimensional scanning technology faces large-scale buildings, because of the large number of templates and large size and area, more tested stations (one scanner constantly changes scanning positions) are needed, and the scanning data of each tested station need to be spliced, so that splicing difficulty exists. In addition, the existing template size confirmation technology based on the three-dimensional scanner still has the problem of inaccurate estimation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a building template size estimation method based on unmanned aerial vehicle shooting, which can solve the technical problems described in the background art.
The technical scheme for realizing the purpose of the invention is as follows:
a building template size estimation method based on unmanned aerial vehicle shooting comprises the following steps:
step 1: obtaining a template image of a target building based on unmanned aerial vehicle shooting, and carrying out three-dimensional point cloud reconstruction on the template image to obtain three-dimensional point cloud data of a template;
step 2: dividing design point cloud data of a template based on a target design drawing of a target building to obtain template point cloud data of the template divided according to floors, and marking the template point cloud data as first target point cloud data;
step 3: and estimating the beam column part size of each floor slab region in the target building template based on the corner features in the first target point cloud data.
Further, in step 1, an unmanned aerial vehicle with a five-eye tilt camera is used for performing tilt photography on a template of a roof of a template building, so as to obtain a template image.
Further, in step 1, the three-dimensional point cloud reconstruction is completed by adopting ContextCapture or Xingjiang intelligent map software.
Further, the specific implementation process of the step 2 includes the following steps:
step 21: obtaining a Liang Pingfa construction drawing of a template of a target building, wherein the Liang Pingfa construction drawing only comprises two types of components, namely a beam and a column;
step 22: in Liang Pingfa construction diagram, M points representing beam column components meeting the first condition are selected, Z-axis coordinate values of the M points are all assigned to be 0, M is more than or equal to 3, the selected M points are all used as design drawing control points of template point cloud data,
condition one: the area surrounded by the selected beam column parts exceeds a preset threshold value and is not on the same straight line, and point clouds similar to the positions of the selected beam column parts can be found in the template point cloud data;
step 23: selecting N point clouds corresponding to the positions of the control points of the design drawing from the template point cloud data, wherein the selected N point clouds are used as scanning point cloud control points of the template point cloud data, and N=M;
step 24: calculating transformation matrixes of the control points of the design drawing and the control points of the scanning point cloud, and transforming the template point cloud data under the design coordinate system based on the transformation matrixes to obtain the template point cloud data under the design coordinate system;
step 25: in Liang Pingfa, filling each floor area by using a filling tool, and storing the floor areas in a preset file format, for example, storing the floor areas in a DXF file format, wherein keywords of filling pattern types are used as indexes, and plane coordinates of corner points of each floor area are stored, wherein the corner points of the floor areas are the vertexes of a polygon formed by floor outlines;
step 26: based on the angle coordinates of the filled floor areas, establishing a corresponding surrounding frame of an XY plane for each floor area, wherein the surrounding frame of the XY plane surrounds the outline of the main floor area, and amplifying the surrounding frame;
step 27: and screening out the template point cloud data under the design coordinate system in the amplified bounding box through the amplified bounding box to obtain the template point cloud data divided according to the floor slab.
Further, in step 26, the bounding box is enlarged in a proportion of 1.02.
Further, the specific implementation process of the step 3 includes the following steps:
step 31: traversing each point cloud in the template point cloud data divided according to the floor slab, fitting the point cloud with a RANSAC algorithm to obtain a plurality of planes, wherein the planes comprise side planes and top planes of the template, the currently fitted point cloud is an outer point of the plane obtained by the previous fitting, the fitting is stopped until the number of the outer points is less than a point cloud preset threshold value, and the relative position of the plane in the template point cloud data divided according to the floor slab can be judged based on the normal vector of the plane and the coordinate value of the inner point;
step 32: calculating corner points of the top surface and the bottom surface of the template according to the relative positions of the plane and the plane in the template point cloud data divided according to the floor slab;
step 33: calculating the center points of all the corner points on the top surface, wherein the center points of the corner points refer to the average value of all the corner point coordinates on the top surface, namely the XYZ coordinate values of the center points are the average value of all the corner point XYZ coordinates;
step 34: constructing a direction vector by taking the central point of the top surface of the current template as a starting point and the central point of the top surface of each template within a preset distance range as an end point, so as to obtain a plurality of direction vectors of the current template and the potential neighborhood template;
step 35: for the current template, connecting the central point of the top surface angular point of the current template with two angular points at the top of each side plane respectively, and dividing the potential neighborhood template of the current template into potential adjacent templates of each side plane in the angle range of the included angle of the two connecting lines;
step 36: calculating the projection length of a first connecting line on the normal vector of the current side plane, wherein the first connecting line is a connecting line of a first central point and a second central point, the first central point is the central point of a side plane corner point of a potential adjacent template of the current side plane, the second central point is the central point of the current side plane corner point of the current template, the current side plane of the current template corresponds to each side plane of the potential adjacent template, a plurality of first connecting lines are obtained, the minimum projection length is taken as the width of a beam of the current template in all the first connecting lines, the potential neighborhood side planes corresponding to the current side plane and the first connecting line with the minimum projection length are recorded, each side plane only carries out one beam size estimation,
calculating the average value of the height difference between the upper and lower corner points of the current side plane where the first connecting line is located and the upper and lower corner points of the side plane corresponding to the minimum projection length, taking the average value as the beam height between the current template and the neighborhood template for eliminating the thickness of the floor slab, completing the size estimation of the beam,
traversing the remaining side planes of the current template, estimating the beam sizes participated in by the remaining side planes,
and traversing the residual templates to complete the estimation of all beam sizes.
Further, in step 31, after the first fitting of the plane, the point cloud located at the upper portion of the first fitting plane needs to be deleted.
Further, the point cloud preset threshold is 100.
Further, after step 3, step 4 is included,
step 4: and comparing the error between the estimated size and the designed size of the beam-column component, and retesting and correcting the size of the beam-column component of the actual template based on the error.
Further, the error between the dimension of the beam-column component and the design dimension is compared, and the method comprises the following steps:
step 41: in Liang Pingfa construction drawing, obtaining the design size of the beam column part based on in-situ labeling information;
step 42: the design size of the beam column part in the Liang Pingfa construction drawing is moved to the corresponding beam column part, and the size and the coordinates of the beam column part in the Liang Pingfa construction drawing are stored in a DXF file format and extracted through keyword searching;
step 43: and extracting the dimension label closest to the side plane according to the center point of the side plane and the side plane equation of the estimated dimension of the beam column part, and taking the extracted dimension label as the corresponding design dimension, thereby calculating the error between the estimated dimension of the beam column part and the design dimension of the beam column part.
The beneficial effects of the invention are as follows: according to the invention, the unmanned aerial vehicle is provided with the oblique photographing equipment to collect the template data, so that the automatic collection of the data of the large-area template engineering can be realized. By the template point cloud example segmentation method based on the design drawing, efficient segmentation of large-area template point cloud data is realized. The cast-in-situ beam geometric dimension estimation method based on the corner features is high in automation degree, and intelligent beam dimension estimation is realized. The template size matching method based on the design drawing is used for rapidly identifying the deviation between the estimated size and the design size, and intuitively displaying the deviation of the template size. By adopting the cast-in-situ floor template size pre-inspection method based on unmanned aerial vehicle oblique photography, the rapid pre-inspection of the large template engineering can be realized, the template acceptance efficiency is obviously improved, and the global quality control of the template engineering is facilitated.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
fig. 2 is a template image of a building captured by an unmanned aerial vehicle;
FIG. 3 is a beam leveling construction work drawing of FIG. 2;
FIG. 4 is a schematic illustration of the filling of FIG. 3 by floor area;
FIG. 5 is a schematic view of the division of beam-column components of each of the floor areas of FIG. 2 based on corner features;
fig. 6 is a schematic view of the positions of corner points of the top and bottom surfaces of a certain template, wherein black dots in the figure represent the corner points.
Detailed Description
The invention is further described with reference to the accompanying drawings and detailed description below:
as shown in fig. 1 to 6, a building template size estimation method based on unmanned aerial vehicle shooting comprises the following steps:
step 1: and obtaining a template image of the target building based on unmanned aerial vehicle shooting, and carrying out three-dimensional point cloud reconstruction on the template image to obtain three-dimensional point cloud data of the template.
Referring to fig. 2, fig. 2 is a template image of a building photographed by an unmanned aerial vehicle.
In the step, unmanned aerial vehicle with five-eye inclined camera can be adopted to carry out inclined photography on the templates of the building roof of the template, so as to obtain template images. The course overlapping rate of the unmanned aerial vehicle is 90%, the side overlapping rate is 80%, and the flying height is as close to the upper surface of the template as possible and is 10m (meters) higher than the highest point in the flying area.
The three-dimensional point cloud reconstruction can be completed by adopting existing three-dimensional reconstruction software such as ContextCapture or intelligent map of Xinjiang.
Step 2: based on a target design drawing of a target building, dividing design point cloud data of the template to obtain template point cloud data of the template divided according to floors, and recording the template point cloud data as first target point cloud data.
The specific implementation process of the step comprises the following steps:
step 21: a Liang Pingfa construction map of the formwork of the target building is obtained, and the Liang Pingfa construction map comprises only two types of components, namely a beam and a column.
Step 22: in a Liang Pingfa construction drawing, M points representing beam column components meeting a first condition are selected, Z-axis coordinate values of the M points are all assigned to be 0, namely the sum of the beam components and the column components is M, M is more than or equal to 2, and the selected M points are all used as design drawing control points of template point cloud data.
Condition one: the selected beam column parts are larger in area (namely exceeding a preset threshold value) and are not on the same straight line, and point clouds similar to the positions of the selected beam column parts can be found in the template point cloud data.
In this embodiment, m=3, that is, three points representing the beam-column component are selected in the Liang Pingfa construction drawing, the area enclosed by the three points is larger and not on the same straight line, and three point clouds can be found in the template point cloud data, and the three point clouds are respectively similar to the three point clouds one by one.
Referring to fig. 3, fig. 3 is a beam leveling construction drawing of fig. 2.
Step 23: and selecting N point clouds corresponding to the positions of the control points of the design drawing from the template point cloud data, wherein the selected N point clouds are used as scanning point cloud control points of the template point cloud data, and N=M.
Step 24: and calculating transformation matrixes of the control points of the design drawing and the control points of the scanning point cloud, and transforming the template point cloud data under the design coordinate system based on the transformation matrixes to obtain the template point cloud data under the design coordinate system.
Step 25: in the Liang Pingfa construction diagram, each floor area is filled with filling tools and saved in a predetermined file format, for example, in a DXF file format. Wherein, the key words of filling pattern type are used as index, and the plane coordinates (namely XY coordinates) of the corner points of each floor area are stored, wherein the corner points of the floor area are the vertexes of the polygon formed by the outline of the floor deoiling.
Step 26: and establishing a corresponding XY plane bounding box for each floor area based on the angle coordinates of the filled floor areas, wherein the XY plane bounding box encloses the outline of the main floor area.
Referring to fig. 4, fig. 4 is a schematic illustration of the filling of fig. 3 by floor area.
In this step, the bounding box is enlarged in a proportion of 1.02 (which can be changed as needed in actual use).
Step 27: and screening out the template point cloud data under the design coordinate system in the amplified bounding box through the amplified bounding box to obtain the template point cloud data divided according to the floor slab.
Step 3: and estimating the beam column part size of each floor slab region in the target building template based on the corner features in the first target point cloud data.
Referring to fig. 5, fig. 5 is a schematic view of the division of beam-column components of each floor area of fig. 2 based on corner features.
The specific implementation process of the step comprises the following steps:
step 31: traversing each point cloud in the template point cloud data divided according to the floor slab, fitting the point cloud with a RANSAC algorithm to obtain a plurality of planes, wherein the planes comprise side planes and top planes of the template, the currently fitted point cloud is an outer point of the plane obtained by the previous fitting until the number of the outer points is less than a point cloud preset threshold, namely, when the number of the outer points is less than the point cloud preset threshold, stopping fitting. After the plane is fitted for the first time, the point cloud located at the upper part of the plane fitted for the first time needs to be deleted. And the relative position of the plane in the template point cloud data divided according to the floor slab can be judged based on the normal vector of the plane and the coordinate value of the internal point.
The outer points and the inner points are two concepts in the RANSAC algorithm, and represent the position relation of the plane fitted by the RANSAC algorithm.
In this embodiment, the point cloud preset threshold is 100.
Step 32: and calculating the angular points of the top surface and the bottom surface of the template according to the relative positions of the plane and the plane in the template point cloud data divided according to the floor.
Referring to fig. 6, fig. 6 is a schematic view of the positions of corner points of the top and bottom surfaces of a certain template, where black dots indicate corner points.
Step 33: and calculating the center points of all the corner points on the top surface, wherein the center points of the corner points refer to the average value of all the corner point coordinates on the top surface, namely the XYZ coordinate values of the center points are the average value of all the corner point XYZ coordinates.
Step 34: and constructing a direction vector by taking the central point of the top surface of the current template as a starting point and the central point of the top surface of each template within a preset distance range as an end point, so as to obtain a plurality of direction vectors of the current template and the potential neighborhood template.
Step 35: for the current template, the center point of the top surface corner point of the current template is respectively connected with two corner points at the top of each side plane, and the potential neighborhood template of the current template is divided into potential adjacent templates of each side plane in the angle range of the included angle of the two connected lines.
Step 36: calculating the projection length of a first connecting line on the normal vector of the current side plane, wherein the first connecting line is a connecting line of a first central point and a second central point, the first central point is the central point of a side plane corner point of a potential adjacent template of the current side plane, the second central point is the central point of the current side plane corner point of the current template, the current side plane of the current template corresponds to each side plane of the potential adjacent template, a plurality of first connecting lines are obtained, the minimum projection length is taken as the width of a beam of the current template in all the first connecting lines, the potential neighborhood side planes corresponding to the current side plane and the first connecting line with the minimum projection length are recorded, each side plane only carries out one beam size estimation,
calculating the average value of the height difference between the upper and lower corner points of the current side plane where the first connecting line is located and the upper and lower corner points of the side plane corresponding to the minimum projection length, taking the average value as the beam height between the current template and the neighborhood template for eliminating the thickness of the floor slab, completing the size estimation of the beam,
traversing the remaining side planes of the current template, estimating the beam sizes participated in by the remaining side planes,
and traversing the residual templates to complete the estimation of all beam sizes.
For a side plane, there are two corner points at the top (i.e. upper layer) and two corner points at the bottom (i.e. lower layer). The height difference exists between one corner point of the top and one corner point of the bottom on the same straight line, the height difference exists between one corner point of the top and one corner point of the bottom on the other straight line, and the average value obtained by evaluating the two height differences is used as the height of the beam column component of the current template. Where the current template is the current floor area and thus the dimensions (including width and height) of the beam-column components of the respective floor area are obtained.
In an alternative embodiment, step 4 is also included after step 3.
Step 4: and comparing the error between the estimated size and the designed size of the beam-column component, and retesting and correcting the size of the beam-column component of the actual template based on the error.
The specific implementation process of the method comprises the following steps of:
step 41: in Liang Pingfa construction drawings, beam column component design dimensions are obtained based on in-situ labeling information.
In this step, the Liang Pingfa construction drawing will typically label the relevant components in situ, and the design dimensions of the beam-column components with template information can be obtained from these in situ labels.
Step 42: and (3) moving the designed size of the beam column part in the Liang Pingfa construction drawing to a corresponding beam column part, storing in a DXF file format, and extracting the size and the coordinates of the beam column part in the Liang Pingfa construction drawing through keyword searching.
Step 43: and extracting the dimension label closest to the side plane according to the center point of the side plane and the side plane equation of the estimated dimension of the beam column part, and taking the extracted dimension label as the corresponding design dimension, thereby calculating the error between the estimated dimension of the beam column part and the design dimension of the beam column part.
According to the invention, the unmanned aerial vehicle is provided with the oblique photographing equipment to collect the template data, so that the automatic collection of the data of the large-area template engineering can be realized. By the template point cloud example segmentation method based on the design drawing, efficient segmentation of large-area template point cloud data is realized. The cast-in-situ beam geometric dimension estimation method based on the corner features is high in automation degree, and intelligent beam dimension estimation is realized. The template size matching method based on the design drawing is used for rapidly identifying the deviation between the estimated size and the design size, and intuitively displaying the deviation of the template size. By adopting the cast-in-situ floor template size pre-inspection method based on unmanned aerial vehicle oblique photography, the rapid pre-inspection of the large template engineering can be realized, the template acceptance efficiency is obviously improved, and the global quality control of the template engineering is facilitated.
The embodiment disclosed in the present specification is merely an illustration of one-sided features of the present invention, and the protection scope of the present invention is not limited to this embodiment, and any other functionally equivalent embodiment falls within the protection scope of the present invention. Various other corresponding changes and modifications will occur to those skilled in the art from the foregoing description and the accompanying drawings, and all such changes and modifications are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (9)
1. The building template size estimation method based on unmanned aerial vehicle shooting is characterized by comprising the following steps of:
step 1: obtaining a template image of a target building based on unmanned aerial vehicle shooting, and carrying out three-dimensional point cloud reconstruction on the template image to obtain three-dimensional point cloud data of a template;
step 2: dividing design point cloud data of a template based on a target design drawing of a target building to obtain template point cloud data of the template divided according to floors, and marking the template point cloud data as first target point cloud data;
step 3: in the first target point cloud data, beam column component sizes of all floor slab areas in the target building template are estimated based on corner features,
the specific implementation process of the step 3 comprises the following steps:
step 31: traversing each point cloud in the template point cloud data divided according to the floor slab, fitting the point cloud with a RANSAC algorithm to obtain a plurality of planes, wherein the planes comprise side planes and top planes of the template, the currently fitted point cloud is an outer point of the plane obtained by the previous fitting, the fitting is stopped until the number of the outer points is less than a point cloud preset threshold value, and the relative position of the plane in the template point cloud data divided according to the floor slab can be judged based on the normal vector of the plane and the coordinate value of the inner point;
step 32: calculating corner points of the top surface and the bottom surface of the template according to the relative positions of the plane and the plane in the template point cloud data divided according to the floor slab;
step 33: calculating the center points of all the corner points on the top surface, wherein the center points of the corner points refer to the average value of all the corner point coordinates on the top surface, namely the XYZ coordinate values of the center points are the average value of all the corner point XYZ coordinates;
step 34: constructing a direction vector by taking the central point of the top surface of the current template as a starting point and the central point of the top surface of each template within a preset distance range as an end point, so as to obtain a plurality of direction vectors of the current template and the potential neighborhood template;
step 35: for the current template, connecting the central point of the top surface angular point of the current template with two angular points at the top of each side plane respectively, and dividing the potential neighborhood template of the current template into potential adjacent templates of each side plane in the angle range of the included angle of the two connecting lines;
step 36: calculating the projection length of a first connecting line on the normal vector of the current side plane, wherein the first connecting line is a connecting line of a first central point and a second central point, the first central point is the central point of a side plane corner point of a potential adjacent template of the current side plane, the second central point is the central point of the current side plane corner point of the current template, the current side plane of the current template corresponds to each side plane of the potential adjacent template, a plurality of first connecting lines are obtained, the minimum projection length is taken as the width of a beam of the current template in all the first connecting lines, the potential neighborhood side planes corresponding to the current side plane and the first connecting line with the minimum projection length are recorded, each side plane only carries out one beam size estimation,
calculating the average value of the height difference between the upper and lower corner points of the current side plane where the first connecting line is located and the upper and lower corner points of the side plane corresponding to the minimum projection length, taking the average value as the beam height between the current template and the neighborhood template for eliminating the thickness of the floor slab, completing the size estimation of the beam,
traversing the remaining side planes of the current template, estimating the beam sizes participated in by the remaining side planes,
and traversing the residual templates to complete the estimation of all beam sizes.
2. The method for estimating the size of a building form based on unmanned aerial vehicle photographing according to claim 1, wherein in step 1, an unmanned aerial vehicle with a five-eye tilt camera is used to perform tilt photographing on a form of a roof of a form building to obtain a form image.
3. The building template size estimation method based on unmanned aerial vehicle shooting according to claim 1, wherein in step 1, three-dimensional point cloud reconstruction is completed by adopting ContextCapture or Dajiang intelligent map software.
4. The unmanned aerial vehicle shooting-based building template size estimation method according to claim 1, wherein the specific implementation process of step 2 comprises the following steps:
step 21: obtaining a Liang Pingfa construction drawing of a template of a target building, wherein the Liang Pingfa construction drawing only comprises two types of components, namely a beam and a column;
step 22: in Liang Pingfa construction drawing, M points representing beam column components meeting the first condition are selected, Z-axis coordinate values of the M points are all assigned to 0, M is more than or equal to 3, the selected M points are all used as design drawing control points of template point cloud data,
condition one: the area surrounded by the selected beam column parts exceeds a preset threshold value and is not on the same straight line, and point clouds similar to the positions of the selected beam column parts can be found in the template point cloud data;
step 23: selecting N point clouds corresponding to the positions of the control points of the design drawing from the template point cloud data, wherein the selected N point clouds are used as scanning point cloud control points of the template point cloud data, and N=M;
step 24: calculating transformation matrixes of the control points of the design drawing and the control points of the scanning point cloud, and transforming the template point cloud data under the design coordinate system based on the transformation matrixes to obtain the template point cloud data under the design coordinate system;
step 25: in a Liang Pingfa construction drawing, filling each floor area by using a filling tool, and storing according to a preset file format, wherein a keyword of a filling pattern type is used as an index, and plane coordinates of corner points of each floor area, namely the corner points of the floor area are the vertexes of a polygon formed by floor outlines, are stored;
step 26: based on the angle coordinates of the filled floor areas, establishing a corresponding surrounding frame of an XY plane for each floor area, wherein the surrounding frame of the XY plane surrounds the outline of the main floor area, and amplifying the surrounding frame;
step 27: and screening out the template point cloud data under the design coordinate system in the amplified bounding box through the amplified bounding box to obtain the template point cloud data divided according to the floor slab.
5. The unmanned aerial vehicle photographing-based building template size estimation method according to claim 4, wherein in step 26, the bounding box is enlarged in a proportion of 1.02.
6. The method for estimating the size of a building template based on unmanned aerial vehicle shooting according to claim 1, wherein in step 31, after the first fitting of the plane, the point cloud located at the upper part of the first fitted plane is deleted.
7. The unmanned aerial vehicle shooting-based building template size estimation method according to claim 1, wherein the point cloud preset threshold is 100.
8. The unmanned aerial vehicle photographing-based building template size estimation method according to claim 1, further comprising step 4,
step 4: and comparing the error between the estimated size and the designed size of the beam-column component, and retesting and correcting the size of the beam-column component of the actual template based on the error.
9. The unmanned aerial vehicle shooting-based building template size estimation method according to claim 8, wherein the comparing the estimated errors of the size and the design size of the beam-column components comprises the following steps:
step 41: in Liang Pingfa construction drawing, obtaining the design size of the beam column part based on in-situ labeling information;
step 42: the design size of the beam column part in the Liang Pingfa construction drawing is moved to the corresponding beam column part, and the size and the coordinates of the beam column part in the Liang Pingfa construction drawing are stored in a DXF file format and extracted through keyword searching;
step 43: and extracting the dimension label closest to the side plane according to the center point of the side plane and the side plane equation of the estimated dimension of the beam column part, and taking the extracted dimension label as the corresponding design dimension, thereby calculating the error between the estimated dimension of the beam column part and the design dimension of the beam column part.
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