CN112686877A - Binocular camera-based three-dimensional house damage model construction and measurement method and system - Google Patents
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Abstract
The invention discloses a binocular camera-based three-dimensional house damage model construction and measurement method and system, which utilize the principle of binocular camera imaging parallax, obtain a house indoor plane image through scanning of a binocular camera, and calculate according to a binocular stereo matching algorithm to obtain a depth image; further obtaining the actual size information of each position and each component of the house structure, and realizing non-contact high-precision three-dimensional measurement and nondestructive detection on the house structure; then, a damage recognition and segmentation algorithm is adopted to segment the crack damage from the house indoor plane image of the left eye camera in the binocular cameras; projecting the pixel points obtained by segmentation onto a depth map by adopting a 3D damage refinement and quantization algorithm, so as to obtain three-dimensional information of each point in the crack damage; and the 3D Convex hull Convex hull algorithm is used for calculating the three-dimensional volume of the crack damage, so that the actual data of the house damage can be directly obtained, and the rapid detection and safety evaluation after the disaster of the large-scale urban building group are facilitated.
Description
Technical Field
The invention relates to the technical field of house measurement.
Background
In daily use, due to accidental or potential events such as fire, earthquake and the like, the civil building often causes the conditions of cracks, steel bar corrosion, concrete falling and the like of the building, and the conditions can influence the normal use of the building and even endanger the safety of the building structure. The house quality monitoring means that a professional carries out daily inspection and measurement on the house structure quality by using a certain technical means and method to carry out regular monitoring. The house quality detection means that a professional detects and evaluates damage of a house and reports the quality.
The current house quality monitoring and detecting contents mainly include structure settlement, inclination, cracks, material strength detection, house shock resistance detection and identification and the like, main technical equipment of the house quality monitoring and detecting method comprises a distance meter, a box ruler, a vernier caliper, a total station, a steel bar detector, a resiliometer and the like, the detecting equipment and the detecting method are low in detecting precision, low in speed and high in cost and cannot realize long-time monitoring, on the other hand, the whole detecting program is complex, the detecting result is often judged by depending on experience, and the detecting result is easily influenced by human factors. Therefore, how to accurately and quickly carry out daily monitoring and detection on the quality and the damage of the house, and quick detection and safety assessment after disasters of large-scale urban building groups become a problem to be solved urgently.
Disclosure of Invention
The invention aims to overcome the technical defects, provides a binocular camera-based three-dimensional house damage model construction and measurement method and system, and solves the problems that the existing house detection is low in precision, low in efficiency and complex in program, and large-scale building groups cannot be rapidly detected and safety evaluated.
In order to achieve the technical purpose, a first aspect of the technical solution of the present invention provides a method for constructing and measuring a three-dimensional house damage model based on a binocular camera, which includes the following steps:
scanning by a binocular camera to obtain a house indoor plane image, and calculating according to a binocular stereo matching algorithm to obtain a depth image;
dividing crack damage from the house indoor plane image of the left eye camera in the binocular cameras by adopting a damage identification and division algorithm;
projecting the pixel points obtained by segmentation onto a depth map by adopting a 3D damage refinement and quantization algorithm, so as to obtain three-dimensional information of each point in the crack damage;
and performing three-dimensional volume calculation on the fracture damage by using a 3D Convex hull Convex hull algorithm.
The invention provides a binocular camera-based three-dimensional house damage model construction and measurement system, which comprises the following functional modules:
the image acquisition and calculation module is used for obtaining an indoor plane image of the house through scanning of a binocular camera and calculating a depth image according to a binocular stereo matching algorithm;
the crack damage segmentation module is used for segmenting the crack damage from the house indoor plane image of the left-eye camera in the binocular camera by adopting a damage identification segmentation algorithm;
the depth projection module is used for projecting the segmented pixel points onto a depth map by adopting a 3D damage refinement quantization algorithm, so that the three-dimensional information of each point in the crack damage can be obtained;
and the damage volume calculation module is used for performing three-dimensional volume calculation on the crack damage by using a 3D Convex hull Convex hull algorithm.
A third aspect of the present invention provides a server, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the binocular camera-based three-dimensional house damage model building and measuring method when executing the computer program.
A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-mentioned steps of the binocular camera-based three-dimensional house damage model construction measurement method.
Compared with the prior art, the method utilizes the binocular camera imaging parallax principle to calculate and obtain the depth map of the house, further obtain the actual size information of each position and each component of the house structure, realize the non-contact high-precision three-dimensional measurement and nondestructive detection of the house structure, and has high detection efficiency; meanwhile, crack damage is segmented from the house indoor plane image of the left-eye camera in the binocular camera, the crack damage is projected to the 3D view from the 2D view, the three-dimensional volume of the crack damage is calculated by using a 3D Convex hull Convex hull algorithm, and actual data of the house damage can be directly obtained, so that rapid detection and safety evaluation after a large-scale urban building group is in danger are facilitated.
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Fig. 1 is a flow chart of a binocular camera-based three-dimensional house damage model construction measurement method according to an embodiment of the present invention;
fig. 2 is a block diagram of a binocular camera-based three-dimensional house damage model construction measurement system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, an embodiment of the present invention provides a binocular camera-based three-dimensional house damage model building and measuring method, which includes the following steps:
and S1, scanning through a binocular camera to obtain a house indoor plane image, and calculating according to a binocular stereo matching algorithm to obtain a depth image.
Based on the house indoor plane image, an indoor three-dimensional reconstruction model is established by using a three-dimensional modeling module, all bearing components and non-bearing components are subjected to instance segmentation in three-dimensional point cloud data by using a point cloud instance segmentation algorithm, and different component types are segmented and positioned.
Specifically, internal parameter information of a camera in a binocular camera is obtained, and distortion correction is performed on an image obtained by the camera according to the internal parameter information of the camera; the camera coordinate system can be corrected through the IMU (inertial sensor), unknown rigid body transformation of the IMU and the camera can be obtained through calibrating the initial poses of the IMU and the camera, and real-time image stabilization can be better performed by utilizing IMU feedback data. And calibrating according to the three coordinate systems in the camera calibration process, and solving the relation between every two coordinate systems. Which are the image coordinate system, the camera coordinate system and the world coordinate system, respectively. And calibrating the camera by using a checkerboard calibration method, and solving the internal parameters and the external parameters of the camera. And taking the external parameters as coordinate values of data points acquired by the camera under a world coordinate system, and using the internal parameters for solving a homography matrix in an image stabilizing process to finish the calibration of the relative pose of the IMU-camera.
And then, scanning the indoor scene by 360 degrees through a binocular camera to obtain a house indoor plane image and point cloud data, filtering the point cloud data based on a Static Outer Removal (SOR) filter, constructing by using an RTAB-map robot mapping model of an SDK library to obtain an indoor three-dimensional reconstruction model, and measuring the actual size information of the indoor structural member in real time through the indoor three-dimensional reconstruction model to replace a contact type measuring method used in the existing house quality detection.
The method is characterized in that a pointent + + training network is adopted to identify bearing components and non-bearing components, and different components are respectively segmented and displayed in three-dimensional point cloud data through different colors, so that segmentation and positioning of different component types are realized.
In order to simulate the capturing of human eyes on a stereoscopic scene and the recognition capability of different scenes, a binocular stereo matching algorithm (SGBM algorithm) requires that two cameras are adopted to replace human eyes, and two images which are very close to each other are obtained to obtain the depth of field (parallax), so that the distances between different scenes and the cameras are calculated, and a depth of field image is obtained. The invention adopts a binocular camera to replace human eyes, namely when two cameras on the same horizontal line shoot, the same object is shot in the two cameras, the object has different coordinates relative to the center point of the cameras in the two cameras, Xleft is the relative position of the object in the left camera, and Xright is the relative position of the object in the right camera. When two images are overlapped, the projection position of the P on the left video camera and the projection position of the P on the right video camera have a distance | Xleft | + | Xright |, the distance is called parallax error, based on the parallax error principle, the coordinate of a left eye camera in the binocular camera is used as an alignment coordinate, the depth z of the object P from the video camera can be obtained according to a similar triangle, and then a depth map is obtained. The calculation formula of the depth of field z is as follows:
z＝sf/d
wherein, S is the distance between two cameras, f is the focal length, d is the parallax Disparity (| Xleft | - | Xright |).
And S2, segmenting the crack damage from the house indoor plane image of the left eye camera in the binocular camera by adopting a damage identification segmentation algorithm.
Namely, the depth network Mask R-CNN is segmented from the house indoor plane image of the left eye camera in the binocular cameras.
And S3, projecting the segmented pixel points onto a depth map by adopting a 3D damage refinement and quantization algorithm, and acquiring the three-dimensional information of each point in the crack damage.
Specifically, because the depth map and the coordinates of the left eye camera are aligned when the SGBM stereo matching is performed, each coordinate can be added with the color of the left eye RGB, and one point of the obtained point cloud is described as [ X, Y, Z, R, G, B ].
And expressing the position of the matrix occupied forcibly by NAN by using the point cloud coordinates which can not be calculated by the points of the block with unsuccessful stereo matching, and aligning the 2D image coordinates with the 3D point cloud coordinates. Such as point cloud data, such as severely reflectorized, infinity, etc., which is actually noise.
The basis of the alignment of the 2D image coordinate and the 3D point cloud coordinate is that a disparity map is formed by selecting the disparity of each pixel point, a global energy function related to the disparity map is set, the energy function is minimized, and the purpose of solving the optimal disparity of each pixel is achieved, wherein the function expression is as follows:
wherein D represents a disparity map, and E (D) represents an energy function corresponding to the disparity map; p, q represents a certain pixel in the image; n is a radical ofpThe neighboring pixel points (typically 8 connected) of the representative pixel p; c (p, D)p) Means that the parallax of the current pixel point is DpThen, cost of the pixel point; p is a radical of1A penalty factor, which is applied to those pixels whose disparity values are different from the disparity value of p by 1 in the neighboring pixels of the pixel p; p is a radical of2A penalty factor is applied to the pixels with the disparity value of p different from that of p by more than 1 in the adjacent pixels of the pixel p; i.]Indicating that the function returns a1 if the parameter in the function is true, otherwise returns a 0.
After projection, Static Outer Remove (SOR) filter is used for filtering point cloud data, and therefore damage identification and segmentation in the 3D point cloud are completed.
In addition, the multidimensional size information can be accurately obtained by projecting the coordinate alignment to the 3D point cloud, and each point cloud has actual spatial coordinates (x, y, z), so that the length, width and height of the damage can be calculated by calculating the distance between two point clouds, for example, the distance D between the point cloud a1(x1, y1, z1) and the point cloud a2(x2, y2, z2) can be calculated by the following formula:
and S4, performing three-dimensional volume calculation on the fracture damage by using a 3D Convex hull Convex hull algorithm.
Before the three-dimensional volume calculation is carried out on the crack damage by using a 3D Convex hull Convex hull algorithm, interpolation is carried out on the extracted point cloud, the point cloud density is added, and downsampling and voxelization are carried out to simulate the geometrical form of the point cloud.
In the binocular scanning process, point clouds in some areas are not scanned or polluted by noise, some noise points are deleted after denoising, and lack of enough points can cause inaccuracy of volume calculation of a later-stage 3D covex hull, so that interpolation is performed between two adjacent points by using an interpolation method to add the point clouds;
compared with a projection or voxel segmentation method for processing point clouds, the method for directly extracting the characteristics of the point clouds can better reserve three-dimensional structure information, but due to the disorder of the point clouds, the direct processing method needs higher calculation cost when searching neighborhoods. Therefore, the point cloud is downsampled, the operation of all point clouds is converted to key points obtained by downsampling, and the calculated amount is reduced; particularly, a three-dimensional voxel grid is constructed by using FPS (fast point sampling), and then other points in the voxel are approximately displayed by using the gravity centers of all points in the voxel in each voxel, so that all points in the voxel are represented by using one gravity center point, and the filtering effect is achieved, and the data volume is greatly reduced.
The point cloud is converted into a three-dimensional voxel grid because the down-sampling is to construct the three-dimensional voxel grid, each point in the point cloud data has a voxel corresponding to the point cloud data in a three-dimensional space, and the voxelization of the point cloud data can be realized only by attaching a voxel label to each point.
Calculating the point cloud geometrical shape of the crack damage by using a 3D Convex hull Convex hull algorithm to obtain the three-dimensional volume of the crack damage, wherein the calculation formula is as follows:
in the formula, P represents a set of points in a real vector space; alpha represents the number of convex edges; n represents the number of points.
According to the binocular camera-based three-dimensional house damage model construction and measurement method, the binocular camera imaging parallax principle is utilized, the depth map of a house is obtained through calculation, further, the actual size information of each position and each component of the house structure is obtained, non-contact high-precision three-dimensional measurement and nondestructive detection of the house structure are achieved, and the detection efficiency is high; meanwhile, crack damage is segmented from the house indoor plane image of the left-eye camera in the binocular camera, the crack damage is projected to the 3D view from the 2D view, the three-dimensional volume of the crack damage is calculated by using a 3D Convex hull Convex hull algorithm, and actual data of the house damage can be directly obtained, so that rapid detection and safety evaluation after a large-scale urban building group is in danger are facilitated.
As shown in fig. 2, the embodiment of the invention also discloses a binocular camera-based three-dimensional house damage model construction and measurement system, which comprises the following functional modules:
the image acquisition and calculation module 10 is used for obtaining a house indoor plane image through binocular camera scanning and calculating to obtain a depth image according to a binocular stereo matching algorithm;
the crack damage segmentation module 20 is used for segmenting the crack damage from the house indoor plane image of the left-eye camera in the binocular camera by adopting a damage identification segmentation algorithm;
the depth projection module 30 is configured to project the segmented pixel points onto a depth map by using a 3D damage refinement quantization algorithm, so that three-dimensional information of each point in the crack damage can be obtained;
and the damage volume calculation module 40 is used for performing three-dimensional volume calculation on the fracture damage by using a 3D Convex hull Convex hull algorithm.
The execution mode of the binocular camera-based three-dimensional house damage model building and measuring system of the embodiment is basically the same as that of the binocular camera-based three-dimensional house damage model building and measuring method, and therefore detailed description is omitted.
The server in this embodiment is a device for providing computing services, and generally refers to a computer with high computing power, which is provided to a plurality of consumers via a network. The server of this embodiment includes: a memory including an executable program stored thereon, a processor, and a system bus, it will be understood by those skilled in the art that the terminal device structure of the present embodiment does not constitute a limitation of the terminal device, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
The memory may be used to store software programs and modules, and the processor may execute various functional applications of the terminal and data processing by operating the software programs and modules stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the terminal, etc. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The method comprises the steps of storing a executable program of a binocular camera-based three-dimensional house damage model building and measuring method on a memory, wherein the executable program can be divided into one or more modules/units, the one or more modules/units are stored in the memory and are executed by a processor to complete information acquisition and implementation processes, and the one or more modules/units can be a series of computer program instruction segments capable of completing specific functions and are used for describing the execution process of the computer program in the server. For example, the computer program may be segmented into an image acquisition calculation module, a fracture damage segmentation module, a depth projection module, a damage volume calculation module.
The processor is a control center of the server, connects various parts of the whole terminal equipment by various interfaces and lines, and executes various functions of the terminal and processes data by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, thereby performing overall monitoring of the terminal. Alternatively, the processor may include one or more processing units; preferably, the processor may integrate an application processor, which mainly handles operating systems, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The system bus is used to connect functional units in the computer, and can transmit data information, address information and control information, and the types of the functional units can be PCI bus, ISA bus, VESA bus, etc. The system bus is responsible for data and instruction interaction between the processor and the memory. Of course, the system bus may also access other devices such as network interfaces, display devices, etc.
The server at least includes a CPU, a chipset, a memory, a disk system, and the like, and other components are not described herein again.
In the embodiment of the present invention, the executable program executed by the processor included in the terminal specifically includes: a binocular camera-based three-dimensional house damage model construction and measurement method comprises the following steps:
scanning by a binocular camera to obtain a house indoor plane image, and calculating according to a binocular stereo matching algorithm to obtain a depth image;
dividing crack damage from the house indoor plane image of the left eye camera in the binocular cameras by adopting a damage identification and division algorithm;
projecting the pixel points obtained by segmentation onto a depth map by adopting a 3D damage refinement and quantization algorithm, so as to obtain three-dimensional information of each point in the crack damage;
and performing three-dimensional volume calculation on the fracture damage by using a 3D Convex hull Convex hull algorithm.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the modules, elements, and/or method steps of the various embodiments described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
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| CN118862506A (en) * | 2024-08-06 | 2024-10-29 | 兰州理工大学 | Multi-UAV collaborative post-earthquake building safety rapid assessment system and method |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101147685A (en) * | 1999-11-18 | 2008-03-26 | 罗切斯特大学 | Apparatus and method for forming cone beam volume computed tomography breast images of a patient's breast |
| CN103983228A (en) * | 2014-06-04 | 2014-08-13 | 长江工程地球物理勘测武汉有限公司 | Method for measuring borehole fracture attitude by use of electronic rock core |
| CN104792804A (en) * | 2015-03-27 | 2015-07-22 | 江苏大学 | Method for diagnosing internal damage of cereal grains based on micro-CT (computed tomography) technology |
| CN106023226A (en) * | 2016-05-31 | 2016-10-12 | 彭博 | Crack automatic detection method based on three-dimensional virtual pavement |
| CN106791773A (en) * | 2016-12-30 | 2017-05-31 | 浙江工业大学 | A kind of novel view synthesis method based on depth image |
| CN107179322A (en) * | 2017-06-15 | 2017-09-19 | 长安大学 | A kind of bridge bottom crack detection method based on binocular vision |
| US20180034038A1 (en) * | 2015-06-04 | 2018-02-01 | Eoplex Limited | Lead carrier structure and packages formed therefrom without die attach pads |
| CN109242855A (en) * | 2018-07-19 | 2019-01-18 | 中国科学院自动化研究所 | Roof dividing method, system and equipment based on Three-dimensional Multi-resolution statistical information |
| CN110349251A (en) * | 2019-06-28 | 2019-10-18 | 深圳数位传媒科技有限公司 | A kind of three-dimensional rebuilding method and device based on binocular camera |
| CN111537610A (en) * | 2020-05-15 | 2020-08-14 | 华东理工大学 | A sensor array optimization method for damage localization of metal curved plates |
| CN112102491A (en) * | 2020-08-12 | 2020-12-18 | 西安交通大学 | Skin damage surface three-dimensional reconstruction method based on surface structured light |
-
2021
- 2021-01-05 CN CN202110009707.9A patent/CN112686877B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101147685A (en) * | 1999-11-18 | 2008-03-26 | 罗切斯特大学 | Apparatus and method for forming cone beam volume computed tomography breast images of a patient's breast |
| CN103983228A (en) * | 2014-06-04 | 2014-08-13 | 长江工程地球物理勘测武汉有限公司 | Method for measuring borehole fracture attitude by use of electronic rock core |
| CN104792804A (en) * | 2015-03-27 | 2015-07-22 | 江苏大学 | Method for diagnosing internal damage of cereal grains based on micro-CT (computed tomography) technology |
| US20180034038A1 (en) * | 2015-06-04 | 2018-02-01 | Eoplex Limited | Lead carrier structure and packages formed therefrom without die attach pads |
| CN106023226A (en) * | 2016-05-31 | 2016-10-12 | 彭博 | Crack automatic detection method based on three-dimensional virtual pavement |
| CN106791773A (en) * | 2016-12-30 | 2017-05-31 | 浙江工业大学 | A kind of novel view synthesis method based on depth image |
| CN107179322A (en) * | 2017-06-15 | 2017-09-19 | 长安大学 | A kind of bridge bottom crack detection method based on binocular vision |
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