阅读说明：本技术 一种隧道成型质量及其缺陷的定量化检测方法及系统 (Quantitative detection method and system for tunnel forming quality and defects thereof ) 是由 夏毅敏 郭子泺 程永亮 邓朝辉 李清友 龙斌 徐震 姚捷 于 2019-10-30 设计创作，主要内容包括：本发明公开了一种隧道成型质量及其缺陷的定量化检测方法及系统,包括如下步骤：使用一台激光扫描仪和一部相机采集隧道点云数据和隧道图像；对点云数据和隧道图像进行预处理,并将其转换到同一大地坐标系下；进行点云的三维重构,并计算中轴线；识别图像中的隧道表面缺陷；使用点云数据对隧道缺陷进行定量计算：隧道裂缝深度、隧道管片错台量、隧道超欠挖量等；将隧道表面缺陷的检测结果与点云重构模型相融合。本发明降低施工人员劳动量,提高施工效率,为实现更精确、更高效、更智能的隧道缺陷检测提供一种新的思路和方法。(The invention discloses a quantitative detection method and a system for tunnel forming quality and defects thereof, which comprises the following steps: collecting tunnel point cloud data and a tunnel image by using a laser scanner and a camera; preprocessing point cloud data and a tunnel image, and converting the point cloud data and the tunnel image into the same geodetic coordinate system; performing three-dimensional reconstruction of the point cloud, and calculating a central axis; identifying tunnel surface defects in the image; and (3) quantitatively calculating the tunnel defects by using point cloud data: the depth of a tunnel crack, the dislocation quantity of tunnel segments, the overbreak and underexcavation quantity of the tunnel and the like; and fusing the detection result of the tunnel surface defect with the point cloud reconstruction model. The invention reduces the labor amount of constructors, improves the construction efficiency and provides a new idea and method for realizing more accurate, more efficient and more intelligent tunnel defect detection.)

1. A quantitative detection method for tunnel forming quality and defects thereof is characterized by comprising the following steps:

the method comprises the following steps: the method comprises the following steps that a laser scanner and a camera respectively acquire tunnel three-dimensional point cloud data and a tunnel surface image through a rotating platform;

step two: respectively preprocessing the tunnel point cloud data and the tunnel surface image;

step three: respectively converting the acquired tunnel point cloud data and the tunnel surface image into the same geodetic coordinate system;

step four: performing three-dimensional reconstruction on the tunnel point cloud data;

step five: calculating the central axis of the tunnel according to the tunnel point cloud data under the geodetic coordinate system;

step six: processing the tunnel surface image, identifying defects such as tunnel cracks, water seepage and the like, and calculating the position of the tunnel surface defect in the whole geodetic coordinate system;

step seven: according to the position of the tunnel defect, the three-dimensional point cloud data is used for quantitatively calculating the crack depth, the tunnel segment dislocation amount, the tunnel overbreak and underexcavation amount and the like in the tunnel defect;

step eight: and fusing the detection result of the tunnel surface defect with the reconstruction model.

2. The quantitative detection method for tunnel forming quality and defects thereof according to claim 1, wherein the camera in step three converts an image coordinate system to a tunnel geodetic coordinate system according to position information when the image is taken; the laser scanner firstly obtains three-dimensional point cloud data of a ring of tunnels, and then converts the three-dimensional point cloud data into a geodetic coordinate system which is the same as that of the camera according to the position of the tunnel where the laser scanner is located.

3. The quantitative detection method for tunnel forming quality and defects thereof according to claim 1, wherein a Poisson surface reconstruction method is applied in the fourth step to perform three-dimensional reconstruction on tunnel point cloud data, and a tunnel three-dimensional model is established to achieve a visualization effect.

4. The method as claimed in claim 1, wherein in the sixth step, tunnel defects are identified for each of the acquired images, and if tunnel defects are identified in the images, the positions of the images in the geodetic coordinate system are located.

5. The quantitative detection method for tunnel forming quality and defects thereof according to claim 1, characterized in that, in the seventh step, tunnel defect type identification is performed according to tunnel type, if the tunnel is a TBM tunnel, tunnel cracks in the image are identified, the tunnel crack range of the tunnel point cloud under the geodetic coordinate system is intercepted, the actual radius distance from each point to the central axis of the tunnel is calculated in the range, the maximum depth of the crack is calculated by subtracting the theoretical radius from the actual radius of the tunnel, and the overexcavation amount is calculated;

if the tunnel is a shield tunnel, identifying and positioning a segment joint in the image, picking up each point on two sides of the segment joint, calculating the distance between the two points and the central axis of the tunnel, and calculating the difference value of the two distances, wherein the difference value is the slab staggering amount of the segment of the tunnel.

6. The quantitative detection method for tunnel forming quality and defects thereof according to claim 1, wherein in step eight, tunnel defects in the image are visualized in a tunnel three-dimensional model: splicing images of each tunnel defect in the tunnel, and inserting the images into a tunnel three-dimensional model according to the coordinates of each tunnel defect to realize fusion of the detection result of the tunnel surface defect and a reconstructed model; marking quantitative information of the tunnel defect position in the three-dimensional model, wherein the quantitative information comprises the following steps: the crack characteristic of the tunnel, the slab staggering quantity of the tunnel segments, the over-short excavation quantity of the tunnel and the like.

7. A quantitative detection system for tunnel forming quality and defects thereof is characterized by comprising the following parts:

a vehicle body for carrying equipment;

a stepper motor for providing power;

a height adjusting device for adjusting the height of the rotating platform;

the laser scanner is used for acquiring point cloud data;

a camera for acquiring a tunnel image;

the rotary platform is used for driving the laser scanner and the camera to move;

and the industrial personal computer is used for processing data.

8. The quantitative detection system for tunnel forming quality and defects thereof according to claim 7, wherein the wheels of the vehicle body can be replaced according to the type of the detected tunnel, and if the detected tunnel is a rail tunnel, the wheels are rail wheels; if the detected tunnel is a road tunnel, the wheel is an automobile tire.

9. The quantitative detection system for tunnel forming quality and defects thereof according to claim 7, wherein the height adjustment device body is a hydraulic cylinder, and the height of the rotary platform is adjusted according to different tunnel section sizes, so that the height of the rotary platform is at the center of the tunnel section.

10. The quantitative detection system for tunnel forming quality and defects thereof according to claim 7, wherein the rotating platform drives the laser scanner and the camera to rotate 360 degrees, when the rotating platform works, the camera continuously shoots images on the surface of the tunnel, the overlapping area of two adjacent images is more than 50%, and the shooting frequency is determined by the overlapping area of the images; and converting the point cloud data acquired by the laser scanner into three-dimensional point cloud data according to the information of a rotary encoder in the rotary platform, and recording the angle of the tunnel section where all images are shot by the camera.

Technical Field

The invention belongs to the field of tunnel detection, and particularly relates to a quantitative detection method and system for tunnel forming quality and defects thereof.

Background

The built and used tunnels in China are more and more, the daily maintenance and detection work of the tunnels is more and more, and along with the operation of the tunnels, the tunnel structure has the tunnel defects of cracks, slab staggering, water seepage and the like easily, so that the structural performance and the safety of the tunnels are seriously influenced. For tunnel defect detection, the traditional manual measurement adopts means such as visual measurement and a total station, the defects of high detection working intensity, low efficiency, insufficient precision and the like exist, and the normal operation of the tunnel is seriously influenced.

Therefore, a simple, convenient, efficient and high-precision method and system for detecting tunnel defects are needed.

Disclosure of Invention

The invention aims to provide a quantitative detection method and a system for tunnel forming quality and defects thereof, wherein images are acquired through a camera, and the types and positions of the defects on the surface of a tunnel are identified; and acquiring point cloud data through a laser scanner, establishing a three-dimensional model of the tunnel and quantitatively calculating defects of the tunnel.

In order to achieve the aim, the invention provides a quantitative detection method for tunnel forming quality and defects thereof, which comprises the following steps:

the method comprises the following steps: the method comprises the following steps that a laser scanner and a camera respectively acquire tunnel three-dimensional point cloud data and a tunnel surface image through a rotating platform;

step two: respectively preprocessing the tunnel point cloud data and the tunnel surface image;

step three: respectively converting the acquired tunnel point cloud data and the tunnel surface image into the same geodetic coordinate system;

step four: performing three-dimensional reconstruction on the tunnel point cloud data;

step five: calculating the central axis of the tunnel according to the tunnel point cloud data under the geodetic coordinate system;

step six: processing the tunnel surface image, identifying defects such as tunnel cracks, water seepage and the like, and calculating the position of the tunnel surface defect in the whole geodetic coordinate system;

step seven: according to the position of the tunnel defect, the three-dimensional point cloud data is used for quantitatively calculating the crack depth, the tunnel segment dislocation amount, the tunnel overbreak and underexcavation amount and the like in the tunnel defect;

step eight: and fusing the detection result of the tunnel surface defect with the reconstruction model.

The method comprises the steps of positioning a detection vehicle by using a total station before the detection vehicle enters a tunnel, setting the center of a section where the tunnel starts as the origin of a tunnel geodetic coordinate system, setting the section where the tunnel starts as an XOY surface, setting the horizontal direction as an X axis, setting the vertical direction as a Y axis, and setting the forward direction of the tunnel as a Z axis.

When the camera acquires the images in the first step, the overlapping area of two adjacent images is required to be more than 50%, and the precision of the images in three-dimensional reconstruction is ensured.

And in the second step, point cloud preprocessing such as denoising, sequencing and matching is carried out on the tunnel three-dimensional point cloud data, and image preprocessing such as graying and denoising is carried out on the shot image.

In the third step, according to the position information when the camera shoots the image, the tunnel surface characteristics in each image are converted into a tunnel geodetic coordinate system; the laser scanner firstly obtains three-dimensional point cloud data of a ring of tunnels, and then converts the three-dimensional point cloud data into a geodetic coordinate system which is the same as that of the camera according to the position of the tunnel where the laser scanner is located.

And in the fourth step, the tunnel point cloud data is subjected to three-dimensional reconstruction by using a Poisson curved surface reconstruction method, and a tunnel three-dimensional model is established, so that the visualization effect is achieved.

And fifthly, projecting the tunnel three-dimensional point cloud data onto an XOZ surface and a YOZ surface, extracting boundary lines of the point cloud of the XOZ surface and the point cloud of the YOZ surface, calculating center lines of the boundary lines in the two planes, and calculating to obtain a central axis of the whole tunnel by combining coordinates of the two center lines in the respective planes.

And sixthly, identifying tunnel defects of each acquired image, and if the tunnel defects are identified in the images, positioning the positions of the tunnel defects in the images under a geodetic coordinate system.

Identifying the defect type of the tunnel according to the type of the tunnel, identifying the tunnel crack in the image if the tunnel is a TBM tunnel, intercepting the range of the tunnel crack of the point cloud of the tunnel under a geodetic coordinate system, calculating the actual radius distance from each point to the central axis of the tunnel in the range, calculating the maximum depth of the crack by subtracting the theoretical radius from the actual radius of the tunnel, and calculating the overexcavation amount of the crack;

if the tunnel is a shield tunnel, identifying and positioning a segment joint in the image, picking up each point on two sides of the segment joint, calculating the distance between the two points and the central axis of the tunnel, and calculating the difference value of the two distances, wherein the difference value is the slab staggering amount of the segment of the tunnel.

Step eight, performing visualization processing on the tunnel defects in the image in a tunnel three-dimensional model: splicing images of each tunnel defect in the tunnel, and inserting the images into a tunnel three-dimensional model according to the coordinates of each tunnel defect to realize fusion of the detection result of the tunnel surface defect and a reconstructed model; marking quantitative information of the tunnel defect position in the three-dimensional model, wherein the quantitative information comprises the following steps: the crack characteristic of the tunnel, the slab staggering quantity of the tunnel segments, the over-short excavation quantity of the tunnel and the like.

The invention also provides a quantitative detection system for tunnel forming quality and defects thereof, which comprises:

a vehicle body for carrying equipment;

a stepper motor for providing power;

a height adjusting device for adjusting the height of the rotating platform;

the laser scanner is used for acquiring point cloud data;

a camera for acquiring a tunnel image;

the rotary platform is used for driving the laser scanner and the camera to move;

and the industrial personal computer is used for processing data.

The wheels of the vehicle body can be replaced according to the type of the detected tunnel, and if the detected tunnel is a track tunnel, the wheels are track wheels; if the detected tunnel is a road tunnel, the wheel is an automobile tire.

The wheels are driven by a stepping motor, a rotary encoder is arranged on a wheel shaft, the advancing distance of the detection vehicle is calculated through the rotary encoder, and the position of the tunnel where the detection vehicle is located is determined; and converting the point cloud data and the image acquired each time into a geodetic coordinate system according to the position of the tunnel where the detection vehicle is located.

The height adjusting device main body is a hydraulic cylinder, and the height of the rotating platform is adjusted according to different tunnel section sizes, so that the height of the rotating platform is positioned at the center of the tunnel section.

The rotating platform drives the laser scanner and the camera to rotate for 360 degrees, when the rotating platform works, the camera continuously shoots images on the surface of the tunnel, the overlapping area of two adjacent images is more than 50%, and the shooting frequency is determined by the image overlapping area; and converting the point cloud data acquired by the laser scanner into three-dimensional point cloud data according to the information of a rotary encoder in the rotary platform, and recording the angle of the tunnel section where all images are shot by the camera.

The invention provides a quantitative detection method and a system for tunnel forming quality and defects thereof.

Drawings

FIG. 1 is a schematic flow chart of a quantitative detection method for tunnel forming quality and defects thereof according to the present invention;

FIG. 2 is a schematic structural diagram of a quantitative detection system for tunnel forming quality and defects thereof according to the present invention;

description of the reference numerals

1 vehicle body 2 stepping motor

3 wheel 4 height adjusting device

5 laser scanner 6 camera

7 rotating platform 8 industrial control computer

Detailed description of the invention

Embodiments and specific systems of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the embodiments and specific systems described herein are only for purposes of illustrating and explaining the present invention and are not intended to limit the present invention.

Fig. 1 is a schematic flow chart of a quantitative detection method for tunnel forming quality and defects thereof according to the present invention, as shown in fig. 1, the method may include the following steps:

the method comprises the following steps: the method comprises the following steps that a laser scanner and a camera respectively acquire tunnel three-dimensional point cloud data and a tunnel surface image through a rotating platform;

step two: respectively preprocessing the tunnel point cloud data and the tunnel surface image;

step three: respectively converting the acquired tunnel point cloud data and the tunnel surface image into the same geodetic coordinate system;

step four: performing three-dimensional reconstruction on the tunnel point cloud data;

step five: calculating the central axis of the tunnel according to the tunnel point cloud data under the geodetic coordinate system;

step six: processing the tunnel surface image, identifying defects such as tunnel cracks, water seepage and the like, and calculating the position of the tunnel surface defect in the whole geodetic coordinate system;

step seven: according to the position of the tunnel defect, the three-dimensional point cloud data is used for quantitatively calculating the crack depth, the tunnel segment dislocation amount, the tunnel overbreak and underexcavation amount and the like in the tunnel defect;

step eight: and fusing the detection result of the tunnel surface defect with the reconstruction model.

The method comprises the steps of positioning a detection vehicle by using a total station before the detection vehicle enters a tunnel, setting the center of a section where the tunnel starts as the origin of a tunnel geodetic coordinate system, setting the section where the tunnel starts as an XOY surface, setting the horizontal direction as an X axis, setting the vertical direction as a Y axis, and setting the forward direction of the tunnel as a Z axis.

When the camera acquires the images in the first step, the overlapping area of two adjacent images is required to be more than 50%, and the precision of the images in three-dimensional reconstruction is ensured.

And in the second step, point cloud preprocessing such as denoising, sequencing and matching is carried out on the tunnel three-dimensional point cloud data, and image preprocessing such as graying and denoising is carried out on the shot image.

In the third step, according to the position information when the camera shoots the image, the tunnel surface characteristics in each image are converted into a tunnel geodetic coordinate system; the laser scanner firstly obtains three-dimensional point cloud data of a ring of tunnels, and then converts the three-dimensional point cloud data into a geodetic coordinate system which is the same as that of the camera according to the position of the tunnel where the laser scanner is located.

And in the fourth step, the tunnel point cloud data is subjected to three-dimensional reconstruction by using a Poisson curved surface reconstruction method, and a tunnel three-dimensional model is established, so that the visualization effect is achieved.

And fifthly, projecting the tunnel three-dimensional point cloud data onto an XOZ surface and a YOZ surface, extracting boundary lines of the point cloud of the XOZ surface and the point cloud of the YOZ surface, calculating center lines of the boundary lines in the two planes, and calculating to obtain a central axis of the whole tunnel by combining coordinates of the two center lines in the respective planes.

And sixthly, identifying tunnel defects of each acquired image, and if the tunnel defects are identified in the images, positioning the positions of the tunnel defects in the images under a geodetic coordinate system.

Identifying the defect type of the tunnel according to the type of the tunnel, identifying the tunnel crack in the image if the tunnel is a TBM tunnel, intercepting the range of the tunnel crack of the point cloud of the tunnel under a geodetic coordinate system, calculating the actual radius distance from each point to the central axis of the tunnel in the range, calculating the maximum depth of the crack by subtracting the theoretical radius from the actual radius of the tunnel, and calculating the overexcavation amount of the crack;

if the tunnel is a shield tunnel, identifying and positioning a segment joint in the image, picking up each point on two sides of the segment joint, calculating the distance between the two points and the central axis of the tunnel, and calculating the difference value of the two distances, wherein the difference value is the slab staggering amount of the segment of the tunnel.

Step eight, performing visualization processing on the tunnel defects in the image in a tunnel three-dimensional model: splicing images of each tunnel defect in the tunnel, and inserting the images into a tunnel three-dimensional model according to the coordinates of each tunnel defect to realize fusion of the detection result of the tunnel surface defect and a reconstructed model; marking quantitative information of the tunnel defect position in the three-dimensional model, wherein the quantitative information comprises the following steps: the crack characteristic of the tunnel, the slab staggering quantity of the tunnel segments, the over-short excavation quantity of the tunnel and the like.

Fig. 2 is a schematic structural diagram of a quantitative detection system for tunnel forming quality and defects thereof according to the present invention, as shown in fig. 2, the apparatus includes: a vehicle body 1 for carrying equipment; a stepping motor 2 for providing power; a wheel 3 for detecting the movement of the vehicle; a height adjusting device 4 for adjusting the height of the rotating platform; a laser scanner 5 for acquiring point cloud data; a camera 6 for acquiring tunnel images; a rotating platform 7 for driving the laser scanner 5 and the camera 6 to move; and the industrial personal computer 8 is used for processing data.

The wheels 3 of the vehicle body 1 can be replaced according to the type of the detected tunnel, if the detected tunnel is a track tunnel, the wheels 3 are track wheels, and the track is a material transport vehicle track laid by the development machine during construction; if the detected tunnel is a road tunnel, the wheel 3 is an automobile tire.

The wheels 3 are driven by the stepping motor 2, a rotary encoder is arranged on a wheel shaft, the advancing distance of the detection vehicle is calculated through the rotary encoder, and the position of the tunnel where the detection vehicle is located is determined; and converting the point cloud data and the image acquired each time into a geodetic coordinate system according to the position of the tunnel where the detection vehicle is located.

The height adjusting device 4 is mainly a hydraulic cylinder, and adjusts the height of the rotating platform 7 according to different tunnel section sizes, so that the height of the rotating platform 7 is positioned at the center of the tunnel section.

The rotary platform 7 is fixed on the height adjusting device 4 through a cantilever beam, the rotary platform 7 extends out of the rear part of the vehicle body 1, a laser scanner 5 and a camera 6 are installed on the rotary platform 7, the laser scanner 5 and the camera 6 on the rotary platform 7 are away from the vehicle body 1 on a horizontal plane by a certain distance, and the vehicle body 1 is prevented from influencing the laser scanner 5 and the camera 6 to acquire point cloud data and images of the bottom of the tunnel; the rotating platform 7 drives the laser scanner 5 and the camera 6 to rotate for 360 degrees, when the rotating platform works, the camera 6 continuously shoots images on the surface of the tunnel, the overlapping area of two adjacent images is more than 50%, and the shooting frequency is determined by the image overlapping area; according to the information of a rotary encoder in the rotary platform 7, point cloud data acquired by the laser scanner 5 is converted into three-dimensional point cloud data, and the angle of the tunnel section where each image is shot by the camera 6 is recorded.

The preferred method and system of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the scope of the present invention.