CN109373921B - Tunnel monitoring method and device - Google Patents
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Abstract
The application provides a tunnel monitoring and analyzing method and a device, wherein the method comprises the following steps: acquiring original section point cloud data of a tunnel; respectively performing point cloud splicing operation and reflectivity image generation operation according to the acquired data to generate a tunnel integral point cloud model and a tunnel pipe wall expansion diagram; carrying out binarization processing on the tunnel tube wall development image, carrying out parameter mapping on a binary image by using Hough transform, and extracting a straight line segment of the binary image within a preset angle range; extracting a single segment image in a tunnel tube wall expansion diagram according to the straight line segment, and respectively extracting sub-images of the top sealing block and the connecting blocks at two sides in the single segment image according to the single segment image; dividing the tunnel integral point cloud model into point cloud small block models by using the capping block, the connection block subimages at two sides and the pixel positions of the single segment images; and calculating the slab staggering height according to the fitting planes of the point cloud small block models and the distance between the fitting planes, and analyzing the deformation condition of the tunnel segment.
Description
Technical Field
The application relates to the field of computer data processing, in particular to a tunnel monitoring method and device.
Background
With the increasing mileage of urban rail transit, rail transit is more and more important for people's trip. Under the condition that the rail transit system is subjected to continuous deformation and accelerated aging of the tunnel, the daily maintenance of the tunnel system is very important. Through maintaining the tunnel system, not only can guarantee that the track traffic system supports the operation, can also greatly reduce the incident that causes because of the artificial carelessness.
The common daily maintenance items of the tunnel comprise deformation detection, platform staggering, block dropping, water leakage and the like. Because the local and integral dislocation of the duct piece is realized, the integral deformation of the duct piece is the direct cause of diseases such as block falling, water leakage and the like, and therefore, the health condition of the tunnel is usually monitored by monitoring the geometric deformation index.
Most tunnel monitoring means adopted by traditional tunnel image-based disease monitoring judge the health condition of a tunnel through macroscopic geometric deformation, local disease characteristics and the like, and can be detected only after diseases occur or are aggravated, so that the method is a passive monitoring method, the monitoring result is not fine enough, and a more refined and reasonable analysis method is urgently needed.
Disclosure of Invention
The embodiment of the application provides a tunnel monitoring method and device, which can improve the fineness of a tunnel monitoring result.
In one embodiment, a method of tunnel monitoring includes:
acquiring original section point cloud data of a tunnel;
respectively performing point cloud splicing operation and reflectivity image generation operation according to the collected original cross section point cloud data to generate a tunnel integral point cloud model and a tunnel pipe wall expansion diagram;
carrying out binarization processing on the tunnel tube wall development image to obtain a binary image, carrying out parameter mapping on the binary image by using Hough transform, and extracting a straight line segment of the binary image within a preset angle range;
extracting a single segment image in the tunnel tube wall expansion diagram according to the straight line segment, and respectively extracting a capping block sub-image and two side connecting block sub-images from the single segment image;
determining the position of a corresponding tunnel point cloud by using the top sealing block subimage, the two side connecting block subimages and the pixel position of the single segment image, and dividing the whole tunnel point cloud model into point cloud small block models;
calculating the slab staggering height according to the fitting planes of the point cloud small block models and the distance between the fitting planes, and determining the segment deformation condition of the tunnel according to the slab staggering height.
In one embodiment, the acquiring of the point cloud data of the original section of the tunnel is implemented by using a scanner, and specifically includes: determining the moving speed of the mobile platform and the scanning parameters of the scanner according to the preset monitoring precision; calibrating the scanner by using the calibration position; and when the mobile platform is moved, the scanner scans to acquire the point cloud data of the original section.
In one embodiment, according to the collected original cross-section point cloud data, performing point cloud registration operation and reflectivity image generation operation respectively to generate a tunnel integral point cloud model and a tunnel tube wall expansion map, including: and splicing the complete tunnel point cloud, and generating an expanded image of the tunnel tube wall by using the reflectivity data.
In one embodiment, extracting a single segment image in the tunnel tube wall expansion map from the straight segment includes: and removing redundant straight line segments by using prior information, and extracting a single segment image in the tunnel tube wall expansion map.
In one embodiment, the a priori information includes: the design width of the tube sheet and the average width of the tube gaps.
In one embodiment, the method further comprises: extracting standard block sub-images from the single duct piece image.
In one embodiment, the extracting of the capping block sub-image and the two-side connection block sub-image from the single segment image respectively includes:
matching the edge of a capping block in a single segment image with a capping block design template by adopting a function so as to determine the position of the capping block in the tunnel expansion image and extract a sub-image of the capping block at the position;
determining the positions of the connecting blocks on two sides and the positions of the standard blocks according to the positions of the capping blocks and the symmetry of the pipe piece, extracting sub-images of the connecting blocks on two sides of the positions according to the positions of the connecting blocks on two sides, and extracting sub-images of the standard blocks at the positions according to the positions of the standard blocks.
In one embodiment, the point cloud patch model comprises: the point cloud model of a single duct piece and the point cloud model of a single connecting block small block.
In one embodiment, the determining the position of the corresponding tunnel point cloud by using the pixel positions of the capping block sub-image, the two-side connecting block sub-image and the single segment image, and the segmenting the tunnel overall point cloud model into the point cloud small block model includes: and dividing the tunnel integral point cloud model into a point cloud model of a single connecting small block by using the capping block subimage and the connecting block subimages at two sides, and dividing the tunnel integral point cloud model into a point cloud model of a single segment according to the pixel position of the single segment image.
In one embodiment, a tunnel monitoring device includes: the device comprises an acquisition module, a data splicing module, a straight line and edge detection module, a duct piece and connecting block determination module, a point cloud segmentation module and a monitoring analysis module;
the acquisition module is used for acquiring point cloud data of an original section of the tunnel;
the data splicing module is used for respectively carrying out point cloud splicing operation and reflectivity image generation operation according to the collected original section point cloud data to generate a tunnel integral point cloud model and a tunnel pipe wall expansion diagram;
the straight line and edge detection module is used for carrying out binarization processing on the tunnel pipe wall development image to obtain a binary image, carrying out parameter mapping on the binary image by using Hough transform, and extracting a straight line segment of the binary image within a preset angle range;
the segment and connecting block determining module is used for extracting a single segment image in the tunnel tube wall expansion map according to the straight line segment, and extracting a capping block sub-image and connecting block sub-images on two sides from the single segment image respectively;
the point cloud segmentation module is used for determining the position of the corresponding tunnel point cloud by using the pixel positions of the capping block subimage, the two side connection subimages and the single segment image, and segmenting the whole tunnel point cloud model into a small point cloud model;
and the monitoring analysis module is used for calculating the slab staggering height according to the fitting planes of the point cloud small block models and the distance between the fitting planes and determining the segment deformation condition of the tunnel according to the slab staggering height.
In the embodiment, by using the tunnel monitoring method and device provided by the embodiment of the application, the point cloud segmentation of each connecting block in the tunnel segment can be realized by using the laser reflectivity image data, the slab staggering height of the local and overall tunnel segments is calculated, the overall deformation condition of the segments is analyzed according to the slab staggering height in a fine mode, and the fineness of the tunnel monitoring result is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a flow diagram of a tunnel monitoring method according to one embodiment of the present description;
FIG. 2 is a schematic diagram of a structure of a collecting apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a tunnel global point cloud model generated by joining section point cloud data in one embodiment of the present disclosure;
FIG. 4 is an expanded image of a tunnel wall generated from cloud reflectance intensity values at various points in one embodiment of the present description;
FIG. 5 is a schematic diagram of the structure of a single tube piece and each connecting block of a tunnel according to one embodiment of the present disclosure;
FIG. 6 is a schematic view of a segment and connection blocks extracted in one embodiment of the present disclosure;
FIG. 7 is a schematic diagram of a point cloud patch model after segmentation in an embodiment of the present disclosure;
FIG. 8 shows the dislocation height calculated from the fitting plane of the point cloud patch model according to one embodiment of the present disclosure;
fig. 9 is a block diagram of a tunnel monitoring apparatus according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions thereof in the present specification are provided to explain the present application, but are not intended to limit the present application.
Fig. 1 is a flow diagram of a tunnel monitoring method according to one embodiment of the present description. As shown in fig. 1, the tunnel monitoring method may include the following steps.
S101: and acquiring point cloud data of the original section of the tunnel.
The original cross-section point cloud data of the tunnel can be collected.
In one embodiment, the raw cross-section point cloud data may be acquired using a scanner. Fig. 2 is a schematic structural diagram of an acquisition device in an embodiment of the present invention. Referring to fig. 2, the scanner may be mounted on an acquisition device that also includes a mobile platform.
Specifically, the moving speed of the moving platform and the scanning parameters of the scanner can be determined according to the preset monitoring precision; calibrating the scanner by using the calibration position; and when the mobile platform is moved, the scanner scans to acquire the point cloud data of the original section.
Wherein, the monitoring precision can be the precision of the image picture. For example, it may be 2 mm, then the moving speed of the moving platform may be 0.25 m/s, the scanning resolution of the scanner may be set to 1/4, and the scanning quality may be set to: 3.
calibrating the scanner with a calibration position may include: the calibration object is placed at the scanning start position in advance. By calibrating the scanner, the point cloud data and the image data can be matched, and meanwhile, the method can be used for correcting the compression ratio of the data in the Y-axis direction.
In one embodiment, the acquired original cross-section point cloud data may be stored as a point cloud file of a preset size. For example, each point cloud file may be stored as one fls type file per 100 sections, i.e., 426800 points.
In one embodiment, the point cloud data comprises: 3 coordinate data relative to the center of a circle and 1 reflectivity intensity value. The profile data may be expressed as { X }t，Yt，Zt，ItIn which XtCan represent the X-axis coordinate value, YtCan represent a Y-axis coordinate value, ZtZ-axis coordinate values may be represented. I istThe reflectance intensity value may be represented.
S102: and respectively carrying out point cloud splicing operation and reflectivity image generation operation according to the collected original cross section point cloud data to generate a tunnel integral point cloud model and a tunnel pipe wall expansion diagram.
According to the moving speed V of the moving platformcNumber of point clouds N generated by the scanner per secondpAnd the number of point clouds C contained in a single sectionsingleAnd point cloud coordinates of the Y axis can be calculated. The point cloud coordinate of the Y axis can be calculated by adopting the following formula:
further, the cross sections can be spliced into a tunnel integral point cloud model and a tunnel pipe wall expansion diagram generated based on reflectivity according to the corresponding time stamp sequence of the cross sections.
Specifically, the complete tunnel point cloud can be spliced by using the point cloud coordinate data in the section data, and the expanded image of the tunnel tube wall can be generated by using the reflectivity data in the section data.
In one embodiment, the profile data { X } may bet，ZtAccording to the time sequence t1，t2，t3，…，tnAre arranged according to the calculated YtSplicing section data into complete tunnel point cloud { X1，Y1，Z1，X2，Y2，Z2，…，Xn，Yn，Zn}. Wherein the Y-axis values are continuously accumulated, i.e.: y is1＝Yt；Y2＝Y1+Yt；Yn＝Y1+Y2+…+Yn-1。
In one embodiment, the number of point clouds C contained in a single section may be determinedscAs the number of pixels of the image in the Y-axis direction, then, a single point cloud of a single section can be regarded as one pixel on the Y-axis, i.e., a single section { X }t，ZtA column of pixels constituting an image. The intensity value of a single pixel is ItThereby generating a frame Csc× W pixels, namely a tunnel tube wall expansion image, wherein the number W of the pixels in the X-axis direction can be selected according to actual requirements.
Fig. 3 is a schematic diagram of a tunnel whole point cloud model generated by joining section point cloud data in one embodiment of the present specification. FIG. 4 is an expanded image of the tunnel wall generated from the cloud reflectance intensity values at various points in one embodiment of the present description.
In another embodiment, the tunnel monitoring method may further include: and correcting the point cloud data to enable one point cloud to correspond to one pixel.
For example, assume that the total number of segments in the original data is WsIn the well spliced point cloud data, the total length of the tunnel is Ms. Then, before the generated original picture is interpolated, the size of the image should be Csc×WsI.e. with WsEach pixel is distributed in a length MsI.e. a single pixel in the X-axis direction needs to be stretched to MsThe length of/W pixels, which enables the three-dimensional point cloud and the two-dimensional image to be registered with each other.
In another embodiment, the tunnel monitoring method may further include: and carrying out interpolation processing on the corrected tunnel pipe wall expansion map.
S103: and carrying out binarization processing on the tunnel tube wall development image to obtain a binary image, carrying out parameter mapping on the binary image by using Hough transform, and extracting a straight line segment of the binary image within a preset angle range.
In one embodiment, the tunnel tube wall expansion map can be subjected to binarization processing by using a Canny edge detection operator. For example, the Canny edge detector's gaussian radius may be set to 2, the low threshold may be set to 40, and the high threshold may be set to 80.
The binary image after the binarization processing may be saved as:
Ed＝((x1,y1),(x2,y2),…,(xn,yn))
because of the existence of vertical or nearly vertical straight line segments in the binary image, we need to replace the traditional straight line equation with a polar parameter equation, and the parameter equation in the technology can adopt the following formula:
ρ＝x cosθ+y sinθ；
in the above equation, ρ represents the perpendicular distance from the origin to the straight line, and θ represents the angle of the x-axis to the straight line. Theta can be taken as a preset angle, and the value range of theta can be [ +10 degrees, -10 degrees ] and [ +80 degrees, -80 degrees ]. The horizontal straight line (or approaching the horizontal straight line), the vertical straight line, and the edge portion of the trapezoidal capping block (abbreviated as SF) in the binary image can be detected by the above formula, respectively.
By limiting the range of the polar coordinates within the above-mentioned preset angle range, the influence of noise data can be greatly reduced, and the monitoring efficiency can be improved.
S104: and extracting a single segment image in the tunnel tube wall expansion map according to the straight line segment, and respectively extracting a capping block sub-image and two side connecting block sub-images from the single segment image.
Redundant straight line segments can be removed by utilizing prior information, and a single segment image in the tunnel tube wall expansion map is determined.
In one embodiment, the design width of the tube sheet (abbreviated as d)cs) And the average width of the tube gap (abbreviated as g) as prior information.
Removing redundant straight line segments through prior information, and determining a single segment image in the tunnel tube wall expansion map, which may specifically include: screening out the tunnel pipe wall expansion map [ d ]cs-g,dcs+g]And the vertical line segment in the range is the position of a single segment, and the image of the position of the single segment on the tunnel tube wall expansion diagram is the image of the single segment.
In another embodiment, the single segment image may also be stored. For example, the single segment image may be stored as Is。
The capping block sub-image and the side connection block sub-images may be extracted from the single segment image, respectively.
In one embodiment, a sub-image of a standard block in the single segment image may also be extracted from the single segment image.
An optimization function may be employed to match the capped blocks in a single segment image to a capped block (SF) design template to determine the location of the capped blocks in the tunnel unwrapped image, and to extract a capped block sub-image at that location.
In particular, the design template of the capping block can be used as a template operator K of n × ndesignTaking a pixel block K of n × n in the picturehPerforming convolution operation with the template operator, traversing the whole tunnel tube wall expansion diagram, and determining the position of the capping block by using the following formula:
wherein the capping block template operator KdesignMay be an n × n size matrix containing [0,1 ]]Elements, where the edge portion may be represented by 1 and the remaining portion may be filled with 0.
Further, the positions of the connection blocks (S L1, S L2) and the standard blocks (SB1, SB2) on both sides can be determined according to the positions of the capping blocks (SF) and the symmetry of the segments, the sub-images of the connection blocks on both sides at the positions are extracted according to the positions of the connection blocks on both sides, and the sub-images of the standard blocks at the positions are extracted according to the positions of the standard blocks FIG. 5 is a schematic diagram of the structure of a single segment and each connection block of the tunnel in one embodiment of the present specification FIG. 5 includes the capping blocks (SF), the connection blocks (S L1, S L2), the standard blocks (SB1, SB2, GD) FIG. 6 is a schematic diagram of the segments and each connection block extracted in one embodiment of the present specification FIG. 6 includes the capping blocks (SF), the connection blocks (S L1, S L2), and the standard blocks (SB1, SB 2).
S105: and determining the position of the point cloud corresponding to the tunnel by using the top sealing block subimage, the two side connecting block subimages and the pixel position of the single segment image, and dividing the whole point cloud model of the tunnel into point cloud small block models.
The point cloud patch model may include: the point cloud model of a single segment and the point cloud model of a single connected small block.
Specifically, the point cloud model of the whole tunnel may be divided into point cloud models of single connected small blocks by using the capping block sub-image and the two-side connecting block sub-images, and the point cloud model of the whole tunnel may be divided into point cloud models of single duct pieces according to the pixel positions of the single duct piece image.
FIG. 7 is a diagram illustrating a point cloud small block model after segmentation in an embodiment of the present disclosure.
S106: calculating the slab staggering height according to the fitting planes of the point cloud small block models and the distance between the fitting planes, and determining the segment deformation condition of the tunnel according to the slab staggering height.
Specifically, the distance between the point cloud small block model and the point cloud plane around the point cloud small block model can be calculated according to the point cloud small block model fitting plane, and the tunnel segment deformation condition can be judged according to the slab staggering height. Thereby realize tunnel segment deformation monitoring.
Fig. 8 shows the dislocation height calculated according to the fitting plane of the point cloud small block model in one embodiment of the present disclosure.
From the above description, it can be seen that the embodiments of the present application achieve the following technical effects: the point cloud segmentation of each connecting block in the tunnel segment can be realized by utilizing laser reflectivity image data, the slab staggering height of the local and overall existence of the tunnel segment is calculated, the overall deformation condition of the segment is analyzed according to the slab staggering height, and the fineness of a tunnel monitoring result is improved.
Based on the same inventive concept, the embodiment of the present application further provides a tunnel monitoring device, as described in the following embodiments. Because the principle of the tunnel monitoring device for solving the problems is similar to that of the tunnel monitoring method, the implementation of the tunnel monitoring device can refer to the implementation of the tunnel monitoring method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Fig. 9 is a block diagram of a tunnel monitoring apparatus according to an embodiment of the present disclosure, and as shown in fig. 9, the tunnel monitoring apparatus may include: the system comprises an acquisition module 901, a data splicing module 902, a straight line and edge detection module 903, a segment and connecting block determination module 904, a point cloud segmentation module 905 and a monitoring analysis module 906.
The acquisition module 901 may be configured to acquire original cross-section point cloud data of a tunnel.
The data stitching module 902 may be configured to perform point cloud stitching operation and reflectance image generation operation respectively according to the acquired original cross-section point cloud data, so as to generate a tunnel integral point cloud model and a tunnel tube wall expansion map.
The straight line and edge detection module 903 may be configured to perform binarization processing on the tunnel tube wall development image to obtain a binary image, perform parameter mapping on the binary image by using hough transform, and extract a straight line segment of the binary image within a preset angle range.
The segment and connection block determining module 904 may be configured to extract a single segment image in the tunnel tube wall expansion map according to the straight line segment, and extract a capping block sub-image and two side connection block sub-images from the single segment image.
The point cloud segmentation module 905 may be configured to determine a position of a corresponding tunnel point cloud by using the pixel positions of the capping block sub-image, the two-side connection sub-images, and the single segment image, and segment the tunnel overall point cloud model into a point cloud small block model.
The monitoring and analyzing module 906 may be configured to calculate a slab staggering height according to a distance between fitting planes according to the point cloud small block model fitting planes, and determine a segment deformation condition of the tunnel according to the slab staggering height. Thereby realize tunnel segment deformation monitoring.
It will be apparent to those skilled in the art that the modules or steps of the embodiments described above in this specification can be implemented by a general purpose computing device, they can be centralized on a single computing device or distributed over a network of multiple computing devices, and alternatively they can be implemented by program code executable by a computing device, such that they can be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described can be executed out of order, or fabricated separately into individual integrated circuit modules, or multiple modules or steps of them can be fabricated into a single integrated circuit module. Thus, embodiments of the present description are not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and it will be apparent to those skilled in the art that various modifications and variations can be made in the embodiment of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method for monitoring a tunnel, comprising:
acquiring original section point cloud data of a tunnel;
generating a tunnel integral point cloud model by performing point cloud splicing operation according to the collected original section point cloud data, and generating a tunnel pipe wall expansion diagram by reflectivity image generation operation;
carrying out binarization processing on the tunnel tube wall development image to obtain a binary image, carrying out parameter mapping on the binary image by using Hough transform, and extracting a straight line segment of the binary image within a preset angle range;
extracting a single segment image in the tunnel tube wall expansion diagram according to the straight line segment, and respectively extracting a capping block sub-image and two side connecting block sub-images from the single segment image;
determining the position of a corresponding tunnel point cloud by using the top sealing block subimage, the two side connecting block subimages and the pixel position of the single segment image, and dividing the whole tunnel point cloud model into point cloud small block models;
calculating the slab staggering height according to the fitting planes of the point cloud small block models and the distance between the fitting planes, and determining the segment deformation condition of the tunnel according to the slab staggering height.
2. The tunnel monitoring method according to claim 1, wherein the acquiring of the point cloud data of the original section of the tunnel is implemented by using a scanner, and specifically comprises: determining the moving speed of the mobile platform and the scanning parameters of the scanner according to the preset monitoring precision; calibrating the scanner by using the calibration position; and when the mobile platform is moved, the scanner scans to acquire the point cloud data of the original section.
3. The tunnel monitoring method according to claim 1, wherein the steps of performing point cloud registration and reflectance image generation respectively according to the collected point cloud data of the original section to generate a tunnel integral point cloud model and a tunnel wall expansion map comprise: and splicing the complete tunnel point cloud, and generating an expanded image of the tunnel tube wall by using the reflectivity data.
4. The tunnel monitoring method of claim 1, wherein extracting a single segment image in the tunnel tube wall expansion map from the straight-line segment comprises: and removing redundant straight line segments by using prior information, and extracting a single segment image in the tunnel tube wall expansion map.
5. The method according to claim 4, wherein the prior information includes: the design width of the tube sheet and the average width of the tube gaps.
6. The method of claim 1, further comprising: extracting standard block sub-images from the single duct piece image.
7. The tunnel monitoring method of claim 1 or 6, wherein the extracting the capping block sub-image and the two-side connecting block sub-images from the single segment image respectively comprises:
matching the edge of a capping block in a single segment image with a capping block design template by adopting a function so as to determine the position of the capping block in the tunnel expansion image and extract a sub-image of the capping block at the position;
determining the positions of the connecting blocks on two sides and the positions of the standard blocks according to the positions of the capping blocks and the symmetry of the pipe piece, extracting sub-images of the connecting blocks on two sides of the positions according to the positions of the connecting blocks on two sides, and extracting sub-images of the standard blocks at the positions according to the positions of the standard blocks.
8. The tunnel monitoring method of claim 1, wherein the point cloud patch model comprises: the point cloud model of a single duct piece and the point cloud model of a single connecting block small block.
9. The method of claim 8, wherein the step of using the pixel locations of the capping block sub-image, the two-side connecting block sub-image and the single segment image to determine the location of the corresponding tunnel point cloud to segment the tunnel global point cloud model into point cloud small block models comprises: and dividing the tunnel integral point cloud model into a point cloud model of a single connecting small block by using the capping block subimage and the connecting block subimages at two sides, and dividing the tunnel integral point cloud model into a point cloud model of a single segment according to the pixel position of the single segment image.
10. A tunnel monitoring device, comprising: the device comprises an acquisition module, a data splicing module, a straight line and edge detection module, a duct piece and connecting block determination module, a point cloud segmentation module and a monitoring analysis module;
the acquisition module is used for acquiring point cloud data of an original section of the tunnel;
the data splicing module is used for generating a tunnel integral point cloud model by performing point cloud splicing operation according to the collected original section point cloud data and generating a tunnel pipe wall expansion diagram by reflectivity image generation operation;
the straight line and edge detection module is used for carrying out binarization processing on the tunnel pipe wall development image to obtain a binary image, carrying out parameter mapping on the binary image by using Hough transform, and extracting a straight line segment of the binary image within a preset angle range;
the segment and connecting block determining module is used for extracting a single segment image in the tunnel tube wall expansion map according to the straight line segment, and extracting a capping block sub-image and connecting block sub-images on two sides from the single segment image respectively;
the point cloud segmentation module is used for determining the position of the corresponding tunnel point cloud by using the pixel positions of the capping block subimage, the two side connection subimages and the single segment image, and segmenting the whole tunnel point cloud model into a small point cloud model;
and the monitoring analysis module is used for calculating the slab staggering height according to the fitting planes of the point cloud small block models and the distance between the fitting planes and determining the segment deformation condition of the tunnel according to the slab staggering height.
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| CN109373921B (en) * | 2018-10-26 | 2020-07-14 | 南京航空航天大学 | Tunnel monitoring method and device |
| CN110246223B (en) * | 2019-06-03 | 2022-12-13 | 南京航空航天大学 | Tunnel modeling method and device |
| CN111524154B (en) * | 2020-04-21 | 2021-12-28 | 南京航空航天大学 | Image-based tunnel segment automatic segmentation method |
| CN111538353B (en) * | 2020-05-12 | 2021-10-19 | 南京航空航天大学 | Tunnel detects car stabilising arrangement |
| CN111710027B (en) * | 2020-05-25 | 2021-05-04 | 南京林业大学 | Tunnel three-dimensional geometric reconstruction method |
| CN111610193A (en) * | 2020-05-29 | 2020-09-01 | 武汉至科检测技术有限公司 | System and method for inspecting structural defects of subway tunnel segment by adopting multi-lens shooting |
| CN112857252B (en) * | 2021-01-12 | 2023-04-07 | 深圳市地铁集团有限公司 | Tunnel image boundary line detection method based on reflectivity intensity |
| CN113686251B (en) * | 2021-08-19 | 2022-12-13 | 山东科技大学 | Method and system for measuring upward movement and downward movement offset of fully mechanized coal mining face equipment |
| CN114119355B (en) * | 2021-11-29 | 2023-04-28 | 北京工业大学 | Method and system for early warning of blocking dropping risk of shield tunnel |
| WO2023178481A1 (en) * | 2022-03-21 | 2023-09-28 | 深圳大学 | Deformation measurement method and apparatus, electronic device, and storage medium |
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| CN109373921A (en) * | 2018-10-26 | 2019-02-22 | 南京航空航天大学 | A kind of tunnel monitoring method and device |
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| FR3057534B1 (en) * | 2016-10-17 | 2018-11-30 | Metalliance | AUTONOMOUS TRANSPORT SYSTEM FOR TUNNEL |
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