CN111415344A - Disease detection method and device for horseshoe-shaped tunnel - Google Patents
Disease detection method and device for horseshoe-shaped tunnel Download PDFInfo
- Publication number
- CN111415344A CN111415344A CN202010195942.5A CN202010195942A CN111415344A CN 111415344 A CN111415344 A CN 111415344A CN 202010195942 A CN202010195942 A CN 202010195942A CN 111415344 A CN111415344 A CN 111415344A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- point
- tunnel
- horseshoe
- distance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
- G01N21/954—Inspecting the inner surface of hollow bodies, e.g. bores
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/06—Topological mapping of higher dimensional structures onto lower dimensional surfaces
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Analytical Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Signal Processing (AREA)
- Image Analysis (AREA)
Abstract
The application provides a method and a device for detecting diseases in a horseshoe-shaped tunnel, which are characterized by comprising the steps of obtaining coordinate data of point cloud collected on the inner surface of the horseshoe-shaped tunnel and original data of intensity data of the point cloud, determining the position of pixels in a gray level image mapped by the point cloud according to the coordinate data, converting the intensity data into the gray level data of the pixels, converting the original data into the gray level image, and detecting a water seepage area of the horseshoe-shaped tunnel by carrying out preset type image processing on the gray level image. Compared with a manual detection mode, the method has higher efficiency and accuracy.
Description
Technical Field
The application relates to the field of electronic information, in particular to a disease detection method and device for a horseshoe-shaped tunnel.
Background
The horseshoe-shaped tunnel refers to a tunnel with a horseshoe-shaped cross section. The periphery of this section consists of 4 circular arcs: the tunnel top is a semicircular arch, two sides of the tunnel top are connected with side arches with larger curvature radius, the tunnel bottom is an upward-facing bottom arch, and the joints of the side arches and the bottom arch are rounded by circular arcs.
The tunnel disease detection can include tunnel wall water leakage detection, tunnel wall deformation detection and the like. At present, the disease detection of the tunnel is usually carried out by using a manual inspection mode. Therefore, omission is easy and inefficient.
Therefore, how to improve the efficiency and accuracy of disease detection of the horseshoe tunnel becomes a problem to be solved urgently at present.
Disclosure of Invention
The application provides a disease detection method and device for a horseshoe-shaped tunnel, and aims to solve the problem of how to improve the disease detection efficiency and accuracy of the horseshoe-shaped tunnel.
In order to achieve the above object, the present application provides the following technical solutions:
a disease detection method for a horseshoe-shaped tunnel comprises the following steps:
obtaining raw data, the raw data comprising: coordinate data of point clouds collected on the inner surface of the horseshoe-shaped tunnel and intensity data of the point clouds;
converting the raw data into a grayscale image, wherein the coordinate data determines the location of the point cloud mapped to a pixel in the grayscale image, and the intensity data is converted into grayscale data for the pixel;
and detecting the water seepage area of the horseshoe-shaped tunnel by carrying out preset type image processing on the gray level image.
Optionally, the raw data further includes: mileage data of the point cloud, the mileage data indicating a mileage of the point cloud;
the converting the raw data into a grayscale image includes:
taking the circle center of a top semicircular tunnel obtained by dividing the horseshoe-shaped tunnel as the center, and calculating the angle of each point cloud in a space range formed by two cut-off points of the semicircular tunnel relative to the circle center;
dividing the point cloud of the top semicircular tunnel into the grid array according to the corresponding relation between the angle and the preset grid array;
dividing the point cloud of the tunnel into the left side and the right side by taking a perpendicular line where the central points of the two sides and the bottom tunnel obtained by dividing the horseshoe-shaped tunnel are located as a dividing line;
dividing the left point cloud into an upper left point cloud, a left waist point cloud and a lower left point cloud according to the coordinates of the upper left vertex and the lower left vertex of the tunnel;
dividing the right point cloud into an upper right point cloud, a right waist point cloud and a lower right point cloud according to the coordinates of the upper right vertex and the lower right vertex of the tunnel;
rotating the lower left point cloud and the left waist point cloud clockwise by 90 degrees around the lower left vertex and projecting the point clouds to a left inner wall plane, and rotating the point clouds projected into the left inner wall plane clockwise by 90 degrees around the upper left vertex;
rotating the right lower point cloud and the right waist point cloud around the right lower vertex by 90 degrees in an anticlockwise manner and projecting the point clouds onto a right inner wall plane, and rotating the point clouds projected into the right inner wall plane around the right upper vertex by 90 degrees in the anticlockwise manner;
dividing the rotated point cloud into a left side and a right side along the central point, setting grids with preset scales along the left and right directions from the central point, and dividing the rotated point cloud into the grids, wherein the grid array is formed by the grids which are vertically arranged;
converting the point cloud intensity data of the point cloud within the grid to a gray value.
Optionally, before the converting the point cloud intensity data of the point cloud in the grid into a gray value, the method further includes:
for any grid of the point cloud frames comprising a plurality of identical mileage, only one point cloud frame with mileage within the range of the grid is reserved as the point cloud in the grid;
and if no point cloud exists in any one of the grids, taking the point cloud in the last frame of the point cloud frame with the mileage larger than the range of the grid and closest to the range of the grid as the point cloud in the grid.
Optionally, the determining process of the preset scale of the grid includes:
setting the length and width of the gray level image; wherein the ratio of the length to the width is the same as a reference ratio, and the reference ratio is the ratio of the width of the horseshoe-shaped tunnel to the section perimeter;
and determining the scale of the grid in the length direction by using the length of the gray image and the number of the grids preset in the length direction, and determining the scale of the grid in the width direction by using the width of the gray image and the number of the grids preset in the width direction.
Optionally, the preset type of image processing includes:
contrast enhancement, binarization, erosion, saturation adjustment and edge extraction.
Optionally, the method further includes:
and detecting the deformation of the horseshoe-shaped tunnel by analyzing the horizontal distance and the vertical distance of the horseshoe-shaped tunnel, wherein the horizontal distance and the vertical distance are obtained according to the coordinate data.
Optionally, the process of acquiring the horizontal distance includes:
acquiring position data of a target point in the point cloud; wherein the target point is a point of a pair of points, the pair of points comprising: any position point with a preset height away from the ground on any side wall in the tunnel obtained by dividing the horseshoe-shaped tunnel, and a relative point on the opposite side wall;
for any one of the target points, if the difference value between a first distance and a second distance is within a preset difference value range, taking twice of the second distance as the horizontal distance of the target point, otherwise, taking twice of the first distance as the horizontal distance of the target point, wherein the first distance is the horizontal distance from the target point to the central point, the second distance is the distance from a straight line to the central point, and the straight line is the straight line of which the perpendicularity formed by the target point and an adjacent target point meets a preset perpendicularity threshold value;
the acquisition process of the vertical distance comprises the following steps:
calculating a third distance, wherein the third distance is the distance from a target top point to the center of the track, and the target top point is any one top point;
and if the levelness of a straight line formed by the target top point and the adjacent top point meets a preset levelness threshold value, taking twice of a fourth distance as the vertical distance, otherwise, taking twice of the third distance as the vertical distance, wherein the fourth distance is the distance from the straight line to the center of the track.
Optionally, the method further includes:
and if the point cloud is within a preset tunnel equipment limit frame, determining that limit invasion is detected.
A disease detection device for a horseshoe-shaped tunnel comprises:
an obtaining module, configured to obtain raw data, where the raw data includes: coordinate data of point clouds collected on the inner surface of the horseshoe-shaped tunnel and intensity data of the point clouds;
a conversion module for converting the raw data into a grayscale image, wherein the coordinate data determines the position of the point cloud mapped to a pixel in the grayscale image, and the intensity data is converted into grayscale data of the pixel;
and the detection module is used for detecting the water seepage area of the horseshoe tunnel by carrying out preset type image processing on the gray level image.
A disease detection apparatus for a horseshoe tunnel, comprising:
a memory and a processor;
the memory is used for storing programs, and the processor is used for running the programs so as to realize the disease detection method aiming at the horseshoe-shaped tunnel.
A computer-readable storage medium, on which a computer program is stored, which, when run on a computer, implements the above-described method for disease detection for a horseshoe tunnel.
The embodiment of the application discloses a method and a device for detecting diseases in a horseshoe-shaped tunnel, which are used for acquiring coordinate data of point cloud acquired on the inner surface of the horseshoe-shaped tunnel and original data of intensity data of the point cloud, determining the position of pixels in a gray level image mapped by the point cloud according to the coordinate data, converting the intensity data into the gray level data of the pixels, converting the original data into the gray level image, and detecting a water seepage area of the horseshoe-shaped tunnel by performing preset type image processing on the gray level image. Compared with a manual detection mode, the method has higher efficiency and accuracy.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a disease detection method for a horseshoe tunnel disclosed in an embodiment of the present application;
FIG. 2 is a flowchart of converting point cloud data into a gray scale map according to an embodiment of the present disclosure;
FIG. 3 is a flow chart for detecting distortion using horizontal and vertical separation as disclosed in an embodiment of the present application;
FIG. 4 is a flow chart of detecting boundary intrusion according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a disease detection device for a horseshoe-shaped tunnel disclosed in an embodiment of the present application.
Detailed Description
The technical scheme of the embodiment of the application can be applied to, but is not limited to, a horseshoe tunnel in the field of rail transit. Further, the method can be applied to disease detection of the horseshoe tunnel of the subway.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a disease detection method for a horseshoe tunnel disclosed in an embodiment of the present application, including the following steps:
s101: raw data is acquired.
In this embodiment, the inner wall of the horseshoe tunnel is scanned by using a laser scanner to obtain point cloud data. Specifically, the inside of the horseshoe-shaped tunnel is scanned by adopting a rail-mounted three-dimensional laser scanner, a laser probe is arranged in the three-dimensional laser scanner, the tunnel section is scanned in a high-frequency rotation laser beam emitting mode, and the laser beam returns to a laser radar after contacting the surface of the tunnel to obtain the information of the surface of the tunnel.
Further, the laser scanner advances in the tunnel direction at a preset interval, the laser probe rotationally emits a laser beam at a high frequency at each position point, and feedback radar data of the laser beam emitted by rotating 360 degrees is used as a frame of point cloud data. At each location, at least one frame of point cloud data can be acquired.
Wherein the raw data comprises: point cloud data (including coordinate data), inertial navigation data, mileage data, and intensity data (representing the intensity of the radar data fed back) for points on the tunnel inner surface.
S102: the raw data is converted into a grayscale image.
That is, a three-dimensional point cloud is converted into a two-dimensional image, the coordinate data of the point cloud determines the positions of pixels in the point cloud mapped into a grayscale image, and the intensity data of the point cloud is converted into grayscale data of the pixels. Because the point cloud data comprises the mileage data, the pixel point corresponding to each frame of point cloud in the gray level image can also correspond to the mileage data.
The specific implementation of S102 will be described in detail in the flow shown in fig. 2.
S103: and detecting the water seepage area of the horseshoe-shaped tunnel by performing preset type image processing on the gray level image.
Among them, the preset type of image processing includes but is not limited to: contrast enhancement, binarization, erosion, saturation adjustment and edge extraction.
Since the water seepage region has a large area and appears in black, a closed region with a gray value of 0 (in practice, not limited to 0, but a threshold may be set according to an empirical value) may be extracted from the gray image as the water seepage region. And (3) binarization and edge extraction in preset type image processing can realize extraction of black closed regions.
However, in the process of research, the applicant finds that the gray values of the background and the water seepage area in the gray map are similar, so that in order to improve the identification accuracy, contrast enhancement processing is performed before binarization so as to increase the difference of the gray values of the pixels of the background and the water seepage area.
After binarization, etching processing and saturation adjustment are performed, aiming at removing noise to improve the accuracy of edge extraction. Specifically, the edge extraction may use the Canny operator edge extraction algorithm.
It should be noted that, for the above specific implementation of image processing, reference may be made to the prior art, and details are not described here. The above image processing modes may be fully automatic, or may be manual and automatic, for example, contrast increasing processing, which may manually set contrast parameters and repeatedly debug, thereby obtaining the best contrast.
It is understood that if an occlusion region exists in the processed image, it is determined that a water infiltration region is detected, and otherwise, it is determined that a water infiltration region is not detected. Furthermore, based on the closed area, the area of the water seepage area can be obtained, and the position of the water seepage area can be accurately positioned by combining mileage information.
S104: and detecting the deformation of the horseshoe-shaped tunnel by analyzing the horizontal distance and the vertical distance of the horseshoe-shaped tunnel.
In actual space, any one position point horizontally spaced from any one side wall of the horseshoe-shaped tunnel by a height (for example, 1.5 m) from the center of the track (the center of the track is the center line of the track laid in the tunnel) is spaced from the opposite side wall by a distance corresponding to the point. The relative point of any one position point is a position point which has the same height with the center of the track and the connecting line of the position point and the position point is a horizontal line.
In this embodiment, a specific implementation manner of calculating the horizontal distance by using the point cloud will be described in detail in the flow shown in fig. 3.
Based on the definition of the horizontal distance, it can be understood that if the relationship between the horizontal distance and the preset horizontal distance threshold value meets the preset condition, it is determined that the deformation of the horseshoe-shaped tunnel is detected, otherwise, it is determined that the horseshoe-shaped tunnel is not deformed.
The vertical distance is the vertical distance between any point at the top of the horseshoe-shaped tunnel and the center of the track. It can be understood that if the relationship between the vertical distance and the preset vertical distance threshold value meets the preset condition, it is determined that the deformation of the horseshoe-shaped tunnel is detected, otherwise, it is determined that the horseshoe-shaped tunnel is not deformed. The specific implementation of calculating the vertical distance by point cloud will be described in detail in the flow shown in fig. 3.
S105: and detecting the boundary intrusion of the horseshoe tunnel through the point cloud data.
A device bounding box is a control line used to limit device installation. The device bounding box is used to constrain the control lines of the device installation. Any part of the building and ground-based fixed-part equipment must not intrude into this limit unless otherwise specified. Otherwise, it is called a boundary intrusion.
In this embodiment, a specific implementation manner of detecting a boundary intrusion of a horseshoe tunnel through point cloud data will be described in detail in the flow shown in fig. 4.
In the process shown in fig. 1, a gray image of the inner surface of the horseshoe-shaped tunnel is obtained through point cloud data of the inner surface of the horseshoe-shaped tunnel, so that the water seepage area in the horseshoe-shaped tunnel is detected based on the gray image. And, through the point cloud data of the internal surface of the horseshoe tunnel, deformation and limit invasion of the horseshoe tunnel are detected. Compare with artifical patrolling and examining, on the one hand can improve detection efficiency, reduce the cost of patrolling and examining by a wide margin, and on the other hand, the point cloud data that laser scanning obtained can cover the tunnel internal surface comprehensively and the granularity is little, and consequently the accuracy of the testing result that obtains based on point cloud data is higher.
Experiments prove that compared with manual detection, the efficiency of the method is improved by more than 5 times.
Fig. 2 is a flowchart of converting original data into a grayscale image, which includes the following steps:
s201: the central point and two cut-off points of the top tunnel of the horseshoe tunnel, and the four vertexes and the central point of the lower tunnel are obtained.
Because the top of the horseshoe-shaped tunnel is a semicircular tunnel, and the left side, the right side and the bottom of the horseshoe-shaped tunnel are formed by arcs with different radiuses, the horseshoe-shaped tunnel can be divided into an upper part and a lower part, the upper part tunnel can be understood as the semicircular tunnel, and the lower part tunnel can be understood as an approximate U-shaped tunnel (hereinafter referred to as the U-shaped or U-shaped tunnel).
The four vertexes of the lower tunnel are the four vertexes of the cross-section U shape of the lower U-shaped tunnel.
The section of the semicircular tunnel is circular arc, and the cut-off points are the starting point and the end point of the circular arc, namely two end points of the circular arc.
S202: and acquiring the scale of the grid in the gray image.
In this embodiment, a grid is set in the grayscale image as a sampling unit of the point cloud. Both the length and width methods divide the grid.
Specifically, the determination method of the scale of the grid is as follows: the length and width of the gray image are set, and the ratio of the length and width of the gray image is the same as the ratio of the width of the horseshoe tunnel to the section perimeter (which can be used as a reference ratio). And determining the dimension of the grid in the length direction by using the length of the gray image and the number of the grids preset in the length direction in the gray image. For example, if the preset number of the grids in the length direction of the grayscale image is 1000 and the length of the grayscale image is 1000 cm, the dimension of the grids in the length direction of the grayscale image is 1 cm. And determining the dimension of the grid in the width direction by using the width of the gray image and the number of the grids preset in the width direction in the gray image.
It is understood that, based on the above-described grid division method and the mileage data of the horseshoe tunnel, the grid may also correspond to the mileage data. For a specific corresponding manner, reference may be made to the prior art, which is not described herein again.
The point clouds of the U-shaped tunnel and the semicircular tunnel obtained by dividing the horseshoe-shaped tunnel are respectively processed as follows, wherein S203-S206 are used for processing the point clouds of the U-shaped tunnel, and S207-S208 are used for processing the point clouds of the semicircular tunnel.
S203: the point cloud belonging to the U-shaped tunnel is divided into the left side and the right side by taking the perpendicular line where the two sides of the horseshoe-shaped tunnel and the center point of the bottom U-shaped tunnel (the coordinate of the center point is obtained in S201) are located as a dividing line.
S204: for any point cloud, taking the left point cloud as an example, and dividing the left point cloud into an upper left point cloud, a lower left point cloud and a lower left point cloud according to the coordinates of the upper left vertex and the lower left vertex (obtained in S201).
S205: and rotating the left lower point cloud clockwise by 90 degrees around the left lower vertex and projecting the cloud to a left inner wall plane (a plane determined in the vertical direction and the advancing direction) and projecting the left waist point cloud to the left inner wall plane, and rotating the point cloud projected to the left inner wall plane clockwise by 90 degrees around the left upper vertex.
And (3) carrying out similar processing on the right point cloud: dividing the right point cloud into a right upper point cloud, a right waist point cloud and a right lower point cloud according to the coordinates of the right upper vertex and the right lower vertex of the two sides and the bottom tunnel, rotating the right lower point cloud and the right waist point cloud around the right lower vertex by 90 degrees in an anticlockwise mode, projecting the right lower point cloud and the right waist point cloud to the right inner wall plane, and rotating the point cloud projected into the right inner wall plane around the right upper vertex by 90 degrees in the anticlockwise mode.
S206: dividing the rotated point cloud into a left side and a right side along a central point, arranging grids from the central point along the left and right directions, and dividing the point cloud into the grids after rotation.
Wherein the scale of the set grid is the scale acquired in S202.
And for any grid, dividing the point cloud of which the mileage is within the range of the grid into the point cloud within the grid. The mileage of any point in the point cloud is represented by mileage data in the point cloud data of the point cloud.
S207: and taking the circle center of a semicircular tunnel of the horseshoe-shaped tunnel as a center, and calculating the angle of each point cloud in a space range formed by the two cut-off points relative to the circle center.
S208: and dividing the point cloud of the top semicircular tunnel into the grid array according to the corresponding relation between the angle and the grid array.
The corresponding relationship between the angle and the grid array may be preset, for example, an interval of 0.1 degree corresponds to one grid, and an angle range of 0 degree to 0.1 degree corresponds to the first grid array. The grid columns are grids of each column in the vertical direction.
S209: for any grid of the point cloud frames comprising a plurality of identical mileage, only one point cloud frame with mileage within the range of the grid is reserved as the point cloud in the grid.
It should be noted that, there may be a plurality of circles of the laser head rotating with the laser scanning head at a position, and therefore, the position corresponds to a plurality of point cloud frames, and therefore, there may be a plurality of point cloud frames whose mileage is within the range of the grid.
S210: and if no point cloud exists in any grid, taking the point cloud in the last frame of the point cloud frame with the mileage larger than the range of the grid and closest to the range of the grid as the point cloud in the grid.
It can be understood that, in the above S209-S210, all grids are filled with the point clouds, and redundant point clouds are deleted, so that the sampling of the point cloud data is completed, and a sampled point cloud is obtained.
S211: and carrying out intensity smoothing and intensity filling processing on the point cloud in the grid.
S211 is an optional step aimed at making the quality of the generated grayscale map higher.
S212: and converting the point cloud intensity data of the point cloud in the grid into a gray value, and converting the point cloud data to obtain a gray map.
Further, mileage information of the point cloud within the grid may be recorded.
The process shown in fig. 2 converts the point cloud data into a gray image according to the structural characteristics of the horseshoe tunnel (S203-S208), and lays a foundation for the water seepage area detection based on the image.
Fig. 3 is a flow of calculating a horizontal distance by point cloud and detecting deformation, which is disclosed in the embodiment of the present application, and includes the following steps:
s301: and acquiring position data of a target point in the point cloud.
Wherein the target point is a point in the pair of points. The point pair includes: any one point on any one side wall of the horseshoe tunnel at a predetermined height (e.g., 1.5 meters) from the ground, and an opposite point on the opposite side wall.
Specifically, the position data of the target point may be determined according to the coordinates in the point cloud data and the coordinates of the end and the center point of the horseshoe tunnel. The detailed manner can be seen in the prior art, and is not described herein.
Further, in order to reduce the amount of calculation, target points at equal intervals on the side wall of the horseshoe-shaped tunnel may be taken.
S302: the horizontal distance from any one of the target points to the center point, i.e., the first distance, is calculated.
S303: and judging whether the verticality of the straight line formed by the target point and the adjacent target point meets a preset verticality threshold (for example, 90 degrees to the ground), if so, executing S304, and if not, executing S306.
S304: the distance of the straight line from the center point, i.e., the second distance, is calculated.
S305: and judging whether the difference value of the first distance and the second distance is within a preset difference value range, if so, executing S307, and if not, executing S306.
S306: twice the second distance is taken as the horizontal distance of the target points.
S307: twice the first distance is taken as the horizontal distance of the target points.
S308: and judging the difference value between the horizontal distance and the horizontal distance threshold value, if the difference value is within the design value allowable range, determining that the horseshoe-shaped tunnel is not deformed, otherwise, determining that the horseshoe-shaped tunnel is deformed.
S309: and calculating the vertical distance from any top point in the point cloud to the center of the track, namely a third distance.
S310: and judging whether the levelness of the straight line formed by the top point and the adjacent top point meets a preset levelness threshold (for example, 0 degree), if so, executing S311, and if not, executing S314.
S311: the distance of the straight line from the center of the track, i.e., the fourth distance, is calculated.
S312: and judging whether the difference value of the third distance and the fourth distance is within a preset difference value range, if so, executing S314, and if not, executing S313.
S313: twice the fourth distance is taken as the vertical distance of the top point.
S314: twice the third distance is taken as the vertical distance of the top point.
S315: and judging the difference value of the vertical distance and the vertical distance threshold, if the difference value is within the design value allowable range, determining that the horseshoe-shaped tunnel is not deformed, otherwise, determining that the horseshoe-shaped tunnel is deformed.
As can be seen from the flow of fig. 3, in this embodiment, the horizontal distance and the vertical distance are obtained based on the point cloud, and then the deformation is detected according to the horizontal distance and the vertical distance, because the point cloud can be regarded as uniform sampling of the points on the inner surface of the tunnel, the accuracy of the deformation detection result is high, and compared with manual measurement, the efficiency is higher and the cost is lower.
Fig. 4 is a flowchart of detecting boundary intrusion through clearance analysis according to an embodiment of the present application, including the following steps:
s401: and acquiring a design value of a tunnel equipment limit box.
A device bounding box is a control line used to limit device installation. The device bounding box is used to constrain the control lines of the device installation. S402: and judging whether the point cloud invades a tunnel equipment bounding box, if so, executing S403, and if not, executing S404.
When the device is mounted on the tunnel wall, the point cloud data includes point cloud data of the device.
The specific way of judging whether the point cloud invades the tunnel equipment bounding box is as follows: and determining curves formed by the point clouds according to the point cloud data, determining equipment limit frame curves according to design values of the tunnel equipment limit frame, and judging whether the two curves have intersection points.
S403: it is determined that a boundary intrusion is detected.
S404: it was determined that no boundary intrusion occurred.
As can be seen from the flow of fig. 4, in this embodiment, whether a tunnel has a boundary intrusion is determined based on a device boundary box design value and point cloud data. Because the point cloud can be regarded as uniform sampling of points on the inner surface of the running tunnel, the deformation detection result is high in accuracy, and has higher efficiency and lower cost compared with manual measurement.
Fig. 5 is a disease detection device for a horseshoe tunnel disclosed in an embodiment of the present application, including: the device comprises an acquisition module, a conversion module and a detection module.
The acquisition module is used for acquiring original data, and the original data comprises: coordinate data of the point cloud and intensity data of the point cloud collected on the inner surface of the horseshoe-shaped tunnel. The conversion module is used for converting the original data into a gray level image, wherein the coordinate data determines the position of the pixels in the point cloud mapping gray level image, and the intensity data is converted into the gray level data of the pixels. The detection module is used for detecting the water seepage area of the horseshoe-shaped tunnel by carrying out preset type image processing on the gray level image. Optionally, the detection module is further configured to detect deformation of the horseshoe-shaped tunnel by analyzing a horizontal distance and a vertical distance of the horseshoe-shaped tunnel, where the horizontal distance and the vertical distance are obtained according to the coordinate data. And determining that the boundary intrusion is detected if the point cloud intrudes into a preset boundary box of the tunnel equipment.
Specifically, the raw data further includes: and mileage data of the point cloud, wherein the mileage data is used for indicating the mileage of the point cloud. The specific implementation mode of converting the original data into the gray level image by the conversion module is as follows:
taking the circle center of a top semicircular tunnel obtained by dividing the horseshoe-shaped tunnel as the center, and calculating the angle of each point cloud in a space range formed by two cut-off points of the semicircular tunnel relative to the circle center; dividing the point cloud of the top semicircular tunnel into the grid array according to the corresponding relation between the angle and the preset grid array; dividing the point cloud of the tunnel into the left side and the right side by taking a perpendicular line where the central points of the two sides and the bottom tunnel obtained by dividing the horseshoe-shaped tunnel are located as a dividing line; dividing the left point cloud into an upper left point cloud, a left waist point cloud and a lower left point cloud according to the coordinates of the upper left vertex and the lower left vertex of the tunnel; dividing the right point cloud into an upper right point cloud, a right waist point cloud and a lower right point cloud according to the coordinates of the upper right vertex and the lower right vertex of the two sides and the bottom tunnel; rotating the lower left point cloud and the left waist point cloud clockwise by 90 degrees around the lower left vertex and projecting the point clouds to a left inner wall plane, and rotating the point clouds projected into the left inner wall plane clockwise by 90 degrees around the upper left vertex; rotating the right lower point cloud and the right waist point cloud around the right lower vertex by 90 degrees in an anticlockwise manner and projecting the point clouds onto a right inner wall plane, and rotating the point clouds projected into the right inner wall plane around the right upper vertex by 90 degrees in the anticlockwise manner; dividing the rotated point cloud into a left side and a right side along the central point, setting grids with preset scales along the left and right directions from the central point, and dividing the rotated point cloud into the grids, wherein the grid array is formed by the grids which are vertically arranged; converting the point cloud intensity data of the point cloud within the grid to a gray value.
Further, the conversion module is further configured to: before converting the point cloud intensity data of the point clouds in the grids into gray values, only one point cloud frame with mileage within the range of the grid is reserved as the point cloud in the grid for any one grid of the point cloud frames with the same mileage; and if no point cloud exists in any one of the grids, taking the point cloud in the last frame of the point cloud frame with the mileage larger than the range of the grid and closest to the range of the grid as the point cloud in the grid.
Further, the process of determining the preset dimension of the grid includes: setting the length and width of the gray level image; wherein, the ratio of the length to the width is the same as the reference ratio which is the ratio of the width of the horseshoe-shaped tunnel to the section perimeter; the length of the gray image and the number of the grids preset in the length direction are used for determining the dimension of the grids in the length direction, and the width of the gray image and the number of the grids preset in the width direction are used for determining the dimension of the grids in the width direction.
Specifically, the preset type of image processing includes: contrast enhancement, binarization, erosion, saturation adjustment and edge extraction.
Specifically, the process of acquiring the horizontal distance includes: acquiring position data of a target point in the point cloud; wherein, the target point is the point in the point pair, and the point pair includes: any position point with a preset height away from the ground on any side wall in the tunnel obtained by dividing the horseshoe-shaped tunnel, and a relative point on the opposite side wall; and regarding any target point, if the difference value between the first distance and the second distance is within a preset difference value range, taking twice of the second distance as the horizontal distance of the target point, otherwise, taking twice of the first distance as the horizontal distance of the target point, wherein the first distance is the horizontal distance from the target point to a central point, the second distance is the distance from a straight line to the central point, and the straight line is the straight line of which the perpendicularity formed by the target point and an adjacent target point meets a preset perpendicularity threshold value.
The acquisition process of the vertical distance comprises the following steps:
and calculating a third distance, wherein the third distance is the distance from the target top point to the center of the track, and the target top point is any one top point. And if the levelness of a straight line formed by the target top point and the adjacent top point meets a preset levelness threshold value, taking twice of a fourth distance as the vertical distance, otherwise, taking twice of the third distance as the vertical distance, wherein the fourth distance is the distance from the straight line to the center of the track.
The device shown in fig. 4 can convert point cloud data of the inner surface of the horseshoe-shaped tunnel into a gray image, detect a water seepage area based on the gray image, detect deformation based on the point cloud data, realize disease detection of the horseshoe-shaped tunnel according to the point cloud data, and has high efficiency and accuracy.
The embodiment of the application also discloses disease detection equipment to horseshoe tunnel includes: a memory and a processor. The memory is used for storing a program, and the processor is used for running the program so as to realize the disease detection method for the horseshoe tunnel described in the above embodiment.
A computer-readable storage medium, on which a computer program is stored, which, when run on a computer, implements the method for detecting a disease in a horseshoe tunnel according to the above-described embodiments.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (11)
1. A disease detection method for a horseshoe-shaped tunnel is characterized by comprising the following steps:
obtaining raw data, the raw data comprising: coordinate data of point clouds collected on the inner surface of the horseshoe-shaped tunnel and intensity data of the point clouds;
converting the raw data into a grayscale image, wherein the coordinate data determines the location of the point cloud mapped to a pixel in the grayscale image, and the intensity data is converted into grayscale data for the pixel;
and detecting the water seepage area of the horseshoe-shaped tunnel by carrying out preset type image processing on the gray level image.
2. The method of claim 1, wherein the raw data further comprises: mileage data of the point cloud, the mileage data indicating a mileage of the point cloud;
the converting the raw data into a grayscale image includes:
taking the circle center of a top semicircular tunnel obtained by dividing the horseshoe-shaped tunnel as the center, and calculating the angle of each point cloud in a space range formed by two cut-off points of the semicircular tunnel relative to the circle center;
dividing the point cloud of the top semicircular tunnel into the grid array according to the corresponding relation between the angle and the preset grid array;
dividing the point cloud of the tunnel into the left side and the right side by taking a perpendicular line where the central points of the two sides and the bottom tunnel obtained by dividing the horseshoe-shaped tunnel are located as a dividing line;
dividing the left point cloud into an upper left point cloud, a left waist point cloud and a left lower point cloud according to the coordinates of the upper left vertex and the lower left vertex of the two sides and the bottom tunnel;
dividing the right point cloud into an upper right point cloud, a right waist point cloud and a lower right point cloud according to the coordinates of the upper right vertex and the lower right vertex of the two sides and the bottom tunnel;
rotating the lower left point cloud and the left waist point cloud clockwise by 90 degrees around the lower left vertex and projecting the point clouds to a left inner wall plane, and rotating the point clouds projected into the left inner wall plane clockwise by 90 degrees around the upper left vertex;
rotating the right lower point cloud and the right waist point cloud around the right lower vertex by 90 degrees in an anticlockwise manner and projecting the point clouds onto a right inner wall plane, and rotating the point clouds projected into the right inner wall plane around the right upper vertex by 90 degrees in the anticlockwise manner;
dividing the rotated point cloud into a left side and a right side along the central point, setting grids with preset scales along the left and right directions from the central point, and dividing the rotated point cloud into the grids, wherein the grid array is formed by the grids which are vertically arranged;
converting the point cloud intensity data of the point cloud within the grid to a gray value.
3. The method of claim 2, further comprising, prior to said converting the point cloud intensity data of the point cloud within the grid to grayscale values:
for any grid of the point cloud frames comprising a plurality of identical mileage, only one point cloud frame with mileage within the range of the grid is reserved as the point cloud in the grid;
and if no point cloud exists in any one of the grids, taking the point cloud in the last frame of the point cloud frame with the mileage larger than the range of the grid and closest to the range of the grid as the point cloud in the grid.
4. The method of claim 2, wherein the determining of the preset dimension of the grid comprises:
setting the length and width of the gray level image; wherein the ratio of the length to the width is the same as a reference ratio, and the reference ratio is the ratio of the width of the horseshoe-shaped tunnel to the section perimeter;
and determining the scale of the grid in the length direction by using the length of the gray image and the number of the grids preset in the length direction, and determining the scale of the grid in the width direction by using the width of the gray image and the number of the grids preset in the width direction.
5. The method according to claim 1, wherein the preset type of image processing comprises:
contrast enhancement, binarization, erosion, saturation adjustment and edge extraction.
6. The method of any one of claims 1-5, further comprising:
and detecting the deformation of the horseshoe-shaped tunnel by analyzing the horizontal distance and the vertical distance of the horseshoe-shaped tunnel, wherein the horizontal distance and the vertical distance are obtained according to the coordinate data.
7. The method of claim 6, wherein the obtaining of the horizontal distance comprises:
acquiring position data of a target point in the point cloud; wherein the target point is a point of a pair of points, the pair of points comprising: any position point with a preset height away from the ground on any side wall in the tunnel obtained by dividing the horseshoe-shaped tunnel, and a relative point on the opposite side wall;
for any one of the target points, if the difference value between a first distance and a second distance is within a preset difference value range, taking twice of the second distance as the horizontal distance of the target point, otherwise, taking twice of the first distance as the horizontal distance of the target point, wherein the first distance is the horizontal distance from the target point to the central point, the second distance is the distance from a straight line to the central point, and the straight line is the straight line of which the perpendicularity formed by the target point and an adjacent target point meets a preset perpendicularity threshold value;
the acquisition process of the vertical distance comprises the following steps:
calculating a third distance, wherein the third distance is the distance from a target top point to the center of the track, and the target top point is any one top point;
and if the levelness of a straight line formed by the target top point and the adjacent top point meets a preset levelness threshold value, taking twice of a fourth distance as the vertical distance, otherwise, taking twice of the third distance as the vertical distance, wherein the fourth distance is the distance from the straight line to the center of the track.
8. The method of claim 1, further comprising:
and if the point cloud is within a preset tunnel equipment limit frame, determining that limit invasion is detected.
9. A disease detection device for a horseshoe-shaped tunnel, comprising:
an obtaining module, configured to obtain raw data, where the raw data includes: coordinate data of point clouds collected on the inner surface of the horseshoe-shaped tunnel and intensity data of the point clouds;
a conversion module for converting the raw data into a grayscale image, wherein the coordinate data determines the position of the point cloud mapped to a pixel in the grayscale image, and the intensity data is converted into grayscale data of the pixel;
and the detection module is used for detecting the water seepage area of the horseshoe tunnel by carrying out preset type image processing on the gray level image.
10. A disease detection device for a horseshoe-shaped tunnel, comprising:
a memory and a processor;
the memory is used for storing a program, and the processor is used for operating the program to realize the disease detection method for the horseshoe tunnel according to any one of claims 1 to 8.
11. A computer-readable storage medium, on which a computer program is stored, which, when run on a computer, implements the method for disease detection for a horseshoe tunnel according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010195942.5A CN111415344B (en) | 2020-03-19 | 2020-03-19 | Disease detection method and device for horseshoe-shaped tunnel |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010195942.5A CN111415344B (en) | 2020-03-19 | 2020-03-19 | Disease detection method and device for horseshoe-shaped tunnel |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111415344A true CN111415344A (en) | 2020-07-14 |
| CN111415344B CN111415344B (en) | 2023-06-20 |
Family
ID=71493076
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010195942.5A Active CN111415344B (en) | 2020-03-19 | 2020-03-19 | Disease detection method and device for horseshoe-shaped tunnel |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111415344B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114965487A (en) * | 2022-06-10 | 2022-08-30 | 招商局重庆交通科研设计院有限公司 | Calibration method and device of automatic monitoring equipment for tunnel typical diseases |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0428485D0 (en) * | 2004-12-30 | 2005-02-02 | Wagner Wolfgang | A method and device for a rail bound solution of the passenger traffic and transportation of goods for toys too |
| WO2014022173A1 (en) * | 2012-07-31 | 2014-02-06 | Datalogic ADC, Inc. | Calibration and self-test in automated data reading systems |
| CN104005325A (en) * | 2014-06-17 | 2014-08-27 | 武汉武大卓越科技有限责任公司 | Pavement crack detecting device and method based on depth and gray level images |
| WO2015170879A1 (en) * | 2014-05-09 | 2015-11-12 | 주식회사 케이엠티엘 | Monorail inspection apparatus |
| CN106127771A (en) * | 2016-06-28 | 2016-11-16 | 上海数联空间科技有限公司 | Tunnel orthography system and method is obtained based on laser radar LIDAR cloud data |
| US9928379B1 (en) * | 2008-09-08 | 2018-03-27 | Steven Miles Hoffer | Methods using mediation software for rapid health care support over a secured wireless network; methods of composition; and computer program products therefor |
| CN108335291A (en) * | 2018-01-24 | 2018-07-27 | 上海岩土工程勘察设计研究院有限公司 | A kind of tunnel defect Yunnan snub-nosed monkey method |
| CN108731640A (en) * | 2017-04-25 | 2018-11-02 | 北京威远图易数字科技有限公司 | Metro Clearance detection method based on point cloud data and detecting system |
| CN110097504A (en) * | 2019-05-13 | 2019-08-06 | 招商局重庆交通科研设计院有限公司 | A kind of image vision acquisition system for tunnel crusing robot |
| CN110322428A (en) * | 2019-05-07 | 2019-10-11 | 武汉汉宁轨道交通技术有限公司 | Detect the method, apparatus and electronic equipment of tunnel defect |
| CN110346370A (en) * | 2019-07-17 | 2019-10-18 | 武汉汉宁轨道交通技术有限公司 | Tunnel defect detection device, system and method |
| CN209926104U (en) * | 2019-05-30 | 2020-01-10 | 夏信辉 | Highway tunnel disease detection lighting device |
-
2020
- 2020-03-19 CN CN202010195942.5A patent/CN111415344B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0428485D0 (en) * | 2004-12-30 | 2005-02-02 | Wagner Wolfgang | A method and device for a rail bound solution of the passenger traffic and transportation of goods for toys too |
| US9928379B1 (en) * | 2008-09-08 | 2018-03-27 | Steven Miles Hoffer | Methods using mediation software for rapid health care support over a secured wireless network; methods of composition; and computer program products therefor |
| WO2014022173A1 (en) * | 2012-07-31 | 2014-02-06 | Datalogic ADC, Inc. | Calibration and self-test in automated data reading systems |
| WO2015170879A1 (en) * | 2014-05-09 | 2015-11-12 | 주식회사 케이엠티엘 | Monorail inspection apparatus |
| CN104005325A (en) * | 2014-06-17 | 2014-08-27 | 武汉武大卓越科技有限责任公司 | Pavement crack detecting device and method based on depth and gray level images |
| CN106127771A (en) * | 2016-06-28 | 2016-11-16 | 上海数联空间科技有限公司 | Tunnel orthography system and method is obtained based on laser radar LIDAR cloud data |
| CN108731640A (en) * | 2017-04-25 | 2018-11-02 | 北京威远图易数字科技有限公司 | Metro Clearance detection method based on point cloud data and detecting system |
| CN108335291A (en) * | 2018-01-24 | 2018-07-27 | 上海岩土工程勘察设计研究院有限公司 | A kind of tunnel defect Yunnan snub-nosed monkey method |
| CN110322428A (en) * | 2019-05-07 | 2019-10-11 | 武汉汉宁轨道交通技术有限公司 | Detect the method, apparatus and electronic equipment of tunnel defect |
| CN110097504A (en) * | 2019-05-13 | 2019-08-06 | 招商局重庆交通科研设计院有限公司 | A kind of image vision acquisition system for tunnel crusing robot |
| CN209926104U (en) * | 2019-05-30 | 2020-01-10 | 夏信辉 | Highway tunnel disease detection lighting device |
| CN110346370A (en) * | 2019-07-17 | 2019-10-18 | 武汉汉宁轨道交通技术有限公司 | Tunnel defect detection device, system and method |
Non-Patent Citations (3)
| Title |
|---|
| 田军等: "山岭隧道围岩-结构层间堵排水效能现场测试分析", 《公路工程》 * |
| 陈功奇: "延安东路隧道衬砌结构表观病害的检测技术与应用", 《中国市政工程》 * |
| 高洪等: "移动三维激光测量系统在地铁运营隧道病害监测中的应用研究", 《测绘通报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114965487A (en) * | 2022-06-10 | 2022-08-30 | 招商局重庆交通科研设计院有限公司 | Calibration method and device of automatic monitoring equipment for tunnel typical diseases |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111415344B (en) | 2023-06-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111325747B (en) | Disease detection method and device for rectangular tunnel | |
| CN110084116B (en) | Road surface detection method, road surface detection device, computer equipment and storage medium | |
| CN113345019B (en) | Method, equipment and medium for measuring potential hazards of transmission line channel target | |
| CN109635816B (en) | Lane line generation method, apparatus, device, and storage medium | |
| US10704900B2 (en) | Detection device and detection method | |
| CN108230344B (en) | Automatic identification method for tunnel water leakage diseases | |
| US20160133008A1 (en) | Crack data collection method and crack data collection program | |
| CN113570665B (en) | Road edge extraction method and device and electronic equipment | |
| CN114549751A (en) | Template monitoring system and method for box girder production | |
| CN116739183B (en) | A mine safety risk early warning and prediction system | |
| CN110550067A (en) | Train wheel measurement method and related system | |
| CN115797258A (en) | Inter-ring staggered platform detection method and system for shield tunnel | |
| CN117647232A (en) | Method, device and equipment for converting laser elevation points into satellite stereoscopic images | |
| CN111009011A (en) | Method, device, system and storage medium for predicting vehicle direction angle | |
| CN107765257A (en) | A kind of laser acquisition and measuring method based on the calibration of reflected intensity accessory external | |
| US20230267593A1 (en) | Workpiece measurement method, workpiece measurement system, and program | |
| CN116740307A (en) | Smart city three-dimensional model construction method | |
| CN111415344B (en) | Disease detection method and device for horseshoe-shaped tunnel | |
| CN108036929B (en) | Method, device and system for detecting picture arranging parameters of display equipment | |
| KR102568111B1 (en) | Apparatus and method for detecting road edge | |
| CN119043268A (en) | Shield tunnel section fitting method, staggered platform quantity detection method and detection system | |
| CN117745941B (en) | A method and device for automatically generating a three-dimensional building model using satellite images | |
| KR101626901B1 (en) | construction system for a precise 3 dimension shape information of a culvert and method thereof | |
| JP7147906B1 (en) | Pantograph peripheral obstruction detection device and detection method | |
| CN117670976A (en) | Diameter measurement method based on visual imaging detection equipment and related device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |