CN116183622A - Subway seepage water detection method based on point cloud information - Google Patents
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Abstract
The invention discloses a subway water leakage detection method based on point cloud information, which utilizes three-dimensional laser point cloud data to divide tunnels, divides tunnel ring numbers into a plurality of single-ring point cloud models, corrects the single-ring point cloud models into a range of gray values by uniformly changing point cloud scalar values in each single ring, replaces the gray values with point cloud intensity values, and sets a threshold value. And counting the seepage water areas and the occupation ratios of all the annular sheets, and screening the annular sheets with larger seepage areas to achieve the effect of quickly detecting the seepage water areas. The invention directly utilizes the intensity value of the point cloud information to distinguish the water leakage areas. The image is composed of pixel points, point cloud data are converted into the image through grating, reflection intensity of light among different materials is different, and the intensity value can be directly used as a gray value to distinguish water leakage, so that the calculation process can be simplified, the recognition speed is improved, and the method has a very strong popularization value.
Description
Technical Field
The invention belongs to the technical field of tunnel engineering, and particularly relates to a subway tunnel water leakage detection method.
Background
At present, the subway tunnel in China is in a large-scale construction period, and as more and more subway lines are put into operation, the used subway tunnel exposes a plurality of disease problems. The main construction method of the subway tunnel is a shield method, and the main defect which damages the health state of the shield tunnel is tunnel leakage water.
The water leakage disease of the subway tunnel can cause weathering and peeling corrosion of the lining, and damage is caused to the lining structure; the surrounding rock is softened and deformed by the water leakage; the leakage water often occurs at the position of the anchor bolt hole, so that the anchor bolt can be rusted, and the ageing of equipment is accelerated; in severe cold winter, the crack expansion is caused by freezing of water leakage in the crack in the tunnel, so that the crack is enlarged, and the icicle formed by the water leakage can possibly invade the limit in the tunnel to influence the driving safety of the train. If the water leakage disease of the lining cannot be detected and maintained in time, the erosion of the lining becomes increasingly serious, the lining of the segment can shift or even stagger for a long time, and when serious, the segment can be peeled off or even the tunnel collapses.
At present, the detection of tunnel leakage water is mainly a manual inspection method, and the condition of leakage inspection can exist in a limited skylight period. To solve this problem, chinese patent application No. 201810066937.7 discloses a method for automatically identifying water leakage by using an image-processed tunnel image based on three-dimensional scanner to obtain tunnel point cloud information. The Chinese patent No. 202010967797.8 is also to identify the water leakage disease image, add the convolutional neural network in the deep learning algorithm, and detect the disease in an automatic and batch processing mode. Chinese patent No. 202210071582.7 is also based on deep learning algorithm, training the data set using VGG network, CNN network to identify the disease location. The existing method is mainly to search the water leakage position through image recognition, input samples are taken to obtain water leakage disease pictures, the photographing causes long field working time, and certain brightness is needed to increase the working difficulty. And the deep learning algorithm has the problems that the sample precision of the training set is not high and the final generalization capability is affected.
The traditional subway tunnel leakage water disease detection method mostly adopts means such as manual inspection, manual shooting and the like to collect data, and takes a great deal of time to process the data and compile report feedback information. The traditional detection mode is difficult to meet the requirements of efficiency, precision and safety, and a new detection technology is urgently required to improve the detection speed and the degree of automation of tunnel leakage water. The three-dimensional laser scanner can acquire the intensity information of the point cloud while acquiring the position information of the point cloud, and the characteristics of the three-dimensional laser scanner are combined, so that the simple, efficient and automatic subway tunnel water leakage detection method is provided.
Disclosure of Invention
In order to solve the problems, the invention provides a subway tunnel water leakage detection method based on point cloud data, which aims to solve the problems of low detection speed and low automation degree of subway tunnel water leakage in the prior art.
The technical scheme of the invention is that the method for detecting the tunnel leakage water damage particularly aims at a shield tunnel and comprises the following steps:
(1) Subway tunnel point cloud information is acquired by a three-dimensional laser scanner, including the position (x, y, z) of the point cloud and scalar values (Sclar). The x-axis is the direction vertical to the trend of the tunnel, the y-axis is the advancing direction of the tunnel, the z-axis is the vertical direction, the Scar is scalar information carried by the scanner, and the reflected intensity value of the point cloud information is reflected.
(2) And dividing the point cloud data along the tunnel axis according to the segment width, and dividing a section of tunnel point cloud model into a single-ring point cloud model according to the ring number.
(3) Correcting the information intensity of the point cloud on each ring slice, correcting the intensity value to 0-255, and replacing the gray value with the intensity value;
(4) And (3) utilizing the difference of the intensity of water and the tunnel surface, setting a threshold value, screening out all point clouds smaller than the threshold value in the ring sheet, identifying the point clouds as water leakage point clouds, and calculating the water leakage area occupation ratio of the single ring.
(5) The number of the single ring segments is certain, the types of the single ring segments are certain, the single ring surface area can be calculated, the single ring leakage water area can be calculated by combining the water leakage area occupation ratio, and the water leakage areas of all ring numbers in a certain mileage are counted according to the ring numbers, so that the disease can be rapidly checked.
The invention divides the tunnel by utilizing three-dimensional laser point cloud data, divides the tunnel ring number into a plurality of single-ring point cloud models, corrects the single-ring point cloud models into a gray value range by uniformly changing the point cloud scalar values in each single ring, replaces the gray value with the point cloud intensity value, and sets a threshold value. And counting the seepage water areas and the occupation ratios of all the annular sheets, and screening the annular sheets with larger seepage areas to achieve the effect of quickly detecting the seepage water areas.
The method is characterized in that in the shield tunnel leakage water disease detection processing process, the leakage water areas are distinguished by directly utilizing the intensity values of the point cloud information. The image is composed of pixel points, point cloud data are converted into the image through grating, reflection intensity of light among different materials is different, and the intensity value can be directly used as a gray value to distinguish water leakage, so that the calculation process can be simplified, and the recognition speed can be improved. Meanwhile, the position of the water leakage area is determined according to the point cloud position information, so that the efficiency and the accuracy of real-time processing are improved.
The three-dimensional laser scanning technology is applied to subway tunnel leakage water detection, tunnel section data can be monitored in real time, detection efficiency and detection precision are greatly improved, convenience is brought to subway construction parties, design parties and construction parties, and the method has high popularization value.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, it being evident that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a subway water leakage detection method based on point cloud data;
FIG. 2 is a schematic illustration of a section cut-out principle according to an embodiment of the invention;
FIG. 3 is a schematic illustration of a subway tunnel divided into single rings according to an embodiment of the present invention;
FIG. 4 is a graph of water leakage area and duty cycle statistics in accordance with an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the invention, fall within the scope of protection of the invention.
According to the embodiment of the invention, a subway tunnel water leakage detection method is provided.
As shown in fig. 1, the subway tunnel leakage water detection method according to the embodiment of the invention comprises the following steps:
101, subway tunnel point cloud data are acquired through a three-dimensional laser scanner.
102, preprocessing the original point cloud data.
And 103, dividing according to the ring numbers to generate a tunnel ring patch point cloud model.
104, correcting the point cloud intensity in the ring patch.
105, setting a threshold value to screen a leakage water area, and calculating the area ratio of the leakage water.
And 106, traversing all the annular sheets divided into the detection sections, and counting the leakage water areas of all the annular sheets. And (5) completing tunnel water leakage identification.
According to the technical scheme, subway tunnel point cloud data are firstly obtained through a three-dimensional laser scanner, then original point cloud data are preprocessed, then tunnel point cloud models are segmented according to ring numbers, then the strength of each ring point cloud is corrected, a threshold value is set to calculate the single ring leakage water area occupation ratio and the leakage water area, finally a detection section is traversed, and the leakage water area is counted according to the ring numbers to finish the leakage water identification.
The method can accurately identify the position, the area and the quantity of the subway tunnel leakage water, and greatly improves the identification precision and the identification efficiency. And the three-dimensional laser scanning technology adopts a non-contact measurement mode and does not need calibration, is little affected by the environment, can acquire data in a full time period and under the condition of no illumination, and improves the detection efficiency.
The point cloud data preprocessing comprises point cloud denoising, point cloud splicing, point cloud simplification and coordinate conversion process generation of new point cloud data.
In one embodiment, the point cloud model acquired by the laser scanner is preprocessed, and the processed point cloud model is segmented according to the ring number.
Further, firstly, normal vectors perpendicular to the rings along the axial direction of the tunnel are established according to the center coordinates of the cross sections of the initial ring and the final ring of the detection section, a cross section equation is established according to the center coordinates of the rings, and point cloud data segmentation is sequentially carried out according to the ring numbers by utilizing a point-to-surface distance formula, as shown in fig. 2.
The specific calculation process is as follows, a cross section equation passing through the center of the ring sheet is established according to the center coordinate of the initial ring number:
(x s -x e )(x-x n )+(y s -y e )(y-y n )+(z s -z e )(z-z n )＝0
wherein (xs ,y s ,z s ) To start the ring patch center coordinates, (x e ,y e ,z e ) Termination ring patch center coordinates, (x) n ,y n ,z n ) And the center coordinates of the nth ring piece.
Setting the segment width d (2 times of the distance from the point to the surface), the segment belongs to the point set phi (x) i ,y i ,z i ) Satisfies the following formula:
the detection section tunnel point cloud model is divided into n single rings according to the above formula, as shown in fig. 3.
Further, correcting the point cloud intensity in the ring patch, and converting the point cloud intensity value into 0-255 to serve as a gray value.
The specific calculation process comprises the following steps of firstly correcting the intensity value of the point cloud in the ring
wherein ,
d is the space distance of the point cloud in the ring s For reference distance (tunnel radius is generally taken), I' is the intensity value after correction, I is the point cloud intensity value before correction.
Further, the corrected intensity value is normalized to 0-255 as the gray value of the in-loop point cloud.
Wherein H is the gray value of the point cloud, I' max For the maximum value of the point cloud intensity after in-loop correction, I' min And correcting the minimum value of the point cloud intensity in the ring.
Further, according to the water leakage image recognition research data, a water leakage threshold K (generally taken (50-70) after binarization, the gray value is smaller than the threshold K as a water leakage area, and the occupation ratio rho of the water leakage area in a single ring is calculated.
In one example, calculating the area of single leakage water for a single ring point cloud model generated by the tunnel point cloud model of the detection section, and counting the area of the leakage water according to the ring number. Specifically, the method comprises the following steps:
according to the segment width, the surface area S of the segment in the single ring can be known, and the water leakage area occupation ratio of the segment in the single ring can be calculated through screening of the point cloud intensity values, so that the water leakage area of the segment in the single ring can be calculated.
Further, the water leakage surface of the decade ring piece in the detection section can be calculated, and a ring number-water leakage area point diagram is established as shown in fig. 4.
Further, a point diagram of the mileage-seepage water area can be established according to the correspondence of the ring number and the mileage, and a mileage section with serious seepage water problem can be automatically screened out by setting the maximum seepage water area.
According to the subway water leakage detection method, the gray level image of the lining on the surface of the tunnel is generated through the obtained point cloud data, the gray level image is taken as a research object, a digital image processing technology is skipped, the tunnel point cloud information is directly processed, the recognition of the water leakage is completed, the information of the position, the number, the area and the like of the water leakage is counted, and the automatic detection of the subway water leakage is realized.
In summary, by means of the above technical scheme of the present invention, the obtained point cloud data is preprocessed by obtaining the point cloud data of the tunnel, the preprocessed point cloud is divided according to the thickness of the segment to generate a single-ring segment point cloud model, the single-ring internal point cloud intensity is corrected, the intensity is used as a gray value, a gray threshold is set, the single-ring leakage water area is calculated, the leakage water area is counted according to the ring number, and the position, area and number information of the leakage water are obtained according to the relationship between the ring number and the mileage. The introduction of the three-dimensional laser scanner has the advantages of being free from the limitation of environment and light sources, reducing personnel requirements and improving the detection efficiency and the automation degree.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. The subway water leakage detection method based on the point cloud information is suitable for the shield tunnel and is characterized in that: comprises the steps of,
(1) Acquiring subway tunnel point cloud information comprising the position (x, y, z) of the point cloud and scalar values through a three-dimensional laser scanner; wherein, the x-axis is the direction vertical to the trend of the tunnel, the y-axis is the advancing direction of the tunnel, the z-axis is the vertical direction, the Scar is a scalar information carried by the scanner, and reflects the reflection intensity value of the point cloud information;
(2) Dividing the point cloud data along the tunnel axis according to the segment width, and dividing a section of tunnel point cloud model into single-ring point cloud models according to the ring number;
(3) Correcting the information intensity of the point cloud on each ring slice, correcting the intensity value to 0-255, and replacing the gray value with the intensity value;
(4) Setting a threshold value by utilizing the difference of the intensities of water and tunnel surfaces, screening out all point clouds smaller than the threshold value in the ring sheet, identifying the point clouds as leakage water point clouds, and calculating the leakage water area occupation ratio of a single ring;
(5) The number of the single-ring segments is certain, the single-ring surface area can be calculated according to certain types, the single-ring leakage water area is calculated by combining the leakage water area occupation ratio, and the diseases are rapidly checked by counting the leakage water areas of all ring numbers in a certain mileage according to the ring numbers.
2. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: the method comprises the steps of dividing a tunnel by utilizing three-dimensional laser point cloud data, dividing the tunnel ring number into a plurality of single-ring point cloud models, uniformly changing point cloud scalar values in each single ring, correcting the single-ring point cloud models into a gray value range, replacing gray values with point cloud intensity values, setting a threshold value, and counting the seepage water areas and the occupation ratios of all ring pieces.
3. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: firstly, subway tunnel point cloud data are obtained through a three-dimensional laser scanner, then original point cloud data are preprocessed, then a tunnel point cloud model is divided according to ring numbers, then the intensity of each ring of point cloud is corrected, a threshold value is set to calculate the single ring leakage water area occupation ratio and the leakage water area, finally, a detection section is traversed, and the leakage water area is counted according to the ring numbers to finish the leakage water identification.
4. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: the point cloud data preprocessing comprises point cloud denoising, point cloud splicing, point cloud simplification and coordinate conversion process generation of new point cloud data.
5. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: preprocessing a point cloud model acquired by a laser scanner, and dividing the processed point cloud model according to a ring number; firstly, establishing normal vectors perpendicular to each ring along the axial direction of a tunnel according to the center coordinates of the cross sections of the initial ring and the final ring of the detection section, establishing a cross section equation according to the center coordinates of each ring, and sequentially carrying out point cloud data segmentation according to ring numbers by utilizing a point-to-surface distance formula;
the specific calculation process is as follows, a cross section equation passing through the center of the ring sheet is established according to the center coordinate of the initial ring number:
(x s -x e )(x-x n )+(y s -y e )(y-y n )+(z s -z e )(z-z n )＝0
wherein (xs ,y s ,z s ) To start the ring patch center coordinates, (x e ,y e ,z e ) Termination ring patch center coordinates, (x) n ,y n ,z n ) The center coordinates of the nth ring piece;
setting the segment width d, the segment width is a point set phi (x) i ,y i ,z i ) Satisfies the following formula:
and dividing the detection section tunnel point cloud model into n single rings according to the formula.
6. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: correcting the intensity of the point cloud in the ring slice, and converting the intensity value of the point cloud into 0-255 to be used as a gray value;
the specific calculation process comprises the following steps of firstly correcting the intensity value of the point cloud in the ring
wherein ,
d is the space distance of the point cloud in the ring s For the reference distance, I' is the intensity value after correction, I is the point cloud intensity value before correction.
7. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: normalizing the corrected intensity value to 0-255 as the gray value of the point cloud in the ring:
wherein H is the gray value of the point cloud, I' max For the maximum value of the point cloud intensity after in-loop correction, I' min And correcting the minimum value of the point cloud intensity in the ring.
8. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: according to the water leakage image identification research data, a water leakage threshold K is set, the gray value is smaller than the threshold K to be a water leakage area, and the occupation ratio rho of the water leakage area in a single ring is calculated.
9. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: calculating the single leakage water area of a single ring point cloud model generated by the tunnel point cloud model of the detection section, and counting the leakage water area according to the ring number; specifically, the method comprises the following steps:
and determining the surface area S of the inner pipe piece of the single ring according to the width of the pipe piece, and calculating the seepage water area occupation ratio of the single ring pipe piece through screening the point cloud intensity values, thereby calculating the seepage water area of the pipe piece of the ring pipe.
10. The subway water leakage detection method based on the point cloud information as set forth in claim 1, wherein: establishing a point diagram of mileage-seepage water area according to the ring number and mileage, and automatically screening out a mileage section with serious seepage water problem by setting the maximum seepage water area;
according to the subway leakage water detection method, a gray level image of the lining on the surface of the tunnel is generated through the obtained point cloud data, the gray level image is taken as a research object, a digital image processing technology is skipped, the tunnel point cloud information is directly processed, the recognition of the leakage water is completed, the position, the number and the area information of the leakage water are counted, and the automatic detection of the subway leakage water is realized.
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