CN116977732A - Subway tunnel water seepage detection method and device based on point cloud reflection intensity - Google Patents
Subway tunnel water seepage detection method and device based on point cloud reflection intensity Download PDFInfo
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Abstract
The embodiment of the specification provides a subway tunnel water seepage detection method and device based on point cloud reflection intensity, wherein the method comprises the following steps: collecting point cloud data of a tunnel to be detected; removing impurity points in the point cloud data; selecting target point cloud data according to preset reflection intensity; and carrying out cluster classification on the target point cloud data to obtain the water seepage point cloud data. The technical scheme provided by the application is used for solving the problems of low detection efficiency and low detection precision in the prior art.
Description
Technical Field
The present document relates to the technical field of laser point clouds, and in particular, to a method and a device for detecting water seepage of a subway tunnel based on the reflection intensity of point clouds.
Background
Along with the increase of subway operation time, diagnosis and control of subway tunnel diseases become the primary work for guaranteeing subway operation.
In the prior art, a mode of manually extracting tunnel leakage water is adopted to realize diagnosis of subway tunnel diseases.
However, as the time taken for operation of the subway increases, the length of the subway tunnel increases, resulting in inefficiency in the above-described manner. Meanwhile, the detection accuracy is easily reduced due to the influence of human subjectivity.
Disclosure of Invention
In view of the above analysis, the present application aims to provide a subway tunnel water seepage detection method and device based on point cloud reflection intensity, so as to solve at least one of the above technical problems.
In a first aspect, one or more embodiments of the present disclosure provide a subway tunnel water seepage detection method based on a point cloud reflection intensity, including:
collecting point cloud data of a tunnel to be detected;
removing impurity points in the point cloud data;
selecting target point cloud data according to preset reflection intensity;
and carrying out cluster classification on the target point cloud data to obtain the water seepage point cloud data.
Further, the removing the impurity points in the point cloud data includes:
determining a distance between each data point in the point cloud data and an adjacent data point;
according to the determined distance, determining the confidence of the Gaussian distribution corresponding to the distance;
and removing the impurity points in the point cloud data according to the confidence.
Further, selecting the target point cloud data according to the preset reflection intensity includes:
configuring a speed sensor for a single-frame point cloud in the point cloud data to obtain three-dimensional point cloud data;
and selecting target point cloud data from the three-dimensional point cloud data according to preset reflection intensity.
Further, the clustering classification of the target point cloud data to obtain the water seepage point cloud data includes:
for each data point in the target point cloud, judging whether the number of the data points in the neighborhood corresponding to the current data point exceeds a preset threshold value;
when the number of the data points in the neighborhood is determined to exceed a preset threshold, obtaining a data cluster according to the current data point;
and obtaining the water seepage point cloud data according to the data cluster.
Further, the obtaining a data cluster according to the current data point includes:
determining, in the neighborhood, all points at which the current data point density is reachable;
wherein the current data point density and all points at which the current data point density is reachable constitute the data cluster.
Further, after the clustering classification is performed on the target point cloud data to obtain the water seepage point cloud data, the method further includes:
and marking the water seepage point cloud data as highlight.
In a second aspect, an embodiment of the present application provides a subway tunnel water seepage detection device based on point cloud reflection intensity, including: the device comprises an acquisition module, a data processing module and a clustering module;
the acquisition module is used for acquiring point cloud data of the tunnel to be detected;
the data processing module is used for removing impurity points in the point cloud data; selecting target point cloud data according to preset reflection intensity;
and the clustering module is used for carrying out clustering classification on the target point cloud data to obtain the water seepage point cloud data.
Further, the data processing module is used for determining the distance between each data point in the point cloud data and the adjacent data point; according to the determined distance, determining the confidence of the Gaussian distribution corresponding to the distance; and removing the impurity points in the point cloud data according to the confidence.
Further, the data processing module is configured to determine, for each data point in the target point cloud, whether the number of data points in a neighborhood corresponding to the current data point exceeds a preset threshold; when the number of the data points in the neighborhood is determined to exceed a preset threshold, obtaining a data cluster according to the current data point; and obtaining the water seepage point cloud data according to the data cluster.
In a third aspect, an embodiment of the present application provides a computer readable storage medium, where a computer program is stored, where the computer program, when executed by a processor, implements the steps of the subway tunnel water seepage detection method based on the point cloud reflection intensity according to any one of the first aspects.
Compared with the prior art, the application can at least realize the following technical effects:
aiming at the characteristic of more subway tunnel interference items, impurity points in point cloud data are removed before the water seepage point cloud data are determined, so that detection accuracy is improved. Aiming at the characteristic of dark light of a subway tunnel, the reflection intensity of a water seepage area is utilized, and the cloud data of a target point is selected so as to remove the area which cannot be permeated with water, so that the data processing amount is reduced, and the detection efficiency is further improved. The above-mentioned process can be accomplished by the procedure, does not need artifical participation, consequently compare in prior art, greatly improved detection efficiency and detection precision.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the following brief description of the drawings is provided for the embodiments or of the solutions of the prior art, it being apparent that the drawings in the following description are only some of the embodiments described in the present description, from which other drawings can be obtained, without inventive faculty for a person skilled in the art.
Fig. 1 is a flowchart of a subway tunnel water seepage detection method based on point cloud reflection intensity according to one or more embodiments of the present disclosure.
Detailed Description
In order to make the technical solutions in one or more embodiments of the present specification better understood by those skilled in the art, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present specification, but not all embodiments. All other embodiments, which may be made by one or more embodiments of the present disclosure without undue burden by one of ordinary skill in the art, are intended to be within the scope of the present disclosure.
The laser scanning equipment can acquire up to millions of data points per second, and the massive point clouds provide possibility for identifying tunnel leakage water diseases. The application adopts the laser scanning technology to determine and identify the tunnel leakage water disease.
However, due to the influence of tunnel dust, water vapor and the like, unexpected data noise points exist in the scanned data, in addition, errors in measurement can generate sparse outliers, and the points can cause unnecessary information to be contained in final output, and meanwhile, unnecessary operation amount exists.
In order to cope with the above problems, an embodiment of the present application provides a subway tunnel water seepage detection method based on point cloud reflection intensity, as shown in fig. 1, including the following steps:
and step 1, collecting point cloud data of a tunnel to be detected.
And step 2, removing the impurity points in the point cloud data.
In embodiments of the application, tunnel dust and moisture can cause contamination points that can interfere with subsequent processing. Therefore, pretreatment is needed, and the treatment method is as follows:
determining a distance between each data point in the point cloud data and an adjacent data point; according to the determined distance, determining the confidence coefficient of the Gaussian distribution corresponding to the distance; and removing the impurity points in the point cloud data according to the confidence. Specifically, for each point, the average distance to all its nearest points is calculated. It is assumed that the average distance of all points conforms to a gaussian distribution whose shape is determined by the mean and standard deviation. And determining the confidence of the average distance according to the shape of the Gaussian distribution, and determining the standard range of the average distance. As such, points outside of the standard range may be defined as outliers and may be removed from the data.
And 3, selecting target point cloud data according to the preset reflection intensity.
In the embodiment of the application, the subway tunnel is of a three-dimensional structure, the collected point cloud data is of a single-dimensional data, and a speed sensor is required to be configured for a single frame of point cloud in the point cloud data to obtain three-dimensional point cloud data in order to improve detection accuracy. Because the water permeable areas are darker than the non-water permeable areas, the reflective characteristic intensity is significantly different from other areas. Based on this, target point cloud data is selected from the three-dimensional point cloud data according to the preset reflection intensity. Preferably, point cloud data with reflection intensity of 5-15 are screened out.
And step 4, carrying out cluster classification on the target point cloud data to obtain the water seepage point cloud data.
In the embodiment of the application, the clustering classification process is as follows:
for each data point in the target point cloud, judging whether the number of the data points in the neighborhood corresponding to the current data point exceeds a preset threshold value; when the number of the data points in the neighborhood is determined to exceed a preset threshold, obtaining a data cluster according to the current data point; and obtaining the water seepage point cloud data according to the data clusters.
The method for obtaining the data cluster comprises the following steps:
in the neighborhood, all points where the current data point density is reachable are determined. The current data point density and all points where the current data point density is reachable constitute a data cluster. To illustrate the meaning of density reachable, an example is given where data point P2 is present in the neighborhood of data point P1, then the relationship between P1 and P2 is density direct. If the data point P3 exists in the neighborhood of the data point P2, the relationship between P2 and P3 is density direct, and the relationship between P1 and P3 is density reachable.
To further illustrate the process of step 4, the present application gives the following specific examples:
1) The parameter neighborhood radius rpps and the threshold pMinPt are first initialized. The point tag in the point cloud set, i.e. the assignment of the point tag Flag, is unvisited, cluster number ccmaster=0.
2) Next, selecting a point p from the point set D, and if flag=visited, reselecting the next point; if flag=unvisited, it is determined whether it is a core object point.
3) If the point is not a core object point, i.e. the number of objects contained in a neighborhood with radius rpps is smaller than a given threshold pMinPts, classifying as a noise point, cclumer= -1; if the point is a core object point, that is, the number of objects contained in a neighborhood with radius rpss is less than or equal to a given threshold pMinPts, querying the neighborhood of the core object, searching all points with density reaching from the point p to form a density connected set, generating a new cluster, that is, ccmaster=ccmaster+1, and assigning all points Flag in the connected set as visited.
4) Repeating the steps 2) and 3) until all points flag=identified of the point cloud.
5) At the end of clustering, output Cluster ccmaster (i), (i=1, 2,3, …, N), cluster is the total number of clusters.
In the embodiment of the application, in order to clearly show the water penetration point cloud data, after the water penetration point cloud data is obtained, the water penetration point cloud data is marked as highlight. Wherein highlighting is to adjust contrast of the water penetration point cloud data instead of changing color.
The embodiment of the application provides a subway tunnel water seepage detection device based on point cloud reflection intensity, which comprises the following components: the device comprises an acquisition module, a data processing module and a clustering module;
the acquisition module is used for acquiring point cloud data of the tunnel to be detected;
the data processing module is used for removing impurity points in the point cloud data; selecting target point cloud data according to preset reflection intensity;
and the clustering module is used for carrying out clustering classification on the target point cloud data to obtain the water seepage point cloud data.
Further, the data processing module is used for determining the distance between each data point in the point cloud data and the adjacent data point; according to the determined distance, determining the confidence of the Gaussian distribution corresponding to the distance; and removing the impurity points in the point cloud data according to the confidence.
Further, the data processing module is configured to determine, for each data point in the target point cloud, whether the number of data points in a neighborhood corresponding to the current data point exceeds a preset threshold; when the number of the data points in the neighborhood is determined to exceed a preset threshold, obtaining a data cluster according to the current data point; and obtaining the water seepage point cloud data according to the data cluster.
The embodiment of the application provides a computer readable storage medium, on which a computer program is stored, which when executed by one or more processors, implements the subway tunnel water seepage detection method based on the point cloud reflection intensity according to any embodiment.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In the 30 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each unit may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present specification.
One skilled in the relevant art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
One or more embodiments of the present specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is by way of example only and is not intended to limit the present disclosure. Various modifications and changes may occur to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. that fall within the spirit and principles of the present document are intended to be included within the scope of the claims of the present document.
Claims (10)
1. The subway tunnel water seepage detection method based on the point cloud reflection intensity is characterized by comprising the following steps of:
collecting point cloud data of a tunnel to be detected;
removing impurity points in the point cloud data;
selecting target point cloud data according to preset reflection intensity;
and carrying out cluster classification on the target point cloud data to obtain the water seepage point cloud data.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the removing the impurity points in the point cloud data comprises the following steps:
determining a distance between each data point in the point cloud data and an adjacent data point;
according to the determined distance, determining the confidence of the Gaussian distribution corresponding to the distance;
and removing the impurity points in the point cloud data according to the confidence.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
selecting target point cloud data according to preset reflection intensity, including:
configuring a speed sensor for a single-frame point cloud in the point cloud data to obtain three-dimensional point cloud data;
and selecting target point cloud data from the three-dimensional point cloud data according to preset reflection intensity.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the step of carrying out cluster classification on the target point cloud data to obtain the water seepage point cloud data comprises the following steps:
for each data point in the target point cloud, judging whether the number of the data points in the neighborhood corresponding to the current data point exceeds a preset threshold value;
when the number of the data points in the neighborhood is determined to exceed a preset threshold, obtaining a data cluster according to the current data point;
and obtaining the water seepage point cloud data according to the data cluster.
5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,
the step of obtaining the data cluster according to the current data point comprises the following steps:
determining, in the neighborhood, all points at which the current data point density is reachable;
wherein the current data point density and all points at which the current data point density is reachable constitute the data cluster.
6. The method of claim 1, wherein after clustering the target point cloud data to obtain water penetration point cloud data, the method further comprises:
and marking the water seepage point cloud data as highlight.
7. Subway tunnel infiltration detection device based on some cloud reflection intensity, its characterized in that includes: the device comprises an acquisition module, a data processing module and a clustering module;
the acquisition module is used for acquiring point cloud data of the tunnel to be detected;
the data processing module is used for removing impurity points in the point cloud data; selecting target point cloud data according to preset reflection intensity;
and the clustering module is used for carrying out clustering classification on the target point cloud data to obtain the water seepage point cloud data.
8. The apparatus of claim 7, wherein the device comprises a plurality of sensors,
the data processing module is used for determining the distance between each data point in the point cloud data and the adjacent data point; according to the determined distance, determining the confidence of the Gaussian distribution corresponding to the distance; and removing the impurity points in the point cloud data according to the confidence.
9. The apparatus of claim 7, wherein the device comprises a plurality of sensors,
the data processing module is used for judging whether the number of data points in the neighborhood corresponding to the current data point exceeds a preset threshold value or not according to each data point in the target point cloud; when the number of the data points in the neighborhood is determined to exceed a preset threshold, obtaining a data cluster according to the current data point; and obtaining the water seepage point cloud data according to the data cluster.
10. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program when executed by a processor implements the steps of the subway tunnel water seepage detection method based on the point cloud reflection intensity according to any one of claims 1 to 6.
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