A method for detecting leakage water of full-face tunnel lining based on three-dimensional laser array

Abstract

The application provides a kind of full-face tunnel lining leakage water detection method based on three-dimensional laser array, comprising: using several kinds of preset laser arrangement mode to carry out beam scanning on full-face tunnel, obtaining the scanning feedback information corresponding to each preset laser arrangement mode, constructing the tunnel appearance image of full-face tunnel, obtaining several tunnel non-smooth features of full-face tunnel through pixel recognition, respectively carrying out color analysis and texture analysis on each tunnel non-smooth feature, determining several key detection areas, generating several scanning detail information of key detection area from the scanning sub-information of each preset laser arrangement mode on key detection area, restoring details to tunnel appearance image, judging whether the key detection area leaks water, generating and displaying the leakage water detection result of full-face tunnel, using laser scanning technology to collect the tunnel condition of full-face tunnel, gradually positioning its water leakage position, and facilitating workers to repair in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel defect detection, and particularly relates to a full-face tunnel lining leakage water detection method based on a three-dimensional laser array. BACKGROUND

[0002] Full-face tunnel refers to a mode in which the whole section of a tunnel is excavated simultaneously on a same working face. Different from a traditional step-by-step excavation mode, the full-face excavation excavates the whole section of the tunnel at one time on a complete working face. The full-face tunnel has some unique advantages in tunnel construction, and is particularly suitable for large-scale, deep or hard rock tunnel projects. However, the tunnel is prone to leakage in a long-term operation process. Even if relevant plugging and drainage measures are taken, leakage water may still occur at other positions, which is difficult to be cured, and in particular, the leakage water at the top of the arch is prone to directly dripping on the road surface, which may cause tire skidding when the amount of leakage water is large, and thus endangers the safety of driving. Statistics of highway departments show that about 30% of highway tunnels in China have serious leakage water diseases. Statistics of railway departments show that 28.4% of railway tunnels in China have serious leakage water diseases. With the rapid development of tunnel projects in China, tunnel diseases have become increasingly prominent. Leakage water is one of the main diseases of tunnels and is also the root cause of other diseases of tunnels, and the health problem of tunnels has become increasingly prominent.

[0003] Therefore, the present application provides a full-face tunnel lining leakage water detection method based on a three-dimensional laser array. SUMMARY

[0004] The full-face tunnel lining leakage water detection method based on the three-dimensional laser array collects the tunnel conditions of the full-face tunnel by using the technology of laser scanning, and then locates the leakage water positions step by step, so that the leakage water positions can be found in time, and workers can timely perform maintenance.

[0005] The present application provides a full-face tunnel lining leakage water detection method based on a three-dimensional laser array, which comprises the following steps:

[0006] Step 1: a plurality of preset laser arrangement modes are used to perform beam scanning on the full-face tunnel, and scanning feedback information corresponding to each of the preset laser arrangement modes is obtained;

[0007] Step 2: a tunnel appearance image of the full-face tunnel is constructed according to the scanning feedback information, pixel recognition is performed on the appearance image, and a plurality of tunnel non-smooth features of the full-face tunnel are obtained;

[0008] Step 3: color analysis and texture analysis are respectively performed on each of the tunnel non-smooth features, and a plurality of key detection areas of the full-face tunnel are determined;

[0009] Step 4: constructing several scanning detail information of each said key detection area according to the scanning sub-information of each said preset laser arrangement mode on said key detection area;

[0010] Step 5: using said scanning detail information to restore details of said tunnel appearance image, judging whether said key detection area leaks water according to the restoration result, generating the leakage water detection result of said full-section tunnel and displaying.

[0011] In an implementable manner,

[0012] Said step 1 comprises:

[0013] Step 11: using several preset laser arrangement modes to perform several times of laser scanning on said full-section tunnel, obtaining the field scanning information corresponding to each said preset laser arrangement mode;

[0014] Step 12: according to the beam angle transformation process corresponding to each said preset laser arrangement mode, performing spatial projection on the corresponding said field scanning information, obtaining the corresponding spatial distribution information;

[0015] Step 13: respectively identifying the beam-tunnel intersection features corresponding to each said spatial distribution information, generating the scanning feedback information of said full-section tunnel corresponding to said preset laser arrangement mode.

[0016] In an implementable manner,

[0017] Said step 2 comprises:

[0018] Step 21: constructing the corresponding tunnel imaging path according to the beam angle transformation process corresponding to each said preset laser arrangement mode, positioning the intersection points between different said tunnel imaging paths to determine several strong scanning positions of said full-section tunnel, and respectively determining several scanning feedback sub-information corresponding to each said strong scanning position;

[0019] Step 22: constructing the image recombination framework of said full-section tunnel according to said beam transformation process, respectively inputting each said scanning feedback information into said image recombination framework for fixation, inputting the unfixed sub-information contained in each said scanning feedback information into said image recombination framework for image recombination according to the fixation result, and generating the tunnel appearance image of said full-section tunnel;

[0020] Step 23: setting the source pointer of corresponding pixel points in said tunnel appearance image according to the sub-information source corresponding to each said scanning feedback sub-information, dividing said tunnel appearance image into several image domains according to the pointer distribution of said tunnel appearance image, and selecting the corresponding recognition intensity based on the domain brightness corresponding to each said image domain.

[0021] Step 24: respectively performing pixel recognition of each of the image domains with corresponding intensity, obtaining several texture features of the full-section tunnel, performing three-dimensional enhancement on each of the texture features according to the pixel gradient transformation information corresponding to the texture feature, and generating several tunnel non-smooth features of the full-section tunnel.

[0022] In an implementable manner,

[0023] Further comprising:

[0024] performing color conversion on the tunnel appearance image, and judging whether the full-section tunnel contains water mark features according to the conversion result;

[0025] If yes, positioning and displaying a water leakage position corresponding to the water mark features in the tunnel appearance image.

[0026] In an implementable manner,

[0027] The step 3 comprises:

[0028] Step 31: respectively inputting each of the tunnel non-smooth features into a gray level co-occurrence matrix for multi-dimensional analysis, obtaining local texture information of the tunnel non-smooth features, and respectively performing preliminary water seepage evaluation on each of the local texture information by using a water sample;

[0029] Step 32: screening several target tunnel non-smooth features belonging to suspected water seepage conditions according to the evaluation result, obtaining a feature sub-image corresponding to the target tunnel non-smooth features in the tunnel appearance image, fusing the local texture information and the feature sub-image, and constructing a layered suspected water seepage map structure of the full-section tunnel.

[0030] Step 33: performing a water seepage test on the layered suspected water seepage map structure, obtaining several positions of the full-section tunnel that do not pass the test, and regarding a range between the positions of the full-section tunnel that do not pass the test with a straight-line distance less than a specified distance as a key detection area of the full-section tunnel.

[0031] In an implementable manner,

[0032] Further comprising:

[0033] Respectively obtaining a region area corresponding to each of the key detection areas;

[0034] Analyzing a water seepage collapse risk level corresponding to the key detection area according to the region area, and performing a risk warning of a corresponding level on each of the key detection areas.

[0035] In an implementable manner,

[0036] The step 4 comprises:

[0037] Step 41: respectively acquire the field scanning information corresponding to each of the preset laser arrangement modes, and screen the scanning sub-information corresponding to each of the key detection areas in the field scanning information;

[0038] Step 42: respectively perform local focusing on each of the scanning sub-information to obtain a plurality of reinforced focal points corresponding to the key detection areas;

[0039] Step 43: respectively map each of the reinforced focal points in the corresponding key detection area to perform pixel enhancement on the key detection area;

[0040] Step 44: respectively acquire the pixel information corresponding to each of the key detection areas to construct the scanning detail information of the key detection areas.

[0041] In an implementable manner,

[0042] The step 5 comprises:

[0043] Step 51: respectively identify the information value corresponding to each of the scanning detail information, divide the scanning detail information into in-range information and out-of-range information according to the discrete characteristics of all the information values, cluster the in-range information to obtain a plurality of information cluster centers; and

[0044] Step 52: perform iterative morphological training on the water sample, when the training result is consistent with the information cluster center, determine the water quantity of the key detection area corresponding to the information cluster center according to the training result value, and determine that the internal key detection area has a water seepage / leakage phenomenon;

[0045] Step 53: when the information cluster center is inconsistent with the training result, respectively perform iterative sharpening processing on the external key detection area corresponding to each of the out-of-range information, sequentially enhance the local contrast corresponding to each pixel point in the external key detection area, when the local contrast difference is higher than the average difference, determine that the external key detection area has a water seepage / leakage phenomenon according to the corresponding difference value;

[0046] Step 54: respectively acquire the detection result corresponding to each of the key detection areas, construct the water seepage detection result of the full-face tunnel, and transmit the detection result to a designated terminal for display.

[0047] In an implementable manner,

[0048] Further comprising:

[0049] When there is no water seepage / leakage phenomenon in the full-face tunnel, generate and display a qualified report of the full-face tunnel.

[0050] In an implementable manner,

[0051] Further comprising:

[0052] According to the leakage water detection result, the overall collapse risk level of the full-face tunnel is analyzed, and a corresponding level warning is performed.

[0053] The implementable beneficial effects of the above technical solution are: in order to quickly locate the water seepage and leakage position of the tunnel, the field information of the full-face tunnel is first collected, so a plurality of laser arrangement modes are used to scan the full-face tunnel, corresponding scanning feedback information is obtained, the obtained scanning feedback information is further used to construct the tunnel appearance image of the full-face tunnel, then the key detection area in the full-face tunnel is located through pixel recognition, color analysis and texture analysis, the area is repeatedly detected, scanning detail information contained therein is obtained, finally the scanning detail information is used to restore the details in the tunnel appearance image, so as to judge whether the key detection area is water seepage or leakage, and generate a corresponding report, through such a way, the full-face tunnel can be analyzed in all directions, and the water seepage and leakage conditions can be detected at the same time, so that the water seepage and leakage are timely detected, and the maintenance intensity of the full-face tunnel is improved.

[0054] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by means of the structure particularly pointed out in the written description and the accompanying drawings.

[0055] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, and are used as an explanation of the embodiments of the present application together with the embodiments of the present application, and do not constitute a limitation on the present application. In the drawings:

[0057] Figure 1 It is a work flow diagram of a full-face tunnel lining leakage water detection method based on a three-dimensional laser array in an embodiment of the present application;

[0058] Figure 2 It is a work flow diagram of step 1 of a full-face tunnel lining leakage water detection method based on a three-dimensional laser array in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present application will be described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used as an explanation and illustration of the present application, and do not serve as a limitation on the present application.

[0060] Embodiment 1

[0061] The embodiment provides a three-dimensional laser array-based full-face tunnel lining leakage water detection method, which comprises the following steps of: Figure 1

[0062] Step 1: performing light beam scanning on a full-face tunnel by using a plurality of preset laser arrangement modes to obtain scanning feedback information corresponding to each of the preset laser arrangement modes;

[0063] Step 2: constructing a tunnel appearance image of the full-face tunnel according to the scanning feedback information, performing pixel recognition on the appearance image to obtain a plurality of tunnel non-smooth features of the full-face tunnel;

[0064] Step 3: respectively performing color analysis and texture analysis on each of the tunnel non-smooth features to determine a plurality of key detection areas of the full-face tunnel;

[0065] Step 4: constructing a plurality of scanning detail information of each of the key detection areas according to scanning sub-information of each of the preset laser arrangement modes on the key detection areas;

[0066] Step 5: performing detail restoration on the tunnel appearance image by using the scanning detail information, judging whether the key detection areas are leaking water according to a restoration result, generating a leakage water detection result of the full-face tunnel and displaying the leakage water detection result.

[0067] In this example, the preset laser arrangement modes comprise a square, a hexagon and a circle.

[0068] In this example, the scanning feedback information represents information generated after laser is refracted after colliding with a tunnel wall of the full-face tunnel.

[0069] In this example, the tunnel non-smooth feature represents a feature presented by an unbalanced area in the full-face tunnel.

[0070] In this example, the color analysis represents a process of identifying whether the tunnel non-smooth feature contains information consistent with the color of water, and the texture analysis represents a process of identifying whether the tunnel non-smooth feature contains information consistent with the texture of water.

[0071] In this example, the scanning detail information represents a result of restoring information of the key detection area by using the scanning sub-information.

[0072] ​The working principle and beneficial effects of the above technical solution are as follows: in order to quickly locate the water seepage position of the tunnel, the field information of the full-section tunnel is first collected, so a plurality of laser arrangement modes are used to scan the full-section tunnel, and corresponding scanning feedback information is obtained, the obtained scanning feedback information is further used to construct the tunnel appearance image of the full-section tunnel, and then the key detection area in the full-section tunnel is located through pixel recognition, color analysis and texture analysis, the area is repeatedly detected, scanning detail information contained therein is obtained, and finally the scanning detail information is used to restore the details in the tunnel appearance image, so as to judge whether the key detection area seeps or leaks, and generate a corresponding report. Through such a way, not only the full-section tunnel can be analyzed in all directions, but also the water seepage and water leakage can be detected at the same time, the timely water seepage and water leakage is realized, and the maintenance of the full-section tunnel is improved.

[0073] Embodiment 2

[0074] Based on the embodiment 1, the one kind based on three-dimensional laser array's full-section tunnel lining water leakage detection method, the step 1, including:

[0075] Step 11: a plurality of preset laser arrangement modes are used to scan the full-section tunnel several times, and field scanning information corresponding to each preset laser arrangement mode is obtained;

[0076] Step 12: according to the light beam angle transformation process corresponding to each preset laser arrangement mode, the corresponding field scanning information is projected in space, and corresponding space distribution information is obtained;

[0077] Step 13: the light beam-tunnel intersection feature corresponding to each space distribution information is identified respectively, and the scanning feedback information of the full-section tunnel to the corresponding preset laser arrangement mode is generated.

[0078] In this example, one preset laser arrangement mode corresponds to one light beam scanning;

[0079] In this example, the space distribution information represents the result of displaying the field scanning information in the three-dimensional space;

[0080] In this example, the light beam-tunnel intersection feature represents the feature generated when the light beam of one light beam scanning and the tunnel wall of the tunnel intersect.

[0081] The working principle and beneficial effects of the above technical solution are as follows: when scanning the full-face tunnel, a plurality of preset laser arrangement modes are used for scanning to obtain a plurality of field scanning information, then the spatial distribution information is constructed through spatial projection, and finally the scanning feedback information of the full-face tunnel is constructed according to the light beam-tunnel intersection characteristics in the spatial distribution information. In this way, the structure of the full-face tunnel can be scanned in all directions, and the accuracy of subsequent appearance mapping is improved.

[0082] Embodiment 3

[0083] Based on the embodiment 1, the step 2 comprises:

[0084] Step 21: constructing a corresponding tunnel imaging path according to the light beam angle transformation process corresponding to each of the preset laser arrangement modes, positioning the intersection points between different tunnel imaging paths to determine a plurality of strong scanning positions of the full-face tunnel, and respectively determining a plurality of scanning feedback sub-information corresponding to each of the strong scanning positions;

[0085] Step 22: constructing an image recombination framework of the full-face tunnel according to the light beam transformation process, respectively inputting each of the scanning feedback information into the image recombination framework for fixation, inputting the unfixed sub-information contained in each of the scanning feedback information into the image recombination framework for image recombination according to the fixation result, and generating a tunnel appearance image of the full-face tunnel;

[0086] Step 23: setting a source pointer for the corresponding pixel points in the tunnel appearance image according to the sub-information source corresponding to each of the scanning feedback sub-information, dividing the tunnel appearance image into a plurality of image domains according to the pointer distribution of the tunnel appearance image, and selecting a corresponding recognition intensity based on the domain brightness corresponding to each of the image domains;

[0087] Step 24: respectively performing pixel recognition of each of the image domains at a corresponding intensity to obtain a plurality of texture features of the full-face tunnel, and performing three-dimensional enhancement on the texture features according to the pixel gradient transformation information corresponding to each of the texture features to generate a plurality of tunnel non-smooth features of the full-face tunnel.

[0088] In this example, the tunnel imaging path represents a path for spatial arrangement of scanning feedback information;

[0089] In this example, the strong scanning position represents a position that has been scanned twice or more than twice;

[0090] In this example, the image recombination framework represents a framework for setting the image format in advance;

[0091] In this example, the source pointer represents a pointer of a memory location of a pixel point;

[0092] In this example, the image domain represents the result of dividing the tunnel appearance image;

[0093] In this example, the recognition intensity is in a positive correlation with the domain brightness, and the higher the domain brightness, the higher the recognition intensity;

[0094] In this example, the pixel gradient transformation information represents the information presented by the pixel value transformation in the texture feature.

[0095] The working principle and beneficial effects of the above technical solution are as follows: the strong scanning position in the scanning process and the corresponding scanning feedback sub-information are determined through the beam transformation process of the preset laser arrangement mode, the image reconstruction framework of the full-face tunnel is constructed according to the beam transformation process, then the scanning feedback information is input into the image reconstruction framework, the tunnel appearance image of the full-face tunnel is generated, the tunnel appearance image is divided into a plurality of image domains according to the distribution presented by the existing source pointer in the tunnel appearance image, the recognition intensity is set for the domain brightness of each image domain, the pixel recognition is performed on the image domain, the phenomenon of distortion of the tunnel appearance image caused by uniform recognition is effectively avoided, the clear texture feature of the full-face tunnel is obtained, and finally the three-dimensional enhancement is performed on the texture feature according to the pixel gradient transformation information of the texture feature, the tunnel non-smooth feature of the full-face tunnel is obtained, all textures of the full-face tunnel can be processed in this way, and the efficiency and quality of subsequent key detection are improved.

[0096] Embodiment 4

[0097] On the basis of embodiment 3, the kind of full-face tunnel lining leakage water detection method based on three-dimensional laser array further includes:

[0098] The color conversion is performed on the tunnel appearance image, and whether the full-face tunnel contains the water mark feature is judged according to the conversion result;

[0099] If yes, the water leakage position corresponding to the water mark feature is located and displayed in the tunnel appearance image.

[0100] The working principle and beneficial effects of the above technical solution are as follows: when the tunnel appearance image contains the water mark feature, it indicates that serious water leakage occurs, so the water leakage position is preferentially marked and positioned and displayed.

[0101] Embodiment 5

[0102] On the basis of embodiment 1, the kind of full-face tunnel lining leakage water detection method based on three-dimensional laser array, as shown in Figure 2 The step 3 includes:

[0103] Step 31: input each of the tunnel non-smooth features into a gray level co-occurrence matrix for multi-dimensional analysis, respectively, to obtain local texture information of the tunnel non-smooth features, and use water samples to preliminarily evaluate each of the local texture information;

[0104] Step 32: according to the evaluation results, screen a plurality of target tunnel non-smooth features belonging to suspected water seepage conditions, obtain a feature sub-image corresponding to the target tunnel non-smooth features in the tunnel appearance image, fuse the local texture information and the feature sub-image, and construct a layered suspected water seepage graph structure of the full-face tunnel;

[0105] Step 33: perform a water seepage test on the layered suspected water seepage graph structure, obtain a plurality of positions in the full-face tunnel that do not pass the test, and regard a range between positions that are less than a specified distance apart in straight line distance as a key detection area of the full-face tunnel.

[0106] In this example, the multi-dimensional analysis includes a local color dimension, a local texture dimension, and a local contrast dimension.

[0107] In this example, the local texture feature represents information of each dimension presented by the texture of each local part of the tunnel non-smooth feature.

[0108] In this example, the layered suspected water seepage graph structure represents an exploded structure diagram of a suspected water seepage position in the full-face tunnel.

[0109] In this example, the specified distance is 50 centimeters.

[0110] Working principle and beneficial effects of the above technical solution: the local texture information of the tunnel non-smooth feature is obtained by using the gray level co-occurrence matrix to perform multi-dimensional analysis on the tunnel non-smooth feature, and then a preliminary water seepage evaluation is performed. The key detection area of the full-face tunnel is determined by re-mapping the target tunnel non-smooth features suspected of water seepage, performing a water seepage test, and fusing the test positions that do not pass the water seepage test. Through this way, all water seepage positions in the tunnel can be quickly located, and the water seepage positions within a short distance are fused to determine the key detection area, effectively improving the quality and efficiency of subsequent water seepage and leakage detection. The positions that are not water seepage and not leakage are temporarily not detected, reducing the consumption of manpower and material resources, effectively improving the maintenance efficiency of the full-face tunnel, and avoiding inconvenience to the road caused by long-term maintenance.

[0111] Embodiment 6

[0112] Based on the embodiment 5, the kind of full-face tunnel lining water leakage detection method based on three-dimensional laser array further includes:

[0113] Respectively acquire the area of each of the key detection regions;

[0114] According to the area, analyze the water seepage collapse risk level of each of the key detection regions, and perform a risk warning of the corresponding level for each of the key detection regions.

[0115] The working principle and beneficial effects of the above technical solution are as follows: according to the area of the key detection region, the collapse level after the collapse of the region is determined, and a corresponding warning is performed, so that early warning is realized, the purpose of effective warning is achieved, and the influence of water seepage on the normal use of the tunnel is avoided.

[0116] Embodiment 7

[0117] On the basis of embodiment 1, the three-dimensional laser array-based full-face tunnel lining water seepage detection method, the step 4 comprises:

[0118] Step 41: respectively acquire the field scanning information corresponding to each of the preset laser arrangement modes, and screen the scanning sub-information corresponding to each of the key detection regions in the field scanning information;

[0119] Step 42: respectively perform local focusing on each of the scanning sub-information to obtain a plurality of enhanced focal points corresponding to the key detection regions;

[0120] Step 43: respectively map each of the enhanced focal points in the corresponding key detection region to perform pixel enhancement on the key detection region;

[0121] Step 44: respectively acquire the pixel information corresponding to each of the key detection regions, and construct the scanning detail information of the key detection regions.

[0122] In this example, the enhanced focal point represents the position with the largest probability of water seepage in the key detection region.

[0123] The working principle and beneficial effects of the above technical solution are as follows: by screening the scanning sub-information in the field scanning information, the key detection region is locally focused, the enhanced focal point of the key detection region is determined, then the key detection region is synchronously pixel-enhanced by using the enhanced focal point, the scanning detail information of the key detection region is constructed, and through such a technology, the position where water seepage occurs can be positioned again, the scanning detail information of the key detection region is generated, and the next detection and identification work is facilitated.

[0124] Embodiment 8

[0125] On the basis of embodiment 1, the three-dimensional laser array-based full-face tunnel lining water seepage detection method, the step 5 comprises:

[0126] Step 51: respectively identify the information value corresponding to each of the scanning detail information, divide the scanning detail information into discrete range information and discrete range out information according to the discrete characteristics of all the information values, cluster the discrete range information to obtain a plurality of information cluster centers;

[0127] Step 52: iteratively train the water sample, when the training result is consistent with the information cluster center, determine the water quantity of the key detection area corresponding to the information cluster center according to the training result value, and determine that the internal key detection area appears water seepage / leakage phenomenon;

[0128] Step 53: when the information cluster center is inconsistent with the training result, respectively perform iterative sharpening processing on each of the external key detection area corresponding to the discrete range out information, and sequentially enhance the local contrast corresponding to each pixel point in the external key detection area, when the local contrast difference is higher than the average difference, determine that the external key detection area appears water seepage / leakage phenomenon according to the corresponding difference value;

[0129] Step 54: respectively acquire the detection result corresponding to each of the key detection area, construct the water leakage detection result of the full-face tunnel and transmit to the designated terminal for display.

[0130] In this example, the discrete range information represents the scanning detail information conforming to the discrete characteristics, and the discrete range out information represents the scanning detail information not conforming to the discrete characteristics;

[0131] In this example, the information cluster center represents the average value of a clustering information class;

[0132] In this example, the iterative morphological training means that the water sample is trained by multiple transformations until the morphology of the water sample is consistent with the information cluster center, and if the water sample is inconsistent with the information cluster center after training, it is determined that the discrete range information does not contain water seepage / leakage phenomenon;

[0133] In this example, the iterative sharpening processing represents the result of enhancing and sharpening the external key detection area;

[0134] In this example, if the local contrast is not higher than the average difference, it is determined that the external key detection area does not exist water seepage / leakage phenomenon.

[0135] The working principle and beneficial effects of the above technical solution are as follows: by dividing the scanning detail information into discrete range information and discrete range out information for water seepage / leakage detection, each scanning detail information corresponding area can be detected, and multiple water seepage / leakage areas can be excluded or determined at one time during the detection process, improving the detection efficiency, and finally generating the water seepage / leakage detection result of the full-face tunnel, providing technical reference and technical guidance for relevant personnel.

[0136] Embodiment 9

[0137] On the basis of embodiment 8, the three-dimensional laser array-based full-face tunnel lining seepage water detection method further comprises:

[0138] When there is no seepage / water leakage phenomenon in the full-face tunnel, a qualified report of the full-face tunnel is generated and displayed.

[0139] The working principle and beneficial effects of the above technical solution are as follows: when there is no seepage and water leakage phenomenon in the full-face tunnel, it indicates that it is in a qualified state, and a corresponding report is generated to prove its qualification.

[0140] Embodiment 10

[0141] On the basis of embodiment 8, the three-dimensional laser array-based full-face tunnel lining seepage water detection method further comprises:

[0142] According to the seepage water detection result, the overall collapse risk level of the full-face tunnel is analyzed, and a corresponding level warning is performed.

[0143] The working principle and beneficial effects of the above technical solution are as follows: when the overall collapse risk level of the full-face tunnel is too high, timely warning is performed to avoid tunnel collapse accidents.

[0144] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for detecting water leakage in a full-face tunnel lining based on a three-dimensional laser array, characterized in that, The application relates to a method for detecting water leakage in a full-section tunnel. Step 1: a full-section tunnel is scanned by using a plurality of preset laser arrangement modes to obtain scanning feedback information corresponding to each preset laser arrangement mode; Step 2: a tunnel appearance image of the full-section tunnel is constructed according to the scanning feedback information, pixel recognition is performed on the appearance image, and a plurality of tunnel non-smooth features of the full-section tunnel are obtained; Step 3: color analysis and texture analysis are respectively performed on each tunnel non-smooth feature to determine a plurality of key detection areas of the full-section tunnel; Step 4: scanning detail information of each key detection area is constructed according to scanning sub-information of the key detection area under each preset laser arrangement mode; Step 5: detail restoration is performed on the tunnel appearance image by using the scanning detail information, whether the key detection area leaks water is judged according to the restoration result, a water leakage detection result of the full-section tunnel is generated and displayed; The step 1 comprises the following steps. Step 11: the full-section tunnel is scanned for a plurality of times by using a plurality of preset laser arrangement modes to obtain field scanning information corresponding to each preset laser arrangement mode; Step 12: the field scanning information corresponding to each preset laser arrangement mode is projected in space according to a corresponding beam angle transformation process to obtain corresponding space distribution information; Step 13: beam-tunnel intersection features corresponding to each space distribution information are respectively recognized to generate scanning feedback information of the full-section tunnel corresponding to the preset laser arrangement mode; The step 2 comprises the following steps. Step 21: a corresponding tunnel imaging path is constructed according to a beam angle transformation process corresponding to each preset laser arrangement mode, intersection points between different tunnel imaging paths are located to determine a plurality of strong scanning positions of the full-section tunnel, and a plurality of scanning feedback sub-information corresponding to each strong scanning position are respectively determined; Step 22: an image recombination framework of the full-section tunnel is constructed according to the beam angle transformation process, each scanning feedback information is respectively input into the image recombination framework for fixation, unfixed sub-information contained in each scanning feedback information is respectively input into the image recombination framework for image recombination according to a fixation result, and a tunnel appearance image of the full-section tunnel is generated; Step 23: a source pointer is set for corresponding pixel points in the tunnel appearance image according to a sub-information source corresponding to each scanning feedback sub-information, the tunnel appearance image is divided into a plurality of image domains according to a pointer distribution condition of the tunnel appearance image, and corresponding recognition intensity is selected based on domain brightness corresponding to each image domain; Step 24: pixel recognition of each image domain is respectively performed at corresponding intensity to obtain a plurality of texture features of the full-section tunnel, and the texture features are three-dimensionally enhanced according to pixel gradient transformation information corresponding to each texture feature to generate a plurality of tunnel non-smooth features of the full-section tunnel.

2. The method of claim 1, wherein the method is a full-face tunnel lining water leakage detection method based on a three-dimensional laser array. The application further comprises the following steps. Color conversion is performed on the tunnel appearance image, and whether the full-section tunnel contains water mark features is judged according to a conversion result. If so, the water leakage position corresponding to the water mark feature in the tunnel appearance image is located and displayed.

3. The method of claim 1, wherein the method is a tunnel lining water leakage detection method based on a three-dimensional laser array, characterized in that, The step 3 comprises: Step 31: input each tunnel non-smooth feature into a gray level co-occurrence matrix for multi-dimensional analysis to obtain local texture information of the tunnel non-smooth feature, and use water samples to perform preliminary water seepage evaluation on each local texture information respectively; Step 32: according to the evaluation result, screen a plurality of target tunnel non-smooth features belonging to suspected water seepage conditions, obtain a feature sub-image corresponding to the target tunnel non-smooth feature in the tunnel appearance image, fuse the local texture information and the feature sub-image, and construct a layered suspected water seepage graph structure of the full-face tunnel; Step 33: perform water seepage test on the layered suspected water seepage graph structure to obtain a plurality of positions of the full-face tunnel that do not pass the test, and regard the range between the positions of the full-face tunnel that do not pass the test with a linear distance less than a specified distance as a key detection area of the full-face tunnel.

4. The method of claim 3, wherein the method is characterized by, Further comprising: Respectively obtain the area of each key detection area; According to the area, analyze the water seepage collapse risk level of the key detection area, and perform risk warning of the corresponding level for each key detection area.

5. The method of claim 1, wherein the method is a full face tunnel lining water leakage detection method based on a three-dimensional laser array. The step 4 comprises: Step 41: respectively obtain the on-site scanning information corresponding to each preset laser arrangement mode, and screen scanning sub-information corresponding to each key detection area in the on-site scanning information; Step 42: respectively perform local focusing on each scanning sub-information to obtain a plurality of enhanced focal points corresponding to the key detection area; Step 43: respectively map each enhanced focal point in the corresponding key detection area to enhance the pixels of the key detection area; Step 44: respectively obtain the pixel information corresponding to each key detection area to construct scanning detail information of the key detection area.

6. The method of claim 1, wherein the method is a full face tunnel lining water leakage detection method based on a three-dimensional laser array. The step 5 comprises: Step 51: respectively identify the information value corresponding to each scanning detail information, divide the scanning detail information into in-discrete range information and out-discrete range information according to the discrete characteristics of all the information values, cluster the in-discrete range information to obtain a plurality of information cluster centers; Step 52: perform iterative morphological training on the water sample, when the training result is consistent with the information cluster center, determine the water amount of the key detection area corresponding to the information cluster center according to the training result value, and determine that the inner key detection area corresponding to the information cluster center has a water seepage / leakage phenomenon; Step 53: when the information cluster center is inconsistent with the training result, respectively perform iterative sharpening processing on the outer key detection area corresponding to each out-discrete range information, and sequentially enhance the local contrast of each pixel point in the outer key detection area, when the local contrast difference is higher than the average difference, determine that the outer key detection area has a water seepage / leakage phenomenon according to the corresponding difference value; Step 54: respectively obtain the detection result corresponding to each key detection area to construct a water seepage detection result of the full-face tunnel and transmit the water seepage detection result to a specified terminal for display.

7. A full face tunnel lining water leakage detection method based on a three-dimensional laser array as claimed in claim 6, characterized in that, Further comprising: When the seepage / leakage phenomenon does not exist in the full-face tunnel, a qualified report of the full-face tunnel is generated and displayed.

8. The method of claim 6, wherein the method is a tunnel lining water leakage detection method based on a three-dimensional laser array, characterized in that, Further comprising: According to the seepage detection result, the overall collapse risk level of the full-face tunnel is analyzed, and a corresponding level warning is given.

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