Full-section tunnel lining water leakage detection method based on three-dimensional laser array

Abstract

The invention provides a full-section tunnel lining water leakage detection method based on a three-dimensional laser array, and the method comprises the steps: carrying out the light beam scanning of a full-section tunnel through a plurality of preset laser arrangement modes, obtaining the scanning feedback information corresponding to each preset laser arrangement mode, and constructing a tunnel appearance image of the full-section tunnel, a plurality of tunnel non-smooth features of a full-section tunnel are obtained through pixel recognition, color analysis and texture analysis are performed on each tunnel non-smooth feature, and a plurality of key detection areas are determined; generating a plurality of pieces of scanning detail information of the key detection area according to the scanning sub-information of the key detection area in each preset laser arrangement mode, performing detail reduction on the tunnel appearance image, judging whether the key detection area leaks water or not, generating a water leakage detection result of the full-section tunnel, and displaying the water leakage detection result. The tunnel condition of the full-section tunnel is collected through the laser scanning technology, the water leakage position of the tunnel is positioned step by step, and workers can maintain the tunnel conveniently 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 on a complete working face at one time. 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 during long-term operation. Even if relevant plugging and drainage measures are taken, leakage may occur at other positions, which is difficult to cure, especially the leakage at the top of the arch, which is prone to directly dripping on the road surface. When the amount of leakage water is large, the tire of a vehicle may slip, which endangers the safety of driving. Statistics of highway departments show that about 30% of highway tunnels in China have serious leakage disease. Statistics of railway departments show that 28.4% of railway tunnels in China have serious leakage disease. With the rapid development of tunnel projects in China, tunnel disease problems are increasingly prominent. Leakage is one of the main diseases of tunnels and is also the root cause of other diseases of tunnels. 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 positions step by step to timely find the leakage positions, so that workers can timely repair.

[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: Step 1: a plurality of preset laser arrangement modes are used to scan the full-face tunnel by light beams to obtain scanning feedback information corresponding to each of the preset laser arrangement modes; Step 2: a tunnel appearance image of the full-face tunnel is constructed according to the scanning feedback information, and pixel recognition is performed on the appearance image to obtain a plurality of tunnel non-smooth features of the full-face tunnel; Step 3: color analysis and texture analysis are respectively performed on each of the tunnel non-smooth features to determine a plurality of key detection areas of the full-face tunnel; Step 4: constructing a plurality of scanning detail information of each key detection area according to the scanning sub-information of each of the preset laser arrangement modes; Step 5: Use the scanned detail information to restore the details of the tunnel appearance image, determine whether the key detection area is leaking water based on the restoration result, generate the water leakage detection result of the full-section tunnel and display it.

[0006] In one practicable manner, The step 1 comprises: Step 11: performing laser scanning on the full-section tunnel several times using several preset laser arrangement modes to obtain on-site scanning information corresponding to each of the preset laser arrangement modes; Step 12: spatially projecting the corresponding on-site scanning information according to the beam angle transformation process corresponding to each of the preset laser arrangement modes to obtain corresponding spatial distribution information; Step 13: Identify the beam-tunnel intersection features corresponding to each piece of the spatial distribution information respectively, and generate scanning feedback information of the full-section tunnel corresponding to the preset laser arrangement.

[0007] In one practicable manner, The step 2 comprises: Step 21: constructing a corresponding tunnel imaging path according to the beam angle transformation process corresponding to each of the preset laser arrangement modes, locating the intersections between different tunnel imaging paths to determine a plurality of strong scanning positions of the full-section tunnel, and respectively determining a plurality of scanning feedback sub-information corresponding to each of the strong scanning positions; Step 22: constructing an image reconstruction framework for the full-section tunnel based on the beam transformation process, inputting each piece of the scanning feedback information into the image reconstruction framework for fixation, and inputting the unfixed sub-information contained in each piece of the scanning feedback information into the image reconstruction framework for image reconstruction based on the fixation result, thereby generating a tunnel appearance image of the full-section tunnel; Step 23: Setting a source pointer for a corresponding pixel point in the tunnel appearance image according to the source of the sub-information corresponding to each of the scan feedback sub-information, dividing the tunnel appearance image into a plurality of image domains according to the distribution of the pointers, and selecting a corresponding recognition strength based on the domain brightness corresponding to each of the image domains; Step 24: Pixel identification of corresponding intensity is performed on each of the image domains to obtain a number of texture features of the full-section tunnel. The texture features are three-dimensionally enhanced according to the pixel gradient transformation information corresponding to each of the texture features to generate a number of tunnel non-smooth features of the full-section tunnel.

[0008] In one practicable manner, Also includes: Performing color conversion on the tunnel appearance image, and determining whether the full-section tunnel contains water mark features based on the conversion result; If so, the water leakage position corresponding to the water mark feature is located in the tunnel appearance image and displayed.

[0009] In one practicable manner, The step 3 comprises: Step 31: Input each of the tunnel non-smooth features into a gray-level co-occurrence matrix for multi-dimensional analysis to obtain local texture information of the tunnel non-smooth features, and perform preliminary water seepage evaluation on each of the local texture information using water samples; Step 32: Filtering several target tunnel non-smooth features that are suspected of water seepage based on the evaluation results, obtaining feature sub-images corresponding to the target tunnel non-smooth features in the tunnel appearance image, fusing the local texture information with the feature sub-images, and constructing a layered suspected water seepage map structure of the full-section tunnel; Step 33: Perform a water seepage test on the layered suspected water seepage map structure to obtain a number of failed test positions in the full-section tunnel, and regard the range between the failed test positions with a straight-line distance less than a specified distance as the key detection area of ​​the full-section tunnel.

[0010] In one practicable manner, Also includes: respectively obtaining the area corresponding to each of the key detection areas; The water seepage collapse risk level corresponding to the key detection area is analyzed according to the area of ​​the area, and a risk warning of a corresponding level is issued for each key detection area.

[0011] In one practicable manner, The step 4 comprises: Step 41: respectively obtaining on-site scanning information corresponding to each of the preset laser arrangement modes, and filtering scanning sub-information corresponding to each of the key detection areas in the on-site scanning information; Step 42: performing local focusing on each of the scanned sub-information to obtain a plurality of enhanced focuses corresponding to the key detection areas; Step 43: Mapping each of the enhanced focal points to the corresponding key detection area and performing pixel enhancement on the key detection area; Step 44: Obtain pixel information corresponding to each of the key detection areas respectively, and construct scanning detail information of the key detection areas.

[0012] In one practicable manner, The step 5 comprises: Step 51: Identify the information value corresponding to each piece of scanning detail information, divide the scanning detail information into information within a discrete range and information outside the discrete range based on the discrete characteristics of all the information values, cluster the information within the discrete range, and obtain a number of information cluster centers; Step 52: Perform iterative morphological training on the water sample. When the training result is consistent with the center of the information cluster, determine the water volume in the key detection area corresponding to the center of the information cluster based on the training result value, and determine whether water seepage / leakage occurs in the corresponding key detection area. Step 53: When the center of the information cluster is inconsistent with the training result, iterative sharpening processing is performed on each outer key detection area corresponding to the information outside the discrete range, and the local contrast corresponding to each pixel in the outer key detection area is sequentially enhanced. When the local contrast difference is higher than the average difference, it is determined that water seepage / leakage occurs in the outer key detection area according to the corresponding difference value; Step 54: respectively obtain the detection results corresponding to each of the key detection areas, construct the water leakage detection results of the full-section tunnel and transmit them to a designated terminal for display.

[0013] In one practicable manner, Also includes: When there is no water seepage / leakage in the full-section tunnel, a qualified report of the full-section tunnel is generated and displayed.

[0014] In one practicable manner, Also includes: The overall collapse risk level of the full-section tunnel is analyzed based on the water leakage detection results, and an early warning of the corresponding level is issued.

[0015] The achievable beneficial effects of the above technical solution are: in order to quickly locate the seepage and leakage positions of the tunnel, it is necessary to first collect on-site information of the full-section tunnel, so the full-section tunnel is scanned using a variety of laser arrangement methods, 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 to obtain the scanning detail information contained therein, and finally the scanning detail information is used to restore the details in the tunnel appearance image, so as to determine whether the key detection area is seeping or leaking, and generate a corresponding report. In this way, not only can the full-section tunnel be analyzed in an all-round way, but the seepage and leakage conditions can also be detected separately at the same time, so that seepage and leakage occur in a timely manner and the maintenance of the full-section tunnel is improved.

[0016] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0017] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 Schematic diagram of the workflow of a method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array in an embodiment of the present invention; Figure 2 This is a schematic diagram of the workflow of step 1 of a method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0020] Example 1 This embodiment provides a method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array. Figure 1 Shown, including: Step 1: Scan the entire tunnel section using a plurality of preset laser arrangements to obtain scanning feedback information corresponding to each of the preset laser arrangements; Step 2: constructing a tunnel appearance image of the full-section tunnel based on the scanning feedback information, performing pixel recognition on the appearance image, and obtaining a plurality of tunnel non-smooth features of the full-section tunnel; Step 3: performing color analysis and texture analysis on each non-smooth feature of the tunnel to determine several key inspection areas of the full-section tunnel; Step 4: constructing a plurality of scanning detail information of each key detection area according to the scanning sub-information of each of the preset laser arrangement modes; Step 5: Use the scanned detail information to restore the details of the tunnel appearance image, determine whether the key detection area is leaking water based on the restoration result, generate the water leakage detection result of the full-section tunnel and display it.

[0021] In this example, the preset laser arrangements include: square, hexagonal, and circular; In this example, the scanning feedback information represents the information generated by the laser being refracted after it hits the tunnel wall of the full-section tunnel; In this example, the tunnel non-smooth characteristics represent the characteristics presented by the unbalanced areas in the full-face tunnel; In this example, color analysis refers to the process of identifying whether the non-smooth features of the tunnel contain information consistent with the color of water, and texture analysis refers to the process of identifying whether the non-smooth features of the tunnel contain information consistent with the texture of water. In this example, the scanning detail information represents the result of restoring the information of the key detection area using the scanning sub-information.

[0022] The working principle and beneficial effects of the above technical solution: In order to quickly locate the seepage and leakage position of the tunnel, it is necessary to first collect on-site information of the full-section tunnel, so the full-section tunnel is scanned using multiple laser arrangement methods to obtain corresponding scanning feedback information, and then the obtained scanning feedback information is 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 to obtain the scanning detail information contained therein, and finally the scanning detail information is used to restore the details in the tunnel appearance image, so as to determine whether the key detection area is seeping or leaking, and generate a corresponding report. In this way, not only can the full-section tunnel be analyzed in an all-round way, but the seepage and leakage conditions can also be detected separately at the same time, so that seepage and leakage occur in time and the maintenance of the full-section tunnel is improved.

[0023] Example 2 Based on Example 1, the method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array, step 1, comprises: Step 11: performing laser scanning on the full-section tunnel several times using several preset laser arrangement modes to obtain on-site scanning information corresponding to each of the preset laser arrangement modes; Step 12: spatially projecting the corresponding on-site scanning information according to the beam angle transformation process corresponding to each of the preset laser arrangement modes to obtain corresponding spatial distribution information; Step 13: Identify the beam-tunnel intersection features corresponding to each piece of the spatial distribution information respectively, and generate scanning feedback information of the full-section tunnel corresponding to the preset laser arrangement.

[0024] In this example, one preset laser arrangement corresponds to one beam scan; In this example, the spatial distribution information represents the result of displaying the on-site scanning information in a three-dimensional space; In this example, the beam-tunnel intersection feature refers to a feature generated when light from a beam scan intersects with a tunnel wall.

[0025] The working principle and beneficial effects of the above technical solution are as follows: when scanning a full-section tunnel, a variety of preset laser arrangements are used to obtain a number of on-site scanning information, and then its spatial distribution information is constructed through spatial projection. Finally, the scanning feedback information of the full-section tunnel is constructed based on the beam-tunnel intersection characteristics in the spatial distribution information. In this way, the structure of the full-section tunnel can be scanned in all directions, thereby improving the accuracy of subsequent appearance composition.

[0026] Example 3 Based on Example 1, the method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array, step 2, includes: Step 21: constructing a corresponding tunnel imaging path according to the beam angle transformation process corresponding to each of the preset laser arrangement modes, locating the intersections between different tunnel imaging paths to determine a plurality of strong scanning positions of the full-section tunnel, and respectively determining a plurality of scanning feedback sub-information corresponding to each of the strong scanning positions; Step 22: constructing an image reconstruction framework for the full-section tunnel based on the beam transformation process, inputting each piece of the scanning feedback information into the image reconstruction framework for fixation, and inputting the unfixed sub-information contained in each piece of the scanning feedback information into the image reconstruction framework for image reconstruction based on the fixation result, thereby generating a tunnel appearance image of the full-section tunnel; Step 23: Setting a source pointer for a corresponding pixel point in the tunnel appearance image according to the source of the sub-information corresponding to each of the scan feedback sub-information, dividing the tunnel appearance image into a plurality of image domains according to the distribution of the pointers, and selecting a corresponding recognition strength based on the domain brightness corresponding to each of the image domains; Step 24: Pixel identification of corresponding intensity is performed on each of the image domains to obtain a number of texture features of the full-section tunnel. The texture features are three-dimensionally enhanced according to the pixel gradient transformation information corresponding to each of the texture features to generate a number of tunnel non-smooth features of the full-section tunnel.

[0027] In this example, the tunnel imaging path represents a path for spatially arranging the scan feedback information; In this example, a strong scanning position means a position that is scanned twice or more; In this example, the image reconfiguration framework refers to a framework for setting an image format in advance; In this example, the source pointer represents a pointer to the memory location of a pixel; In this example, the image domain represents the result of dividing the tunnel appearance image; In this example, the recognition strength is proportional to the domain brightness. The higher the domain brightness, the higher the recognition strength. In this example, the pixel gradient transformation information represents information presented by the pixel value transformation in the texture feature.

[0028] The working principle and beneficial effects of the above technical solution are as follows: the strong scanning position and its corresponding scanning feedback sub-information during the scanning process are determined by the beam transformation process of the preset laser arrangement, and the image reconstruction framework of the full-section tunnel is constructed according to the beam transformation process, and the scanning feedback information is input into it to generate a tunnel appearance image of the full-section tunnel. Then, the tunnel appearance image is divided into several image domains according to the distribution of the existing source pointers in the tunnel appearance image, and the corresponding recognition intensity is set for each image domain according to the domain brightness, and pixel recognition is performed on the image domain, which effectively avoids the phenomenon of distortion of the tunnel appearance image caused by unified recognition, and obtains clear texture features of the full-section tunnel. Finally, the texture features are three-dimensionally enhanced according to the pixel gradient transformation information of the texture features, and the tunnel non-smooth features of the full-section tunnel are obtained. In this way, all textures of the full-section tunnel can be processed, thereby improving the efficiency and quality of subsequent key detection.

[0029] Example 4 Based on Example 3, the method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array further includes: Performing color conversion on the tunnel appearance image, and determining whether the full-section tunnel contains water mark features based on the conversion result; If so, the water leakage position corresponding to the water mark feature is located in the tunnel appearance image and displayed.

[0030] The working principle and beneficial effects of the above technical solution are as follows: when the tunnel appearance image contains water mark features, it indicates that a serious water leakage has occurred, so the water leakage location is marked and located and displayed first.

[0031] Example 5 On the basis of Example 1, the full-section tunnel lining water leakage detection method based on a three-dimensional laser array is as follows: Figure 2 As shown, the step 3 includes: Step 31: Input each of the tunnel non-smooth features into a gray-level co-occurrence matrix for multi-dimensional analysis to obtain local texture information of the tunnel non-smooth features, and perform preliminary water seepage evaluation on each of the local texture information using water samples; Step 32: Filtering several target tunnel non-smooth features that are suspected of water seepage based on the evaluation results, obtaining feature sub-images corresponding to the target tunnel non-smooth features in the tunnel appearance image, fusing the local texture information with the feature sub-images, and constructing a layered suspected water seepage map structure of the full-section tunnel; Step 33: Perform a water seepage test on the layered suspected water seepage map structure to obtain a number of failed test positions in the full-section tunnel, and regard the range between the failed test positions with a straight-line distance less than a specified distance as the key detection area of ​​the full-section tunnel.

[0032] In this example, the multidimensional analysis includes: local color dimension, local texture dimension, and local contrast dimension; In this example, the local texture feature represents the information of each dimension presented by each local texture in the tunnel non-smooth feature; In this example, the layered suspected water seepage diagram structure represents the exploded structural diagram of a suspected water seepage location in a full-section tunnel; In this example, the prescribed distance is 50 cm.

[0033] The working principle and beneficial effects of the above technical solution are as follows: by using the gray-level co-occurrence matrix to perform multi-dimensional analysis on the non-smooth features of the tunnel to obtain the local texture information of the non-smooth features of the tunnel, and then perform a preliminary water seepage evaluation, re-map the non-smooth features of the target tunnel suspected of water seepage and conduct a water seepage test, and fuse the test positions that fail the water seepage test to determine the key detection area of ​​the full-section tunnel. In this way, all water seepage positions in the tunnel can be quickly located, and the water seepage positions within a short distance can be fused to determine the key detection area, which effectively improves the quality and efficiency of subsequent water seepage and leakage detection. Non-water seepage and non-leakage positions are temporarily not detected, which reduces the consumption of manpower and material resources, effectively improves the maintenance efficiency of the full-section tunnel, and avoids the inconvenience caused by long-term maintenance to the highway.

[0034] Example 6 Based on Example 5, the method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array further includes: respectively obtaining the area corresponding to each of the key detection areas; The water seepage collapse risk level corresponding to the key detection area is analyzed according to the area of ​​the area, and a risk warning of a corresponding level is issued for each key detection area.

[0035] The working principle and beneficial effects of the above technical solution are as follows: the collapse level of the area after collapse is determined based on the area of ​​the key detection area, and corresponding early warning is carried out, thereby achieving the purpose of early warning and effective early warning, and avoiding collapse caused by water seepage that affects the normal use of the tunnel.

[0036] Example 7 Based on Example 1, the method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array, step 4, includes: Step 41: respectively obtaining on-site scanning information corresponding to each of the preset laser arrangement modes, and filtering scanning sub-information corresponding to each of the key detection areas in the on-site scanning information; Step 42: performing local focusing on each of the scanned sub-information to obtain a plurality of enhanced focuses corresponding to the key detection areas; Step 43: Mapping each of the enhanced focal points to the corresponding key detection area and performing pixel enhancement on the key detection area; Step 44: Obtain pixel information corresponding to each of the key detection areas respectively, and construct scanning detail information of the key detection areas.

[0037] In this example, the enhanced focus indicates the location in the key detection area where the probability of water seepage is the highest.

[0038] The working principle and beneficial effects of the above technical solution are as follows: by screening the scanning sub-information in the on-site scanning information to locally focus on the key detection area, the enhanced focus of the key detection area is determined, and then it is used to synchronously enhance the pixels of the key detection area, and the scanning detail information of the key detection area is constructed. Through this technology, the location where water seepage will occur can be located again, and the scanning detail information of the key detection area is generated to facilitate the next step of detection and identification.

[0039] Example 8 Based on Example 1, the method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array, step 5, includes: Step 51: Identify the information value corresponding to each piece of scanning detail information, divide the scanning detail information into information within a discrete range and information outside the discrete range based on the discrete characteristics of all the information values, cluster the information within the discrete range, and obtain a number of information cluster centers; Step 52: Perform iterative morphological training on the water sample. When the training result is consistent with the center of the information cluster, determine the water volume in the key detection area corresponding to the center of the information cluster based on the training result value, and determine whether water seepage / leakage occurs in the corresponding key detection area. Step 53: When the center of the information cluster is inconsistent with the training result, iterative sharpening processing is performed on each outer key detection area corresponding to the information outside the discrete range, and the local contrast corresponding to each pixel in the outer key detection area is sequentially enhanced. When the local contrast difference is higher than the average difference, it is determined that water seepage / leakage occurs in the outer key detection area according to the corresponding difference value; Step 54: respectively obtain the detection results corresponding to each of the key detection areas, construct the water leakage detection results of the full-section tunnel and transmit them to a designated terminal for display.

[0040] In this example, information within the discrete range represents scan detail information that conforms to the discrete features, and information outside the discrete range represents scan detail information that does not conform to the discrete features; In this instance, the information cluster center represents the average value of a cluster information class; In this example, iterative morphological training means subjecting the water sample to multiple transformation training until the morphology of the water sample is consistent with the center of the information cluster. If the water sample is inconsistent with the center of the information cluster after training, it is determined that the information within the discrete range does not contain water seepage or leakage. In this example, the iterative sharpening process represents the result of enhancing the sharpness of the outer key detection area; In this example, if the local contrast is not higher than the average drop, it is determined that there is no water seepage or leakage in the outer key detection area.

[0041] The working principle and beneficial effects of the above technical solution are as follows: by dividing the scanned detail information into information within the non-discrete range and information outside the discrete range to perform seepage and leakage detection separately, it is possible to detect the area corresponding to each scanned detail information in a targeted manner, and to exclude or identify multiple seepage and leakage areas at one time during the detection process, thereby improving the efficiency of detection. Finally, the seepage and leakage detection results of the full-section tunnel are generated, providing technical reference and technical guidance to relevant personnel.

[0042] Example 9 Based on Example 8, the method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array further includes: When there is no water seepage / leakage in the full-section tunnel, a qualified report of the full-section tunnel is generated and displayed.

[0043] The working principle and beneficial effects of the above technical solution are as follows: when there is no water seepage or leakage in the full-section tunnel, it indicates that it is in a qualified state, and a corresponding report is generated to prove its qualification.

[0044] Example 10 Based on Example 8, the method for detecting water leakage in a full-section tunnel lining based on a three-dimensional laser array further includes: The overall collapse risk level of the full-section tunnel is analyzed based on the water leakage detection results, and an early warning of the corresponding level is issued.

[0045] The working principle and beneficial effects of the above technical solution are as follows: When the overall collapse risk level of a full-section tunnel is too high, timely warnings are issued to avoid accidents such as tunnel collapse.

[0046] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A full-section tunnel lining water leakage detection method based on a three-dimensional laser array, characterized in that: include: Step 1: Scan the entire tunnel section using a plurality of preset laser arrangements to obtain scanning feedback information corresponding to each of the preset laser arrangements; Step 2: constructing a tunnel appearance image of the full-section tunnel based on the scanning feedback information, performing pixel recognition on the appearance image, and obtaining a plurality of tunnel non-smooth features of the full-section tunnel; Step 3: performing color analysis and texture analysis on each non-smooth feature of the tunnel to determine several key inspection areas of the full-section tunnel; Step 4: constructing a plurality of scanning detail information of each key detection area according to the scanning sub-information of each of the preset laser arrangement modes; Step 5: Use the scanned detail information to restore the details of the tunnel appearance image, determine whether the key detection area is leaking water based on the restoration result, generate the water leakage detection result of the full-section tunnel and display it.

2. The method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array according to claim 1, characterized in that: The step 1 comprises: Step 11: performing laser scanning on the full-section tunnel several times using several preset laser arrangement modes to obtain on-site scanning information corresponding to each of the preset laser arrangement modes; Step 12: spatially projecting the corresponding on-site scanning information according to the beam angle transformation process corresponding to each of the preset laser arrangement modes to obtain corresponding spatial distribution information; Step 13: Identify the beam-tunnel intersection features corresponding to each piece of the spatial distribution information respectively, and generate scanning feedback information of the full-section tunnel corresponding to the preset laser arrangement.

3. The method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array according to claim 1, characterized in that: The step 2 comprises: Step 21: constructing a corresponding tunnel imaging path according to the beam angle transformation process corresponding to each of the preset laser arrangement modes, locating the intersections between different tunnel imaging paths to determine a plurality of strong scanning positions of the full-section tunnel, and respectively determining a plurality of scanning feedback sub-information corresponding to each of the strong scanning positions; Step 22: constructing an image reconstruction framework for the full-section tunnel based on the beam transformation process, inputting each piece of the scanning feedback information into the image reconstruction framework for fixation, and inputting the unfixed sub-information contained in each piece of the scanning feedback information into the image reconstruction framework for image reconstruction based on the fixation result, thereby generating a tunnel appearance image of the full-section tunnel; Step 23: Setting a source pointer for a corresponding pixel point in the tunnel appearance image according to the source of the sub-information corresponding to each of the scan feedback sub-information, dividing the tunnel appearance image into a plurality of image domains according to the distribution of the pointers, and selecting a corresponding recognition strength based on the domain brightness corresponding to each of the image domains; Step 24: Pixel identification of corresponding intensity is performed on each of the image domains to obtain a number of texture features of the full-section tunnel. The texture features are three-dimensionally enhanced according to the pixel gradient transformation information corresponding to each of the texture features to generate a number of tunnel non-smooth features of the full-section tunnel.

4. The method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array according to claim 3, characterized in that: Also includes: Performing color conversion on the tunnel appearance image, and determining whether the full-section tunnel contains water mark features based on the conversion result; If so, the water leakage position corresponding to the water mark feature is located in the tunnel appearance image and displayed.

5. The method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array according to claim 1, characterized in that: The step 3 comprises: Step 31: Input each of the tunnel non-smooth features into a gray-level co-occurrence matrix for multi-dimensional analysis to obtain local texture information of the tunnel non-smooth features, and perform preliminary water seepage evaluation on each of the local texture information using water samples; Step 32: Filtering several target tunnel non-smooth features that are suspected of water seepage based on the evaluation results, obtaining feature sub-images corresponding to the target tunnel non-smooth features in the tunnel appearance image, fusing the local texture information with the feature sub-images, and constructing a layered suspected water seepage map structure of the full-section tunnel; Step 33: Perform a water seepage test on the layered suspected water seepage map structure to obtain a number of failed test positions in the full-section tunnel, and regard the range between the failed test positions with a straight-line distance less than a specified distance as the key detection area of ​​the full-section tunnel.

6. The method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array according to claim 5, characterized in that: Also includes: respectively obtaining the area corresponding to each of the key detection areas; The water seepage collapse risk level corresponding to the key detection area is analyzed according to the area of ​​the area, and a risk warning of a corresponding level is issued for each key detection area.

7. The method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array according to claim 1, characterized in that: The step 4 comprises: Step 41: respectively obtaining on-site scanning information corresponding to each of the preset laser arrangement modes, and filtering scanning sub-information corresponding to each of the key detection areas in the on-site scanning information; Step 42: performing local focusing on each of the scanned sub-information to obtain a plurality of enhanced focuses corresponding to the key detection areas; Step 43: Mapping each of the enhanced focal points to the corresponding key detection area and performing pixel enhancement on the key detection area; Step 44: Obtain pixel information corresponding to each of the key detection areas respectively, and construct scanning detail information of the key detection areas.

8. The method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array according to claim 1, characterized in that: The step 5 comprises: Step 51: Identify the information value corresponding to each piece of scanning detail information, divide the scanning detail information into information within a discrete range and information outside the discrete range based on the discrete characteristics of all the information values, cluster the information within the discrete range, and obtain a number of information cluster centers; Step 52: Perform iterative morphological training on the water sample. When the training result is consistent with the center of the information cluster, determine the water volume in the key detection area corresponding to the center of the information cluster based on the training result value, and determine whether water seepage / leakage occurs in the corresponding key detection area. Step 53: When the center of the information cluster is inconsistent with the training result, iterative sharpening processing is performed on each outer key detection area corresponding to the information outside the discrete range, and the local contrast corresponding to each pixel in the outer key detection area is sequentially enhanced. When the local contrast difference is higher than the average difference, it is determined that water seepage / leakage occurs in the outer key detection area according to the corresponding difference value; Step 54: respectively obtain the detection results corresponding to each of the key detection areas, construct the water leakage detection results of the full-section tunnel and transmit them to a designated terminal for display.

9. The method for detecting water leakage in full-section tunnel linings based on a three-dimensional laser array according to claim 8, characterized in that: Also includes: When there is no water seepage / leakage in the full-section tunnel, a qualified report of the full-section tunnel is generated and displayed.

10. The method for detecting water leakage in full-section tunnel lining based on a three-dimensional laser array according to claim 8, characterized in that: Also includes: The overall collapse risk level of the full-section tunnel is analyzed based on the water leakage detection results, and an early warning of the corresponding level is issued.

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