Underground pipe gallery micro crack detection and measurement method, device, equipment, medium and product

Abstract

The application discloses a kind of underground pipe gallery micro crack detection and measurement method, device, equipment, medium and product, it is related to underground pipe gallery corridor body crack detection technical field, the method comprises: the RGB image of pipe gallery is input target detection model and obtains multiple regions containing crack;Edge detection and contour extraction operation are carried out to each crack-containing region to obtain the contour map of each crack;Based on depth map and RGB image, the three-dimensional point cloud data of pipe gallery is obtained, and the three-dimensional point cloud data of pipe gallery is sequentially subjected to point cloud density analysis and local curvature calculation;Based on the three-dimensional point cloud data of pipe gallery, the contour map of each crack, point cloud density analysis result and the curvature of three-dimensional point cloud data, the three-dimensional point cloud model of crack is constructed;The size of each crack is obtained by processing the three-dimensional point cloud model of crack through principal component analysis algorithm, and the accuracy of micro crack detection and size measurement result can be improved.

Description

Technical Field

[0001] This application relates to the field of underground utility tunnel crack detection technology, and in particular to a method, device, equipment, medium and product for detecting and measuring microcracks in underground utility tunnels. Background Technology

[0002] Traditional methods for detecting and measuring cracks in utility tunnels mainly include manual inspection and crack meter measurement. These methods have the advantages of being easy to operate and low in cost, but they also have disadvantages such as low monitoring efficiency, low data accuracy, and inability to monitor in real time. Traditional methods are also affected by environmental, climatic, and human factors, making it difficult to guarantee the accuracy and reliability of monitoring data.

[0003] With the advancement of technology, modern monitoring technologies have been widely used in the monitoring of cracks in utility tunnels. These technologies mainly include digital imaging technology, fiber optic sensing technology, and infrared thermal imaging technology. However, these technologies do not yield accurate results when detecting and measuring the size of microcracks. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment, medium, and product for detecting and measuring microcracks in underground utility tunnels, which can improve the accuracy of microcrack detection and dimensional measurement results.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] Firstly, this application provides a method for detecting and measuring microcracks in underground utility tunnels, including:

[0007] Acquire RGB and depth images of the utility tunnel;

[0008] The RGB image is input into the target detection model to obtain multiple regions containing cracks; the target detection model is obtained by first training the YOLOv8 model with a general crack dataset, and then training the trained YOLOv8 model with an underground utility tunnel crack dataset;

[0009] Edge detection and contour extraction are performed on each region containing cracks to obtain the contour map of each crack;

[0010] The 3D point cloud data of the pipe gallery is obtained based on the depth map and RGB image. Point cloud density analysis and local curvature calculation are performed on the 3D point cloud data of the pipe gallery in sequence to obtain the point cloud density analysis results and the curvature of the 3D point cloud data.

[0011] The 3D point cloud data of the cracks is obtained based on the 3D point cloud data of the pipe gallery and the contour map of each crack.

[0012] A three-dimensional point cloud model of the crack is constructed based on the crack's three-dimensional point cloud data, point cloud density analysis results, and the curvature of the three-dimensional point cloud data.

[0013] The dimensions of each crack are obtained by processing the three-dimensional point cloud model of the crack using principal component analysis algorithm.

[0014] In one embodiment, edge detection and contour extraction operations are performed on each region containing cracks to obtain a contour map of each crack, specifically including:

[0015] For any region containing a crack, edge detection is performed on the region to obtain a first image;

[0016] The first image is processed using an adaptive thresholding method and the Canny edge detection algorithm to obtain the second image;

[0017] A contour extraction operation is performed on the second image to obtain the contour map of the cracks in the region.

[0018] In one embodiment, the first image is processed using an adaptive thresholding method and the Canny edge detection algorithm to obtain a second image, specifically including:

[0019] The threshold is obtained by processing the first image using an adaptive thresholding method;

[0020] The first image is processed using the Canny edge detection algorithm based on the threshold to obtain the second image.

[0021] In one embodiment, the RGB image is input into a target detection model to obtain multiple regions containing cracks, specifically including:

[0022] The RGB image is sequentially subjected to contrast adjustment, noise reduction, and image normalization to obtain the preprocessed image.

[0023] The preprocessed image is input into the target detection model to obtain multiple regions containing cracks.

[0024] In one embodiment, the dimensions of the cracks include: the maximum length, average width, and maximum depth of the cracks. The dimensions of each crack are obtained by processing the three-dimensional point cloud model of the cracks using a principal component analysis algorithm, specifically including:

[0025] The three-dimensional point cloud model of the crack was processed by principal component analysis algorithm to obtain the principal direction axis, secondary direction axis and normal direction axis;

[0026] Project the 3D point cloud model of the crack onto the main direction axis and calculate the maximum length of each crack;

[0027] Project the 3D point cloud model of the crack onto the secondary direction axis and calculate the average width of each crack;

[0028] The 3D point cloud model of the cracks is projected onto the normal axis, and the maximum depth of each crack is calculated.

[0029] In one embodiment, the three-dimensional point cloud data of the pipe gallery is obtained based on the depth map and RGB image, specifically including:

[0030] The initial 3D point cloud data of the pipe gallery was obtained based on the depth map and RGB image;

[0031] The initial three-dimensional point cloud data of the utility tunnel is optimized to obtain the three-dimensional point cloud data of the utility tunnel.

[0032] Secondly, this application provides a device for detecting and measuring microcracks in underground utility tunnels, comprising:

[0033] The input module is used to acquire RGB and depth images of the utility tunnel.

[0034] The crack detection module is used to input the RGB image into the target detection model to obtain multiple regions containing cracks, and to perform edge detection and contour extraction operations on each region containing cracks to obtain the contour map of each crack; the target detection model is obtained by first training the YOLOv8 model with a general crack dataset, and then training the trained YOLOv8 model with an underground utility tunnel crack dataset.

[0035] The 3D measurement module is used to obtain 3D point cloud data of the pipe gallery based on depth maps and RGB images. It then performs point cloud density analysis and local curvature calculation on the 3D point cloud data of the pipe gallery to obtain the point cloud density analysis results and the curvature of the 3D point cloud data. Based on the 3D point cloud data of the pipe gallery and the contour maps of each crack, it obtains the 3D point cloud data of the cracks. Based on the 3D point cloud data of the cracks, the point cloud density analysis results, and the curvature of the 3D point cloud data, it constructs a 3D point cloud model of the cracks. Finally, it processes the 3D point cloud model of the cracks using a principal component analysis algorithm to obtain the dimensions of each crack.

[0036] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for detecting and measuring microcracks in underground utility tunnels as described in any of the above claims.

[0037] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for detecting and measuring microcracks in underground utility tunnels as described in any of the preceding claims.

[0038] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method for detecting and measuring microcracks in underground utility tunnels as described in any of the preceding claims.

[0039] According to the specific embodiments provided in this application, this application has the following technical effects:

[0040] This application provides a method, device, equipment, medium, and product for detecting and measuring microcracks in underground utility tunnels. First, a YOLOv8 model is trained using a general crack dataset. Then, the trained YOLOv8 model is trained using an underground utility tunnel crack dataset to obtain a target detection model. This can improve the target detection model's accuracy in detecting cracks. Point cloud density analysis and local curvature calculation are used to enhance the ability to express microcrack features, thereby improving the accuracy of microcrack detection and size measurement results. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating a method for detecting and measuring microcracks in an underground utility tunnel, provided as an embodiment of this application;

[0043] Figure 2 A structural block diagram of a microcrack detection and measurement device for underground utility tunnels provided in an embodiment of this application;

[0044] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] In one exemplary embodiment, a method for detecting and measuring microcracks in underground utility tunnels is provided, such as... Figure 1 As shown, the process includes steps 201 to 207. Wherein:

[0048] Step 201: Acquire RGB and depth images of the utility tunnel. Specifically, RGB and depth images can be acquired using a RealSense camera.

[0049] Step 202: Input the RGB image into the target detection model to obtain multiple regions containing cracks; the target detection model is obtained by first training a YOLOv8 model using a general crack dataset, and then training the trained YOLOv8 model using an underground utility tunnel crack dataset. The general crack dataset includes various crack images. The underground utility tunnel crack dataset contains multi-angle, multi-scale images of utility tunnel cracks to enhance the model's ability to learn crack features.

[0050] Step 203: Perform edge detection and contour extraction operations on each region containing cracks to obtain the contour map of each crack.

[0051] Step 204: Obtain the 3D point cloud data of the pipe gallery based on the depth map and RGB image. Perform point cloud density analysis and local curvature calculation on the 3D point cloud data of the pipe gallery in sequence to obtain the point cloud density analysis results and the curvature of the 3D point cloud data.

[0052] Step 205: Obtain the three-dimensional point cloud data of the cracks based on the three-dimensional point cloud data of the pipe gallery and the contour map of each crack.

[0053] Step 206: Construct a three-dimensional point cloud model of the crack based on the three-dimensional point cloud data of the crack, the point cloud density analysis results, and the curvature of the three-dimensional point cloud data.

[0054] Step 207: Process the three-dimensional point cloud model of the cracks using the principal component analysis algorithm to obtain the dimensions of each crack.

[0055] In practical applications, object detection models are obtained by training a pre-defined neural network based on transfer learning. Transfer learning is then used to enhance the model's generalization ability and enable recognition. The pre-defined neural network is a YOLOv8 model, including a CSP backbone network, a Feature Pyramid Network (FPN), and an SPPF spatial feature enhancement module, effectively preventing information loss issues common in deep networks.

[0056] The specific training process involves initial training on a general crack dataset. However, the model's performance in identifying cracks in underground utility tunnels is not very good at this stage. Then, a crack dataset from an underground utility tunnel is used to fine-tune the initially trained model. This application, while preserving the model's low-level feature extraction capabilities, uses the underground utility tunnel crack dataset for fine-tuning, significantly improving the model's recognition ability for specific scenarios. This results in a model with superior performance on the task of identifying cracks in underground utility tunnels. Through transfer learning, the model quickly adapts to crack detection tasks on a smaller, specialized dataset (the underground utility tunnel crack dataset), significantly improving detection accuracy.

[0057] In another exemplary embodiment of this application, edge detection and contour extraction operations are performed on each region containing cracks to obtain a contour map of each crack, specifically including:

[0058] For any region containing a crack, edge detection is performed on the region to obtain a first image.

[0059] The first image is processed using an adaptive thresholding method and the Canny edge detection algorithm to obtain the second image.

[0060] A contour extraction operation is performed on the second image to obtain the contour map of the cracks in the region.

[0061] In another exemplary embodiment of this application, the first image is processed using an adaptive thresholding method and the Canny edge detection algorithm to obtain a second image, specifically including:

[0062] The first image is processed using an adaptive thresholding method to obtain the threshold.

[0063] Based on the threshold, the first image is processed using the Canny edge detection algorithm to obtain the second image. The precise edge contour of the crack is extracted using the Canny edge detection algorithm.

[0064] In another exemplary embodiment of this application, after performing edge detection and contour extraction operations on each region containing cracks to obtain the contour map of each crack, the method further includes: performing morphological processing on the extracted crack contour image. Specifically, isolated noise points are first removed, and then the edges of the broken cracks are connected through a dilation operation. If necessary, opening or closing operations can be combined to further smooth and improve the crack contour structure. After morphological processing, the crack contour is more continuous and complete, which is beneficial for accurately drawing and labeling the detected crack regions on the original image. This application further processes each bounding box region detected by the target detection model, applies an edge detection algorithm to extract the crack contour within the region of interest, and enhances the crack contour through morphological processing to draw the detected crack contour on the original image.

[0065] In another exemplary embodiment of this application, the adaptive thresholding method is the OTSU algorithm. In Canny edge detection, there is an important step: hysteresis thresholding with dual thresholds, a high threshold TH and a low threshold TL, in a ratio between 2:1 and 3:1. This leads to a significant drawback of Canny edge detection: it cannot effectively detect crack edges in underground utility tunnels. Therefore, this application uses the OTSU algorithm to process the image, calculating the optimal threshold using the image's grayscale features to maximize the inter-class variance between the target and background components. First, the OTSU algorithm extracts the image histogram, calculates the cumulative sum of grayscale values, and calculates the mean of the grayscale values. Then, when the grayscale level in the image is less than k, the probability is recorded as p1, and when the grayscale level is greater than k, the probability is recorded as p2. Finally, the class variance is calculated, where k is the optimal threshold when the class variance reaches its maximum value. After obtaining the optimal threshold k, the image is binarized to separate the target object from the background.

[0066] In another exemplary embodiment of this application, the RGB image is input into a target detection model to obtain multiple regions containing cracks, specifically including:

[0067] The RGB image is sequentially subjected to contrast adjustment, noise reduction, and image normalization to obtain a preprocessed image. Specifically, firstly, CLAHE (Contrast-Limited Adaptive Histogram Equalization) technology is used to enhance image contrast to achieve contrast adjustment. Then, a bilateral filter is used to reduce noise while preserving edge details to achieve noise reduction. Finally, image normalization is performed to convert the grayscale image into a binary image for subsequent contour extraction, thus achieving image normalization.

[0068] The preprocessed image is input into the target detection model to obtain multiple regions containing cracks.

[0069] In another exemplary embodiment of this application, the three-dimensional point cloud data of the pipe gallery is obtained based on the depth map and RGB image, specifically including:

[0070] The initial 3D point cloud data of the utility tunnel is obtained based on the depth map and RGB image. Specifically, the depth image is preprocessed, including depth map transformation, unit transformation, and noise filtering to remove outliers (such as invalid depths and extreme values) and noise points, ensuring the accuracy and reliability of the depth data. Then, the RGB image and the preprocessed depth image are aligned to ensure that the RGB image and the depth image are spatially aligned (i.e., the same pixel point corresponds to the same spatial position in both images). Then, the aligned image is scaled and channel matched. For each pixel point in the channel matched image, its 2D coordinates (u,v), depth value D, and corresponding RGB values ​​(R,G,B) are obtained. Using camera intrinsic parameters, (u,v,D) are converted into 3D spatial coordinates (X,Y,Z). Based on (X,Y,Z,R,G,B), colored initial point cloud data, i.e., the initial 3D point cloud data of the utility tunnel, is generated. This step processes the RGB image and depth information, mapping the depth information back to three-dimensional space (combining two-dimensional pixel coordinates with the corresponding depth values ​​to convert them into three-dimensional space coordinates, thereby generating initial point cloud data, and combining the depth information to convert 2D edge points into 3D point cloud data).

[0071] The initial 3D point cloud data of the utility tunnel is optimized to obtain the final 3D point cloud data of the utility tunnel. Specifically, the initial 3D point cloud data is optimized by statistical filtering to remove outliers, voxel downsampling to reduce the amount of data, and calculating voxel centers to improve accuracy, thus obtaining the final 3D point cloud data of the utility tunnel.

[0072] In another exemplary embodiment of this application, point cloud density analysis and local curvature calculation are performed sequentially on the three-dimensional point cloud data of the pipe gallery to obtain the point cloud density analysis results and the curvature of the three-dimensional point cloud data, specifically as follows:

[0073] Point cloud density analysis is performed on the three-dimensional point cloud data of the utility tunnel to analyze the spatial distribution density of the point cloud and identify areas with sparse or abnormally dense point clouds. These areas often correspond to important features such as microcracks or crack intersections. Point cloud density distribution maps or density feature parameters are obtained to provide a basis for subsequent feature enhancement and crack identification.

[0074] Local curvature calculation is performed on the point cloud data after point cloud density analysis. The local curvature of each point in the point cloud is calculated to highlight the micro-structural features such as crack edges and corners, further enhancing the ability to express micro-cracks. The curvature value distribution of each point is obtained to form a curvature feature map. Based on the curvature feature map, the crack identification results can be more refined. Density analysis can help locate micro-crack regions, and curvature calculation further highlights the micro-structural features.

[0075] In another exemplary embodiment of this application, the three-dimensional point cloud data of the crack is obtained based on the three-dimensional point cloud data of the pipe gallery and the contour map of each crack. Specifically, the coordinates of each crack in the map are obtained based on the contour map of each crack, and a coordinate set is obtained. The three-dimensional point cloud data corresponding to each coordinate in the coordinate set is obtained from the three-dimensional point cloud data of the pipe gallery to obtain the three-dimensional point cloud data of the crack.

[0076] In another exemplary embodiment of this application, a three-dimensional point cloud model of the crack is constructed based on the three-dimensional point cloud data of the crack, the point cloud density analysis results, and the curvature of the three-dimensional point cloud data. Specifically, the three-dimensional features of the crack are extracted, the three-dimensional geometric features of the crack are extracted based on the curvature and point cloud density analysis results of the three-dimensional point cloud data, the three-dimensional characteristics of the crack are analyzed, a three-dimensional point cloud model is constructed, and the crack surface is reconstructed and three-dimensionally visualized.

[0077] In another exemplary embodiment of this application, the dimensions of the cracks include: the maximum length, average width, and maximum depth of the cracks. The dimensions of each crack are obtained by processing the three-dimensional point cloud model of the cracks using a principal component analysis algorithm, specifically including:

[0078] The three-dimensional point cloud model of the crack is processed by principal component analysis algorithm to obtain the principal direction axis (the main extension direction of the crack), the secondary direction axis (the width direction of the crack), and the normal direction axis (the depth direction of the crack).

[0079] Project the 3D point cloud model onto the main direction axis and calculate the maximum length of each crack.

[0080] The 3D point cloud model is projected onto the secondary direction axis, and the average width of each crack is calculated. Specifically, the 3D point cloud model is projected onto the secondary direction axis, and after removing outliers through statistical analysis, the average width of the cracks is calculated.

[0081] Project the 3D point cloud model onto the normal axis and calculate the maximum depth of each crack.

[0082] This application combines RGB images and depth images, and through feature alignment, scale normalization and channel matching, achieves cross-modal fusion of image features and depth information, thereby completing the final crack detection and size measurement, effectively preventing the information loss problem in deep networks.

[0083] When training the YOLOv8 model, this application completes the initial training using a general crack dataset. While retaining the model's ability to extract low-level features, it uses an underground utility tunnel crack dataset for model fine-tuning to achieve knowledge transfer. Through the transfer learning strategy, the model can quickly adapt to crack detection tasks even on a smaller specialized dataset (underground utility tunnel crack dataset), significantly improving detection accuracy.

[0084] Based on the same inventive concept, this application also provides an underground utility tunnel microcrack detection and measurement device for implementing the above-mentioned method for detecting and measuring microcracks in underground utility tunnels. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the underground utility tunnel microcrack detection and measurement device provided below can be found in the limitations of the underground utility tunnel microcrack detection and measurement method described above, and will not be repeated here.

[0085] In one exemplary embodiment, such as Figure 2 As shown, a device for detecting and measuring microcracks in underground utility tunnels is provided, comprising:

[0086] The input module is used to acquire RGB and depth images of the utility tunnel.

[0087] The crack detection module is used to input the RGB image into the target detection model to obtain multiple regions containing cracks, and to perform edge detection and contour extraction operations on each region containing cracks to obtain the contour map of each crack; the target detection model is obtained by first training the YOLOv8 model with a general crack dataset, and then training the trained YOLOv8 model with an underground utility tunnel crack dataset.

[0088] The 3D measurement module is used to obtain 3D point cloud data of the pipe gallery based on depth maps and RGB images. It then performs point cloud density analysis and local curvature calculation on the 3D point cloud data of the pipe gallery to obtain the point cloud density analysis results and the curvature of the 3D point cloud data. Based on the 3D point cloud data of the pipe gallery and the contour maps of each crack, it obtains the 3D point cloud data of the cracks. Based on the 3D point cloud data of the cracks, the point cloud density analysis results, and the curvature of the 3D point cloud data, it constructs a 3D point cloud model of the cracks. Finally, it processes the 3D point cloud model of the cracks using a principal component analysis algorithm to obtain the dimensions of each crack.

[0089] As an optional implementation, the underground utility tunnel microcrack detection and measurement device also includes an image preprocessing module, which performs contrast adjustment, noise elimination and image normalization operations on the RGB image in sequence.

[0090] As an optional implementation, the crack detection module includes: a target detection unit, an edge detection unit, an adaptive thresholding unit, a Canny edge detection unit, and a contour extraction and analysis unit.

[0091] The target detection unit is used to input the RGB image into the target detection model to obtain multiple regions containing cracks.

[0092] An edge detection unit is used to perform edge detection on any region containing a crack to obtain a first image.

[0093] An adaptive thresholding unit is used to process the first image using an adaptive thresholding method to obtain a threshold.

[0094] The Canny edge detection unit is used to process the first image using the Canny edge detection algorithm based on the threshold to obtain the second image.

[0095] The contour extraction and analysis unit is used to perform contour extraction on the second image to obtain the contour map of the crack in the region.

[0096] As an optional implementation, the three-dimensional measurement module includes: a point cloud generation and processing unit, a target three-dimensional feature extraction unit, and a crack size calculation unit.

[0097] The point cloud generation and processing unit is used to obtain the initial three-dimensional point cloud data of the pipe gallery based on the depth map and RGB image; optimize the initial three-dimensional point cloud data of the pipe gallery to obtain the three-dimensional point cloud data of the pipe gallery; and perform point cloud density analysis and local curvature calculation on the three-dimensional point cloud data of the pipe gallery in sequence to obtain the point cloud density analysis results and the curvature of the three-dimensional point cloud data.

[0098] The target 3D feature extraction unit is used to obtain the 3D point cloud data of the cracks based on the 3D point cloud data of the pipe gallery and the contour map of each crack; and to construct the 3D point cloud model of the cracks based on the 3D point cloud data of the cracks, the point cloud density analysis results and the curvature of the 3D point cloud data.

[0099] The crack size calculation unit is used to process the three-dimensional point cloud model of the cracks using the principal component analysis algorithm to obtain the size of each crack.

[0100] The 3D measurement module is used to realize the 3D spatial feature analysis and measurement of cracks. Specifically, it includes depth data processing, reading and processing depth images, creating custom depth frame objects to process depth maps of different formats, and applying depth filtering algorithms to remove outliers and noise points; and a point cloud generation and processing unit, which generates 3D point cloud data based on RGB images and depth maps, uses the Open3D library to process and analyze point cloud data, and realizes point cloud optimization, denoising and smoothing.

[0101] This application inputs RGB images into the CSP backbone network of the target detection model for feature extraction, ultimately obtaining multi-scale feature maps. These maps then undergo feature processing. First, the acquired multi-scale feature maps are processed using a C2f residual module. Next, spatial feature enhancement is performed using an SPPF spatial feature enhancement module. Finally, multi-scale feature fusion is achieved through a Feature Pyramid Network (FPN). After feature processing, the extracted contours are fused with previously aligned depth information to finally read the depth value of the detected target for subsequent 3D measurement.

[0102] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data on the detection and measurement of microcracks in underground utility tunnels. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for detecting and measuring microcracks in underground utility tunnels.

[0103] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.

[0104] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.

[0105] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.

[0106] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0107] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0108] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for detecting and measuring microcracks in underground utility tunnels, characterized in that, The method for detecting and measuring microcracks in underground utility tunnels includes: Acquire RGB and depth images of the utility tunnel; The RGB image is input into the target detection model to obtain multiple regions containing cracks; the target detection model is obtained by first training the YOLOv8 model with a general crack dataset, and then training the trained YOLOv8 model with an underground utility tunnel crack dataset; Edge detection and contour extraction are performed on each region containing cracks to obtain the contour map of each crack; The 3D point cloud data of the pipe gallery is obtained based on the depth map and RGB image. Point cloud density analysis and local curvature calculation are performed on the 3D point cloud data of the pipe gallery in sequence to obtain the point cloud density analysis results and the curvature of the 3D point cloud data. The 3D point cloud data of the cracks is obtained based on the 3D point cloud data of the pipe gallery and the contour map of each crack. A three-dimensional point cloud model of the crack is constructed based on the crack's three-dimensional point cloud data, point cloud density analysis results, and the curvature of the three-dimensional point cloud data. The dimensions of each crack are obtained by processing the three-dimensional point cloud model of the crack using principal component analysis algorithm.

2. The method for detecting and measuring microcracks in underground utility tunnels according to claim 1, characterized in that, Edge detection and contour extraction are performed on each region containing cracks to obtain the contour map of each crack, specifically including: For any region containing a crack, edge detection is performed on the region to obtain a first image; The first image is processed using an adaptive thresholding method and the Canny edge detection algorithm to obtain the second image; A contour extraction operation is performed on the second image to obtain the contour map of the cracks in the region.

3. The method for detecting and measuring microcracks in underground utility tunnels according to claim 2, characterized in that, The second image is obtained by processing the first image using an adaptive thresholding method and the Canny edge detection algorithm, specifically including: The threshold is obtained by processing the first image using an adaptive thresholding method; The first image is processed using the Canny edge detection algorithm based on the threshold to obtain the second image.

4. The method for detecting and measuring microcracks in underground utility tunnels according to claim 1, characterized in that, The RGB image is input into the target detection model to obtain multiple regions containing cracks, specifically including: The RGB image is sequentially subjected to contrast adjustment, noise reduction, and image normalization to obtain the preprocessed image. The preprocessed image is input into the target detection model to obtain multiple regions containing cracks.

5. The method for detecting and measuring microcracks in underground utility tunnels according to claim 1, characterized in that, The dimensions of the cracks include: maximum length, average width, and maximum depth. These dimensions are obtained by processing the 3D point cloud model of the cracks using principal component analysis (PCA) algorithm. The three-dimensional point cloud model of the crack was processed by principal component analysis algorithm to obtain the principal direction axis, secondary direction axis and normal direction axis; Project the 3D point cloud model of the crack onto the main direction axis and calculate the maximum length of each crack; Project the 3D point cloud model of the crack onto the secondary direction axis and calculate the average width of each crack; The 3D point cloud model of the cracks is projected onto the normal axis, and the maximum depth of each crack is calculated.

6. The method for detecting and measuring microcracks in underground utility tunnels according to claim 1, characterized in that, The 3D point cloud data of the pipe gallery is obtained based on the depth map and RGB image, specifically including: The initial 3D point cloud data of the pipe gallery was obtained based on the depth map and RGB image; The initial three-dimensional point cloud data of the utility tunnel is optimized to obtain the three-dimensional point cloud data of the utility tunnel.

7. A device for detecting and measuring microcracks in underground utility tunnels, characterized in that, The underground utility tunnel microcrack detection and measurement device includes: The input module is used to acquire RGB and depth images of the utility tunnel. The crack detection module is used to input the RGB image into the target detection model to obtain multiple regions containing cracks, and to perform edge detection and contour extraction operations on each region containing cracks to obtain the contour map of each crack; the target detection model is obtained by first training the YOLOv8 model with a general crack dataset, and then training the trained YOLOv8 model with an underground utility tunnel crack dataset. The 3D measurement module is used to obtain 3D point cloud data of the pipe gallery based on depth maps and RGB images. It then performs point cloud density analysis and local curvature calculation on the 3D point cloud data of the pipe gallery to obtain the point cloud density analysis results and the curvature of the 3D point cloud data. Based on the 3D point cloud data of the pipe gallery and the contour maps of each crack, it obtains the 3D point cloud data of the cracks. Based on the 3D point cloud data of the cracks, the point cloud density analysis results, and the curvature of the 3D point cloud data, it constructs a 3D point cloud model of the cracks. Finally, it processes the 3D point cloud model of the cracks using a principal component analysis algorithm to obtain the dimensions of each crack.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for detecting and measuring microcracks in underground utility tunnels according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for detecting and measuring microcracks in underground utility tunnels as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for detecting and measuring microcracks in underground utility tunnels as described in any one of claims 1-6.

Images