Subway tunnel structure disease type prediction method and system

Abstract

The invention relates to a subway tunnel structure disease type prediction method and system. The method comprises the following steps: acquiring an infrared image of a subway tunnel; performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image; calibrating a disease type presented by each feature-enhanced subway tunnel infrared image to obtain a training sample; inputting the training sample into a MobileNet model for training to obtain a subway tunnel disease type prediction model; and monitoring a target subway tunnel by using the subway tunnel disease type prediction model. According to the method, multi-scale enhancement is carried out on the infrared image, so that the identifiability of disease features can be improved, different types of diseases are more obvious in the image, and the model training effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disease identification, and in particular to a method and system for predicting the type of structural disease in a subway tunnel. Background Art

[0002] With the acceleration of urbanization, subways, as an important means of public transportation, have been widely used around the world. However, due to the complex environment in which they are located and the influence of long-term use, subway tunnels are prone to various structural defects, which seriously affect the safe operation and service quality of the subway. Therefore, timely detection and accurate prediction of tunnel structure defect types are of great significance to improving the safety and operational efficiency of subway tunnels.

[0003] Existing tunnel disease detection technologies mostly rely on manual inspection, regular monitoring and empirical judgment. The methods are relatively simple and it is difficult to comprehensively and timely identify and predict potential tunnel diseases. Summary of the invention

[0004] To solve the above problems, an object of the embodiments of the present invention is to provide a method and system for predicting the type of structural damage in a subway tunnel.

[0005] A method for predicting the type of structural damage in a subway tunnel, comprising: Step 1: Collect infrared images of subway tunnels; Step 2: performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image; Step 3: Calibrate the damage type presented by each feature-enhanced subway tunnel infrared image to obtain training samples; Step 4: Input the training samples into the MobileNet model for training to obtain a subway tunnel disease type prediction model; Step 5: Use the subway tunnel disease type prediction model to monitor the target subway tunnel.

[0006] Preferably, the step 2: performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image comprises: Step 2.1: performing Gaussian pyramid decomposition on the subway tunnel infrared image to obtain a Gaussian pyramid decomposition image; Step 2.2: performing edge monitoring processing on the subway tunnel infrared image to obtain an edge image; Step 2.3: Upsample the Gaussian pyramid decomposition image to obtain an upsampled image; Step 2.4: constructing a pyramid image block using the upsampled image and the edge image; Step 2.5: Perform multi-scale feature enhancement on the pyramid image block to obtain a feature-enhanced subway tunnel infrared image.

[0007] Preferably, the step 2.1: performing Gaussian pyramid decomposition on the subway tunnel infrared image to obtain a Gaussian pyramid decomposition image comprises: The subway tunnel infrared image is subjected to Gaussian pyramid decomposition using a Gaussian pyramid decomposition formula to obtain a Gaussian pyramid decomposition image; wherein the Gaussian pyramid decomposition formula is:

[0008] in, represents the infrared image of a subway tunnel, represents the first-level Gaussian pyramid decomposition image, represents the i-th level Gaussian pyramid decomposition image, Represents a Gaussian filter function.

[0009] Preferably, the step 2.2: performing edge monitoring processing on the subway tunnel infrared image to obtain an edge image comprises: Step 2.2.1: Calculate the change amplitude and change direction of each pixel point of the subway tunnel infrared image; wherein the calculation formula for the change amplitude and change direction of the pixel point is:

[0010] in, Indicates the change amplitude of the pixel point. Indicates the direction of pixel change. represents the magnitude in the x direction, represents the magnitude in the y direction; Step 2.2.2: Pixel points whose change amplitude and change direction are within the preset range are regarded as edge points, and the remaining pixel points are set to zero to obtain an edge image.

[0011] Preferably, the step 2.4: constructing a pyramid image block using the upsampled image and the edge image comprises: The upsampled image is differentially calculated with the Gaussian pyramid decomposition image to obtain a pyramid image block; wherein the pyramid image block construction formula is:

[0012] in, represents the i-th pyramid image block, represents the upsampled image corresponding to the i-th layer Gaussian pyramid decomposition image, Represents an edge image.

[0013] Preferably, the step 2.5: performing multi-scale feature enhancement on the pyramid image block to obtain a feature-enhanced subway tunnel infrared image comprises: Step 2.5.1: Extract the first pyramid image block as the base image and the remaining pyramid image blocks as detail images; Step 2.5.2: performing gamma correction on the basic image to obtain a corrected basic image; Step 2.5.3: performing enhancement processing on the detail image to obtain an enhanced detail image; Step 2.5.4: Fuse the corrected basic image and the enhanced detail image to obtain the feature-enhanced subway tunnel infrared image.

[0014] Preferably, the step 2.5.3: performing enhancement processing on the detail image to obtain an enhanced detail image comprises: The detail image is enhanced using a sharpening enhancement operator to obtain an enhanced detail image; wherein the enhancement processing formula is:

[0015] in, represents the enhanced detail image, Indicates the detail image in The pixel value at position, represents the sharpening enhancement operator, Indicates the preset threshold.

[0016] The present invention also provides a subway tunnel structure defect type prediction system, comprising: Infrared image acquisition module, used to collect infrared images of subway tunnels; An enhancement module, used for performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image; A calibration module is used to calibrate the type of damage presented by each feature-enhanced subway tunnel infrared image to obtain training samples; The training module is used to input the training samples into the MobileNet model for training to obtain a subway tunnel disease type prediction model; A monitoring module is used to monitor the target subway tunnel using the subway tunnel disease type prediction model.

[0017] The present invention also provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and wherein the computer program, when executed by the processor, implements the steps in the above-mentioned method for predicting the type of structural defects in a subway tunnel.

[0018] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the above-mentioned method for predicting the type of structural defects in a subway tunnel are implemented.

[0019] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention relates to a method for predicting the types of structural defects in a subway tunnel. Compared with the prior art, the present invention can improve the recognizability of defect features by performing multi-scale enhancement on infrared images, making different types of defects more obvious in images and improving the effect of model training.

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A flow chart of a method for predicting structural damage types of subway tunnels provided by the present invention; Figure 2 The infrared image of the subway tunnel to be processed provided by the present invention; Figure 3 The infrared image of a subway tunnel after feature enhancement provided by the present invention. DETAILED DESCRIPTION

[0023] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0024] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0025] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] See also Figure 1 , a method for predicting the type of structural damage in a subway tunnel, comprising: Step 1: Collect infrared images of subway tunnels; Step 2: performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image; Furthermore, the step 2 comprises: Step 2.1: performing Gaussian pyramid decomposition on the subway tunnel infrared image to obtain a Gaussian pyramid decomposition image; Among them, step 2.1 includes: The subway tunnel infrared image is subjected to Gaussian pyramid decomposition using a Gaussian pyramid decomposition formula to obtain a Gaussian pyramid decomposition image; wherein the Gaussian pyramid decomposition formula is:

[0027] in, represents the infrared image of a subway tunnel, represents the first-level Gaussian pyramid decomposition image, represents the i-th level Gaussian pyramid decomposition image, Represents a Gaussian filter function.

[0028] Step 2.2: performing edge monitoring processing on the subway tunnel infrared image to obtain an edge image; In the step 2.2, it includes: Step 2.2.1: Calculate the change amplitude and change direction of each pixel point of the subway tunnel infrared image; wherein the calculation formula for the change amplitude and change direction of the pixel point is:

[0029] in, Indicates the change amplitude of the pixel point. Indicates the direction of pixel change. represents the magnitude in the x direction, represents the magnitude in the y direction; Step 2.2.2: Pixel points whose change amplitude and change direction are within the preset range are regarded as edge points, and the remaining pixel points are set to zero to obtain an edge image.

[0030] Step 2.3: Upsample the Gaussian pyramid decomposition image to obtain an upsampled image; Step 2.4: constructing a pyramid image block using the upsampled image and the edge image; Wherein, the step 2.4 comprises: The upsampled image is differentially calculated with the Gaussian pyramid decomposition image to obtain a pyramid image block; wherein the pyramid image block construction formula is:

[0031] in, represents the i-th pyramid image block, represents the upsampled image corresponding to the i-th layer Gaussian pyramid decomposition image, Represents an edge image.

[0032] By introducing edge information, the present invention can highlight important features in each layer of the pyramid, enhance the visualization effect of information, and provide a clearer basis for subsequent image analysis tasks.

[0033] Step 2.5: performing multi-scale feature enhancement on the pyramid image block to obtain a feature-enhanced subway tunnel infrared image; Among them, step 2.5 includes: Step 2.5.1: Extract the first pyramid image block as the base image and the remaining pyramid image blocks as detail images; Step 2.5.2: performing gamma correction on the basic image to obtain a corrected basic image; The basic image contains global information of the entire image. The present invention can avoid the problem of excessive local contrast enhancement in the image by performing Gamma correction on the basic image.

[0034] Step 2.5.3: Use a sharpening enhancement operator to enhance the detail image to obtain an enhanced detail image; wherein the enhancement processing formula is:

[0035] in, represents the enhanced detail image, Indicates the detail image in The pixel value at position, represents the sharpening enhancement operator, Indicates the preset threshold.

[0036] The present invention can extract high-frequency information in an image through a sharpening enhancement operator, which is particularly important for image texture analysis and can make the image look clearer.

[0037] Step 2.5.4: Fuse the corrected basic image and the enhanced detail image to obtain the feature-enhanced subway tunnel infrared image; Through the linear weighted fusion method, the integrity of global information can be maintained while enhancing details, thereby avoiding information loss. The fusion formula is:

[0038] in, represents the infrared image of the subway tunnel after feature enhancement, represents the corrected base image, represents the enhanced detail image, Represents weight.

[0039] like Figure 2-3 As shown, the present invention can achieve significant contrast effect and rich texture detail display by fusing the infrared images of subway tunnels.

[0040] Step 3: Calibrate the damage type presented by each feature-enhanced subway tunnel infrared image to obtain training samples; In practical applications, the present invention cooperates with professional subway maintenance engineers or tunnel disease detection experts to identify and classify various disease types in infrared images, and annotates the infrared images using image annotation tools (such as Labelme, VGG ImageAnnotator, etc.).

[0041] Step 4: Input the training samples into the MobileNet model for training to obtain a subway tunnel disease type prediction model; Step 5: Use the subway tunnel disease type prediction model to monitor the target subway tunnel.

[0042] The present invention can improve the recognizability of disease features by performing multi-scale enhancement on infrared images, making different types of diseases more obvious in the images, making it easier for machine learning models to identify and classify targets, thereby improving the training effect of the model.

[0043] The present invention also provides a subway tunnel structure defect type prediction system, comprising: Infrared image acquisition module, used to collect infrared images of subway tunnels; An enhancement module, used for performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image; A calibration module is used to calibrate the type of damage presented by each feature-enhanced subway tunnel infrared image to obtain training samples; The training module is used to input the training samples into the MobileNet model for training to obtain a subway tunnel disease type prediction model; A monitoring module is used to monitor the target subway tunnel using the subway tunnel disease type prediction model.

[0044] The present invention also provides an electronic device, comprising a bus, a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory and the processor are connected via the bus, and wherein the computer program, when executed by the processor, implements the steps in the above-mentioned method for predicting the type of structural defects of a subway tunnel. Compared with the prior art, the beneficial effects of the electronic device provided by the present invention are the same as the beneficial effects of the method for predicting the type of structural defects of a subway tunnel described in the above-mentioned technical solution, and are not elaborated herein.

[0045] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the above-mentioned method for predicting the type of structural defects of a subway tunnel are implemented. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present invention are the same as the beneficial effects of the method for predicting the type of structural defects of a subway tunnel described in the above-mentioned technical solution, which will not be elaborated here.

[0046] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technical solution that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for predicting the type of structural damage in a subway tunnel, characterized in that: include: Step 1: Collect infrared images of subway tunnels; Step 2: performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image; Step 3: Calibrate the damage type presented by each feature-enhanced subway tunnel infrared image to obtain training samples; Step 4: Input the training samples into the MobileNet model for training to obtain a subway tunnel disease type prediction model; Step 5: Use the subway tunnel disease type prediction model to monitor the target subway tunnel.

2. A method for predicting structural damage types of subway tunnels according to claim 1, characterized in that: The step 2: performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image, comprises: Step 2.1: performing Gaussian pyramid decomposition on the subway tunnel infrared image to obtain a Gaussian pyramid decomposition image; Step 2.2: performing edge monitoring processing on the subway tunnel infrared image to obtain an edge image; Step 2.3: Upsample the Gaussian pyramid decomposition image to obtain an upsampled image; Step 2.4: constructing a pyramid image block using the upsampled image and the edge image; Step 2.5: Perform multi-scale feature enhancement on the pyramid image block to obtain a feature-enhanced subway tunnel infrared image.

3. A method for predicting structural damage types of subway tunnels according to claim 2, characterized in that: The step 2.1: performing Gaussian pyramid decomposition on the subway tunnel infrared image to obtain a Gaussian pyramid decomposition image, comprises: The subway tunnel infrared image is subjected to Gaussian pyramid decomposition using a Gaussian pyramid decomposition formula to obtain a Gaussian pyramid decomposition image; wherein the Gaussian pyramid decomposition formula is: ； in, represents the infrared image of a subway tunnel, represents the first-level Gaussian pyramid decomposition image, represents the i-th level Gaussian pyramid decomposition image, Represents a Gaussian filter function.

4. A method for predicting structural damage types of subway tunnels according to claim 3, characterized in that: The step 2.2: performing edge monitoring processing on the subway tunnel infrared image to obtain an edge image, comprises: Step 2.2.1: Calculate the change amplitude and change direction of each pixel point of the subway tunnel infrared image; wherein the calculation formula for the change amplitude and change direction of the pixel point is: ； in, Indicates the change amplitude of the pixel point. Indicates the direction of pixel change. represents the magnitude in the x direction, represents the magnitude in the y direction; Step 2.2.2: Pixel points whose change amplitude and change direction are within the preset range are regarded as edge points, and the remaining pixel points are set to zero to obtain an edge image.

5. A method for predicting structural damage types of subway tunnels according to claim 4, characterized in that: The step 2.4: constructing a pyramid image block using the up-sampled image and the edge image, comprises: The upsampled image is differentially calculated with the Gaussian pyramid decomposition image to obtain a pyramid image block; wherein the pyramid image block construction formula is: ； in, represents the i-th pyramid image block, represents the upsampled image corresponding to the i-th layer Gaussian pyramid decomposition image, Represents an edge image.

6. A method for predicting structural damage types of subway tunnels according to claim 5, characterized in that: The step 2.5: performing multi-scale feature enhancement on the pyramid image block to obtain a feature-enhanced subway tunnel infrared image, comprises: Step 2.5.1: Extract the first pyramid image block as the base image and the remaining pyramid image blocks as detail images; Step 2.5.2: performing gamma correction on the basic image to obtain a corrected basic image; Step 2.5.3: performing enhancement processing on the detail image to obtain an enhanced detail image; Step 2.5.4: Fuse the corrected basic image and the enhanced detail image to obtain the feature-enhanced subway tunnel infrared image.

7. A method for predicting structural damage types of subway tunnels according to claim 6, characterized in that: The step 2.5.3: performing enhancement processing on the detail image to obtain an enhanced detail image comprises: The detail image is enhanced using a sharpening enhancement operator to obtain an enhanced detail image; wherein the enhancement processing formula is: ； in, represents the enhanced detail image, Indicates the detail image in The pixel value at position, represents the sharpening enhancement operator, Indicates the preset threshold.

8. A subway tunnel structural damage type prediction system, characterized in that: include: Infrared image acquisition module, used to collect infrared images of subway tunnels; An enhancement module, used for performing multi-scale enhancement on the subway tunnel infrared image to obtain a feature-enhanced subway tunnel infrared image; A calibration module is used to calibrate the type of damage presented by each feature-enhanced subway tunnel infrared image to obtain training samples; The training module is used to input the training samples into the MobileNet model for training to obtain a subway tunnel disease type prediction model; A monitoring module is used to monitor the target subway tunnel using the subway tunnel disease type prediction model.

9. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps of a method for predicting structural damage types of a subway tunnel as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for predicting structural damage types of a subway tunnel as described in any one of claims 1 to 7 are implemented.

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