Shield segment detection method and system using image features and feature enhancement

Abstract

The invention discloses a shield segment detection method and system using image features and feature enhancement, and the method comprises the steps: carrying out the preprocessing of a target image sample, and obtaining a standard target image sample; a shield segment detection model with an image feature recognition capability is constructed, the shield segment detection model comprises a trunk feature extraction network, a reinforced feature extraction network, a classifier and a regression device, and the trunk feature extraction network comprises a CONV-C2f module; inputting a standard target image sample into the shield segment detection model, and performing iterative training on the shield segment detection model to obtain a target shield segment detection model; and inputting a to-be-detected image containing the shield segment into the target shield segment detection model, wherein the target shield segment detection model outputs a detection result corresponding to the to-be-detected image. The image is divided into different levels, features are extracted after the image is convolved, and then the features are fused, so that the type of the segment is judged more accurately according to feature verification.

Description

Technical Field

[0001] This invention belongs to the field of image detection technology, and in particular relates to a method and system for detecting tunnel lining segments using image features and feature enhancement. Background Technology

[0002] Shield tunneling is a highly efficient construction method widely used in tunnel construction. Shield segments are the main components of tunnel lining, and their accurate identification is crucial for automated assembly, quality inspection, and safety assurance during the construction process. However, shield segments may be affected by factors such as changes in lighting, background interference, and complex textures in the actual construction environment, making it difficult for traditional identification methods to meet the requirements of high accuracy and real-time performance. Summary of the Invention

[0003] This invention provides a method and system for detecting tunnel lining segments using image features and feature enhancement, which addresses the technical problem that traditional identification methods cannot meet the requirements of high precision and real-time performance.

[0004] In a first aspect, the present invention provides a method for detecting tunnel lining segments using image features and feature enhancement, comprising: Acquire target image samples and preprocess the target image samples to obtain standard target image samples; A shield tunnel segment detection model capable of recognizing image features is constructed. This model includes a backbone feature extraction network, an enhanced feature extraction network, a classifier, and a regressor. The backbone feature extraction network includes a CONV-C2f module, the expression of which is:

[0005] , , , , , In the formula, The final output feature map, To compress the number of channels using 1x1 convolution, This is the feature map obtained by concatenating the feature maps of the main branch and the secondary branch. To be and The stitched feature map The feature map after processing of the main branch. The feature map is directly passed to the secondary branch. for Processed through n Bottleneck modules for Processed through n Bottleneck modules To divide the feature map into two parts, This is the feature map after being expanded through a 1x1 convolution. To Perform a 1x1 convolution operation. The input feature map; The standard target image sample is input into the shield tunnel segment detection model, and the shield tunnel segment detection model is iteratively trained to obtain the target shield tunnel segment detection model. The image to be detected, containing tunnel segments, is input into the target tunnel segment detection model, and the target tunnel segment detection model outputs the detection result corresponding to the image to be detected.

[0006] Secondly, the present invention provides a shield tunnel segment inspection system utilizing image features and feature enhancement, comprising: The acquisition module is configured to acquire target image samples and preprocess the target image samples to obtain standard target image samples; A shield tunnel segment detection model capable of recognizing image features is constructed. This model includes a backbone feature extraction network, an enhanced feature extraction network, a classifier, and a regressor. The backbone feature extraction network includes a CONV-C2f module, the expression of which is:

[0007] , , , , , In the formula, The final output feature map, To compress the number of channels using 1x1 convolution, This is the feature map obtained by concatenating the feature maps of the main branch and the secondary branch. To be and The stitched feature map The feature map after processing of the main branch. The feature map is directly passed to the secondary branch. for Processed through n Bottleneck modules for Processed through n Bottleneck modules To divide the feature map into two parts, This is the feature map after being expanded through a 1x1 convolution. To Perform a 1x1 convolution operation. The input feature map; The training module is configured to input the standard target image samples into the shield tunnel segment detection model, and iteratively train the shield tunnel segment detection model to obtain the target shield tunnel segment detection model. The output module is configured to input the image to be detected, which contains shield tunnel segments, into the target shield tunnel segment detection model, and the target shield tunnel segment detection model outputs the detection result corresponding to the image to be detected.

[0008] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the shield tunnel segment detection method utilizing image features and feature enhancement according to any embodiment of the present invention.

[0009] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the shield tunnel segment detection method utilizing image features and feature enhancement according to any embodiment of the present invention.

[0010] This application proposes a shield tunnel segment detection method and system that utilizes image features and feature enhancement. It proposes a network architecture that enhances feature extraction capabilities by dividing the image into different levels of size, extracting features after convolution, and then fusing the features to more accurately determine the segment category based on feature verification. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a shield tunnel segment detection method utilizing image features and feature enhancement, provided as an embodiment of the present invention; Figure 2A schematic diagram of the principle of shield tunnel segment detection using image features and feature enhancement is provided for a specific embodiment of the present invention; Figure 3 This is a structural block diagram of a shield tunnel segment detection system utilizing image features and feature enhancement, provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Please see Figure 1 The diagram shows a flowchart of a shield tunnel segment detection method based on image features and feature enhancement according to this application.

[0015] like Figure 1 As shown, the shield tunnel segment detection method utilizing image features and feature enhancement specifically includes the following steps: Step S101: Obtain target image samples and preprocess the target image samples to obtain standard target image samples.

[0016] In this step, the target image sample is input into a preset image processor. The image processor performs deblurring and brightening operations on the target image sample and outputs a standard target image sample corresponding to the target image sample.

[0017] Step S102: Construct a shield tunnel segment detection model with the ability to recognize image features. The shield tunnel segment detection model includes a backbone feature extraction network, an enhanced feature extraction network, a classifier, and a regressor. The backbone feature extraction network includes a CONV-C2f module.

[0018] In this step, the backbone feature extraction network consists of a CONV-C2f module and a channel attention module. The shield tunnel segment detection model automatically performs forward propagation. The input shield tunnel segment detection model passes through multiple conv-c2f modules in sequence. All three effective feature layers pass through the channel attention module. After passing through the channel attention module, feature enhancement is performed in the enhancement feature network. Finally, the data is input into the classifier and regressor for discrimination as location labels.

[0019] The expression for the CONV-C2f module is:

[0020] , , , , , In the formula, The final output feature map, To compress the number of channels using 1x1 convolution, This is the feature map obtained by concatenating the feature maps of the main branch and the secondary branch. To be and The stitched feature map The feature map after processing of the main branch. The feature map is directly passed to the secondary branch. for Processed through n Bottleneck modules for Processed through n Bottleneck modules To divide the feature map into two parts, This is the feature map after being expanded through a 1x1 convolution. To Perform a 1x1 convolution operation. The input feature map; It should be noted that the backbone feature extraction network also includes a channel attention module. The expression for the weighting operation via the channel attention module is as follows: , , , , In the formula, This is the feature map after spatial attention weighting. This is the output of the spatial attention module. This indicates element-wise multiplication. This is the feature map after channel attention weighting. For the output of the channel attention module, The Sigmoid activation function is used to normalize the output to the range [0,1]. For feature map The average value in the channel dimension. For feature map The maximum value in the channel dimension. This is a global average pooling operation. To achieve dimensionality reduction and dimensionality increase of the features after average pooling through two fully connected layers, This is a global max pooling operation. To achieve dimensionality reduction and dimensionality increase of the features after max pooling operation, two fully connected layers are used.

[0021] The bounding box regression loss function in the shield tunnel segment detection model is: , In the formula, For loss function, The area of ​​the actual bounding box. To predict the area of ​​the bounding box, To predict the minimum closure region between the bounding box and the true bounding box, To predict the intersection-union ratio between the bounding box and the true bounding box.

[0022] Class loss measures the accuracy of a model's predictions of a target class. This model typically uses the multi-class cross-entropy loss function. The formula for class loss is: , In the formula, For the category loss function, The number of grid cells, The number of bounding boxes per grid cell. This is an indicator function; it is 1 if the j-th bounding box of the i-th cell contains the target, and 0 otherwise. One-Hot encoding for real labels Let be the predicted probability for category j; Step S103: Input the standard target image sample into the shield tunnel segment detection model, and perform iterative training on the shield tunnel segment detection model to obtain the target shield tunnel segment detection model.

[0023] Step S104: Input the image to be detected, which contains the tunnel lining segment, into the target tunnel lining segment detection model, and the target tunnel lining segment detection model outputs the detection result corresponding to the image to be detected.

[0024] In this step, the detection results include the location of the tunnel boring machine segments in the image to be detected and the segment type.

[0025] In this embodiment, please refer to Figure 2 The principles of shield tunnel segment detection using image features and feature enhancement include: Preprocessing typically involves resizing the image to the size required for the model input (e.g., 640×640). Preprocessing includes operations such as image resizing and normalization.

[0026] The trained model is loaded into memory, and the prepared images are then propagated forward through the model. The model identifies and classifies any objects that may exist in the images, generating corresponding bounding boxes and class labels.

[0027] The detection results output by the model (including bounding box coordinates, class confidence, and other information) are parsed into a visual form for easier understanding.

[0028] In summary, the method of this application proposes a network architecture that enhances feature extraction capabilities. It divides the image into different levels of size, extracts features after convolution, and then fuses the features to more accurately determine the category of the pipe segment based on feature verification.

[0029] In one specific embodiment, the simulation experiment of this invention was implemented in PyTORCH, and the experiment used an NVIDIA(R) Tesla(R) V100 GPU for training and testing. For ease of comparison, the same tunnel segment dataset was used. The superior performance of the proposed method was verified by comparing it with three other representative image inpainting algorithms. The five algorithms were, in order, FAST-RCNN, SSD, and YOLOV8n. Several common metrics were used for quality evaluation: mAP@0.5 / % , FLOPs, and Params. The experimental results on the tunnel segment dataset are shown in Table 1.

[0030] Table 1. Results of ablation experiments on the attention mechanism of the backbone network , Table 2 Comparison of experimental results , As shown in Table 2, the proposed method improves the MAP@0.5 by 12.6%, 25.6%, and 6.4% compared to Fast-RCNN, SSD, and YOLOv8n, respectively. Simultaneously, this invention achieves significant reductions in both the number of parameters and the total number of floating-point operations (FLOPS). Comparing different attention modules, the CBAM attention mechanism maintains similar levels of model parameters and computational cost compared to other candidate modules, but demonstrates an advantage in the key metric of mAP@0.5. The introduction of CBAM improves model performance without affecting model resource consumption.

[0031] Please see Figure 3 The diagram shows a structural block diagram of a shield tunnel segment detection system that utilizes image features and feature enhancement according to this application.

[0032] like Figure 3 As shown, the shield tunnel segment detection system 200 includes an acquisition module 210, a construction module 220, a training module 230, and an output module 240.

[0033] The acquisition module 210 is configured to acquire target image samples and preprocess the target image samples to obtain standard target image samples. Module 220 is configured to construct a shield tunnel segment detection model capable of recognizing image features. The shield tunnel segment detection model includes a backbone feature extraction network, an enhanced feature extraction network, a classifier, and a regressor. The backbone feature extraction network includes a C2f module and a CONV module. The C2f module fuses the features extracted by the CONV module, wherein the fusion expression is:

[0034] , , , , , In the formula, The final output feature map, To compress the number of channels using 1x1 convolution, This is the feature map obtained by concatenating the feature maps of the main branch and the secondary branch. To be and The stitched feature map The feature map after processing of the main branch. The feature map is directly passed to the secondary branch. for Processed through n Bottleneck modules for Processed through n Bottleneck modules To divide the feature map into two parts, This is the feature map after being expanded through a 1x1 convolution. To Perform a 1x1 convolution operation. The input feature map; Training module 230 is configured to input the standard target image sample into the shield tunnel segment detection model, perform iterative training on the shield tunnel segment detection model, and obtain the target shield tunnel segment detection model. The output module 240 is configured to input an image to be detected containing shield tunnel segments into the target shield tunnel segment detection model, and the target shield tunnel segment detection model outputs a detection result corresponding to the image to be detected.

[0035] It should be understood that Figure 3 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 3 The various modules in the document will not be described in detail here.

[0036] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the shield tunnel segment detection method utilizing image features and feature enhancement in any of the above method embodiments. In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows: Acquire target image samples and preprocess the target image samples to obtain standard target image samples; A shield tunnel segment detection model capable of recognizing image features is constructed. The model includes a backbone feature extraction network, an enhanced feature extraction network, a classifier, and a regressor. The backbone feature extraction network comprises a C2f module and a CONV module. The C2f module fuses the features extracted by the CONV module, where the fusion expression is:

[0037] , , , , , In the formula, The final output feature map, To compress the number of channels using 1x1 convolution, This is the feature map obtained by concatenating the feature maps of the main branch and the secondary branch. To be and The stitched feature map The feature map after processing of the main branch. The feature map is directly passed to the secondary branch. for Processed through n Bottleneck modules for Processed through n Bottleneck modules To divide the feature map into two parts, This is the feature map after being expanded through a 1x1 convolution. To Perform a 1x1 convolution operation. The input feature map; The standard target image sample is input into the shield tunnel segment detection model, and the shield tunnel segment detection model is iteratively trained to obtain the target shield tunnel segment detection model. The image to be detected, containing tunnel segments, is input into the target tunnel segment detection model, and the target tunnel segment detection model outputs the detection result corresponding to the image to be detected.

[0038] Computer-readable storage media may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required for at least one function; the data storage area may store data created based on the use of the tunnel segment inspection system utilizing image features and feature enhancement. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, which can be connected via a network to the tunnel segment inspection system utilizing image features and feature enhancement. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0039] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 4 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 4 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the shield tunnel segment detection method utilizing image features and feature enhancement as described in the above method embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the shield tunnel segment detection system utilizing image features and feature enhancement. The output device 340 may include a display device such as a screen.

[0040] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0041] In one implementation, the above-described electronic device is applied to a tunnel segment inspection system utilizing image features and feature enhancement, for a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Acquire target image samples and preprocess the target image samples to obtain standard target image samples; A shield tunnel segment detection model capable of recognizing image features is constructed. The model includes a backbone feature extraction network, an enhanced feature extraction network, a classifier, and a regressor. The backbone feature extraction network comprises a C2f module and a CONV module. The C2f module fuses the features extracted by the CONV module, where the fusion expression is:

[0042] , , , , , In the formula, The final output feature map, To compress the number of channels using 1x1 convolution, This is the feature map obtained by concatenating the feature maps of the main branch and the secondary branch. To be and The stitched feature map The feature map after processing of the main branch. The feature map is directly passed to the secondary branch. for Processed through n Bottleneck modules for Processed through n Bottleneck modules To divide the feature map into two parts, This is the feature map after being expanded through a 1x1 convolution. To Perform a 1x1 convolution operation. The input feature map; The standard target image sample is input into the shield tunnel segment detection model, and the shield tunnel segment detection model is iteratively trained to obtain the target shield tunnel segment detection model. The image to be detected, containing tunnel segments, is input into the target tunnel segment detection model, and the target tunnel segment detection model outputs the detection result corresponding to the image to be detected.

[0043] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting tunnel lining segments using image features and feature enhancement, characterized in that, include: Acquire target image samples and preprocess the target image samples to obtain standard target image samples; A shield tunnel segment detection model with the ability to recognize image features is constructed. The shield tunnel segment detection model includes a backbone feature extraction network, an enhanced feature extraction network, a classifier, and a regressor. The backbone feature extraction network includes a CONV-C2f module. The standard target image sample is input into the shield tunnel segment detection model, and the shield tunnel segment detection model is iteratively trained to obtain the target shield tunnel segment detection model. The image to be detected, containing tunnel segments, is input into the target tunnel segment detection model, and the target tunnel segment detection model outputs the detection result corresponding to the image to be detected.

2. The shield tunnel segment detection method using image features and feature enhancement according to claim 1, characterized in that, in, The expression for the CONV-C2f module is: ， ， ， ， ， ， In the formula, The final output feature map, To compress the number of channels using 1x1 convolution, This is the feature map obtained by concatenating the feature maps of the main branch and the secondary branch. To be and The stitched feature map The feature map after processing of the main branch. The feature map is directly passed to the secondary branch. for Processed through n Bottleneck modules for Processed through n Bottleneck modules To divide the feature map into two parts, This is the feature map after being expanded through a 1x1 convolution. To Perform a 1x1 convolution operation. The input feature map.

3. The method for detecting tunnel lining segments using image features and feature enhancement as described in claim 1, characterized in that, The backbone feature extraction network also includes a channel attention module, and the expression for the weighting operation via the channel attention module is: ， ， In the formula, This is the feature map after spatial attention weighting. This is the output of the spatial attention module. This indicates element-wise multiplication. This is the feature map after channel attention weighting. This is the output of the channel attention module.

4. The shield tunnel segment detection method using image features and feature enhancement according to claim 3, characterized in that, in, The expression for the output of the computational spatial attention module is: ， The expression for calculating the output of the channel attention module is: ， In the formula, The Sigmoid activation function is used to normalize the output to the range [0,1]. For feature map The average value in the channel dimension. For feature map The maximum value in the channel dimension. This is a global average pooling operation. To achieve dimensionality reduction and dimensionality increase of the features after average pooling through two fully connected layers, This is a global max pooling operation. To achieve dimensionality reduction and dimensionality increase of the features after max pooling operation, two fully connected layers are used.

5. The method for detecting tunnel lining segments using image features and feature enhancement according to claim 1, characterized in that, The bounding box regression loss function in the target shield tunnel segment detection model is: ， In the formula, For loss function, The area of ​​the actual bounding box. To predict the area of ​​the bounding box, To predict the minimum closure region between the bounding box and the true bounding box, To predict the intersection-union ratio between the bounding box and the true bounding box.

6. The method for detecting tunnel lining segments using image features and feature enhancement according to claim 1, characterized in that, The preprocessing of the target image samples to obtain standard target image samples includes: The target image sample is input into a preset image processor, which performs deblurring and brightening operations on the target image sample and outputs a standard target image sample corresponding to the target image sample.

7. A shield tunnel segment inspection system utilizing image features and feature enhancement, characterized in that, include: The acquisition module is configured to acquire target image samples and preprocess the target image samples to obtain standard target image samples; The construction module is configured to build a shield tunnel segment detection model with the ability to recognize image features. The shield tunnel segment detection model includes a backbone feature extraction network, an enhanced feature extraction network, a classifier, and a regressor. The backbone feature extraction network includes a CONV-C2f module. The training module is configured to input the standard target image samples into the shield tunnel segment detection model, and iteratively train the shield tunnel segment detection model to obtain the target shield tunnel segment detection model. The output module is configured to input the image to be detected, which contains shield tunnel segments, into the target shield tunnel segment detection model, and the target shield tunnel segment detection model outputs the detection result corresponding to the image to be detected.

8. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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