Method and system for identifying pores and cracks of asphalt pavement based on CT image, and storage medium

Abstract

The invention relates to an asphalt pavement pore and crack identification method and system based on a CT image, and a storage medium. The method comprises the following steps: obtaining an asphalt pavement two-dimensional image; wherein the two-dimensional image is obtained by scanning a core sample at the crack of the asphalt pavement through CT equipment; processing the two-dimensional image by adopting a three-dimensional reconstruction algorithm, converting two-dimensional image data into three-dimensional volume data, and generating a three-dimensional structure model of the asphalt pavement; extracting three-dimensional volume data in the three-dimensional structure model, and calculating a volume form factor; according to the volume form factor, classifying pores and cracks of the asphalt pavement by using an automatic classification algorithm; and outputting a classification result to realize the recognition of the pores and cracks of the asphalt pavement.

Description

Technical Field

[0001] The present invention relates to the technical fields of image processing, computer vision, and road detection, and particularly relates to a method, system, and storage medium for identifying pores and cracks in asphalt pavements based on CT images. Background Art

[0002] In the field of pavement engineering, accurately analyzing the internal structure of asphalt pavements is crucial for evaluating pavement performance, predicting service life, and formulating maintenance strategies. Pores and cracks are two common defects in the internal structure of asphalt pavements, and they have a significant impact on the mechanical properties and durability of the pavements.

[0003] Traditional detection methods such as visual inspection, ground-penetrating radar (GPR) technology, and ultrasonic detection technology have problems such as low efficiency, strong subjectivity, and insufficient detection accuracy. Especially in identifying microcracks and internal hidden cracks, they cannot achieve accurate identification and positioning, etc. Moreover, most traditional detection methods can only provide two-dimensional information and cannot accurately reflect the spatial distribution of cracks and pores. In recent years, with the development of CT technology, using CT images for non-destructive detection of the internal structure of asphalt pavements has become a technological trend. However, current CT image processing solutions require a large amount of manual analysis and human intervention, making it difficult to achieve complete automatic differentiation between pores and cracks, and having high professional requirements for operators. Therefore, how to achieve automatic and high-precision differentiation between pores and cracks in CT image processing solutions and realize the identification of pores and cracks in the asphalt pavement has become an urgent problem to be solved.

[0004] This application aims to establish a method, system, and storage medium for identifying pores and cracks in asphalt pavements based on CT images to solve the above problems. Summary of the Invention

[0005] To achieve the above objects and other advantages of the present invention, the first object of the present invention is to provide a method for identifying pores and cracks in asphalt pavements based on CT images, including the following steps:

[0006] Obtain a two-dimensional image of the asphalt pavement; wherein, the two-dimensional image is obtained by scanning a core sample at the crack of the asphalt pavement with a CT device;

[0007] Process the two-dimensional image using a three-dimensional reconstruction algorithm, convert the two-dimensional image data into three-dimensional volume data, and generate a three-dimensional structure model of the asphalt pavement;

[0008] Extract the three-dimensional volume data from the three-dimensional structure model and calculate the volume shape factor;

[0009] Classify the pores and cracks in the asphalt pavement using an automatic classification algorithm according to the volume shape factor;

[0010] Output the classification results to achieve the identification of the pores and cracks in the asphalt pavement. Further, the following steps are also included:

[0011] Convert the three-dimensional data into a visual image to generate a three-dimensional visualization model.

[0012] Further, the three-dimensional reconstruction algorithm adopts the Feldkamp cone-beam reconstruction algorithm to convert the two-dimensional data in the two-dimensional image into three-dimensional volume data.

[0013] Further, the three-dimensional volume data includes characteristic data such as the morphological profiles, positions, quantities, surface areas, volumes, equivalent diameters, etc. of the pores and cracks in the asphalt pavement.

[0014] Further, the top-hat transformation method is used to extract the three-dimensional volume data in the three-dimensional structure model.

[0015] Further, the formula for the positive morphological factor is as follows:

[0016]

[0017] Among them, the Area represents the surface area of the pores and cracks in the three-dimensional data; d represents the equivalent diameter of the pores and cracks in the three-dimensional data; Volume represents the volume of the pores and cracks in the three-dimensional data.

[0018] Further, according to the volume morphological factor, use an automatic classification algorithm to classify the pores and cracks, including the following steps:

[0019] Judge whether the volume shape factor is greater than a preset threshold factor parameter;

[0020] If the volume shape factor is greater than the preset threshold factor parameter, the classification result is a pore;

[0021] If the volume shape factor is less than or equal to the preset threshold factor parameter, the classification result is a crack.

[0022] The second object of the present invention is to provide an identification system for pores and cracks in an asphalt pavement based on CT images, including the following modules:

[0023] A two-dimensional image acquisition module, configured to acquire a two-dimensional image of the asphalt pavement; wherein, the two-dimensional image is obtained by scanning a core sample at the crack of the asphalt pavement with a CT device;

[0024] A three-dimensional structure model generation module, configured to use a three-dimensional reconstruction algorithm to process the two-dimensional image, convert the two-dimensional image data into three-dimensional volume data, and generate the three-dimensional structure model of the asphalt pavement;

[0025] A volume shape factor calculation module, configured to extract three-dimensional volume data from the three-dimensional structure model and calculate the volume shape factor;

[0026] A classification and recognition module, configured to classify the pores and cracks of the asphalt pavement according to the volume shape factor by using an automatic classification algorithm, output a classification result, and realize the recognition of the pores and cracks of the asphalt pavement.

[0027] Further, the following modules are further included:

[0028] A three-dimensional visualization module, configured to convert the three-dimensional volume data into a visual image and generate a three-dimensional visualization model.

[0029] The third object of the present invention is to provide a readable storage medium, on which a computer program is stored, characterized in that when the computer program is executed by a processor, a method for identifying pores and cracks in an asphalt pavement based on CT images is realized.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] The present invention relates to a method, a system and a storage medium for identifying pores and cracks in an asphalt pavement based on CT images. By using a CT device to obtain high-precision two-dimensional images of the asphalt pavement and using a three-dimensional reconstruction algorithm to convert the two-dimensional images into a three-dimensional structure model, the internal structural characteristics of the asphalt pavement can be intuitively displayed. By extracting the three-dimensional volume data of the pores and cracks in the three-dimensional structure model and calculating the volume shape factor, the method realizes an accurate quantitative description of the pores and cracks, and uses an automatic classification algorithm to classify the pores and cracks inside the asphalt pavement according to the volume shape factor, output a classification result, and realize the recognition of the pores and cracks in the asphalt pavement, thereby effectively distinguishing the two and improving the accuracy and efficiency of analysis.

[0032] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it according to the content of the specification, the following takes the preferred embodiments of the present invention and describes them in detail in conjunction with the drawings. The specific implementation manners of the present invention are given in detail by the following embodiments and their drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and the schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0034] Figure 1Flow chart of the method for identifying pores and cracks in asphalt pavement based on CT images of this application Figure 1 ;

[0035] Figure 2 Flow chart of the method for identifying pores and cracks in asphalt pavement based on CT images described in Embodiment 1 Figure 2 ;

[0036] Figure 3 Flow chart of the Feldkamp cone beam reconstruction algorithm described in Embodiment 1

[0037] Figure 4 Schematic diagram of three-dimensional reconstruction of the two-dimensional image in Embodiment 1

[0038] Figure 5 Schematic diagram of the perspective of pores and cracks in the three-dimensional model in Embodiment 1

[0039] Figure 6 Flow chart of classifying pores and cracks in the asphalt pavement using an automatic classification algorithm according to the volume shape factor described in Embodiment 1

[0040] Figure 7 Schematic diagram of the system for identifying pores and cracks in asphalt pavement based on CT images in Embodiment 2

[0041] Figure 8 Schematic diagram of the computer-readable storage medium in Embodiment 3 Detailed implementation manners

[0042] Next, in combination with the accompanying drawings and specific implementation manners, the present invention will be further described. It should be noted that, on the premise of no conflict, any combination of the following-described embodiments or technical features can form a new embodiment.

[0043] In the subsequent description, suffixes such as "module", "component", or "unit" used to represent elements are only for the convenience of description of the present invention, and they have no specific meaning in themselves. Therefore, "module", "component", or "unit" can be used interchangeably.

[0044] Embodiment 1

[0045] The present invention provides a method for identifying pores and cracks in asphalt pavement based on CT images, as Figure 1 shown, which specifically includes the following steps:

[0046] S101, Obtain a two-dimensional image of the asphalt pavement; wherein, the two-dimensional image is obtained by scanning a core sample at the crack of the asphalt pavement using a CT device;

[0047] S102. Process the two-dimensional image using a three-dimensional reconstruction algorithm, convert the two-dimensional image data into three-dimensional volume data, and generate a three-dimensional structural model of the asphalt pavement;

[0048] S103. Extract the three-dimensional volume data from the three-dimensional structural model and calculate the volume shape factor;

[0049] S104. Classify the pores and cracks of the asphalt pavement according to the volume shape factor using an automatic classification algorithm;

[0050] S105. Output the classification result to realize the identification of the pores and cracks of the asphalt pavement.

[0051] In some embodiments, as Figure 2 shown, the following steps are further included:

[0052] S106. Convert the three-dimensional data into a visual image to generate a three-dimensional visualization model.

[0053] In some embodiments, in step S101, the two-dimensional image is scanned by a high-precision CT scanning device on the core sample at the crack of the asphalt pavement to generate two-dimensional images of different layers with a high spatial resolution (50μm - 60μm) to obtain the original image data of its internal structure, so as to ensure the effective capture of internal defects such as microcracks, fine pores, and cracks inside the asphalt pavement.

[0054] In a preferred embodiment, the CT device can be an industrial high-resolution X-ray computed tomography scanner. In specific applications, appropriate scanning parameters can be set according to the material and structural characteristics of the asphalt pavement, such as the energy, current, and scanning speed of the X-ray, to ensure the acquisition of high-quality two-dimensional image data and provide a reliable basis for subsequent three-dimensional reconstruction and three-dimensional data analysis.

[0055] Exemplarily, when the CT scanning device is started, first, multiple samples with typical cracks and pores are selected from the asphalt pavement according to the requirements of the CT scanning device. Generally, they are cylindrical specimens with a diameter of 100mm to 150mm, ensuring that the samples can represent the road surface state and cover different types of cracks (such as surface microcracks, reflective cracks, etc.). Then, the samples are preprocessed to remove surface impurities and moisture to ensure that they are not affected by external interference during CT scanning. The samples are placed in the industrial CT scanner to ensure that the scanning angle covers the entire sample, and an appropriate resolution (generally selected from 50μm to 60μm) is set to ensure high-precision image acquisition. After the scanning data is generated, it is saved as an original CT image file (such as TIFF format) and denoised. Gaussian filtering is used to reduce image noise to ensure the data quality during subsequent analysis.

[0056] In some embodiments, in step S102, the three-dimensional reconstruction algorithm uses the Feldkamp cone-beam reconstruction algorithm to process the data of the two-dimensional image in step S101, and convert the two-dimensional data in the two-dimensional image into three-dimensional volume data.

[0057] In a preferred embodiment, the Feldkamp cone-beam reconstruction algorithm is as Figure 3 shown, and the steps are as follows:

[0058] S1021. Obtain the two-dimensional image data of the core sample at the crack of the asphalt pavement at different projection angles;

[0059] S1022. Preprocess the obtained two-dimensional image data to improve the data quality;

[0060] S1023. Apply the Feldkamp cone-beam reconstruction algorithm to synthesize the preprocessed two-dimensional image data into three-dimensional volume data;

[0061] S1024. Perform post-processing operations such as slicing and rendering on the three-dimensional volume data to generate a three-dimensional structure model.

[0062] In a preferred embodiment, the present application performs three-dimensional reconstruction on the two-dimensional image, as Figure 4 shown. Specifically, it should be understood that in Figure 4 , Figure (a) shows the two-dimensional image of the original asphalt pavement, Figure (b) shows the three-dimensional volume data extracted after top-hat transformation, Figure (c) shows the image obtained by rendering the three-dimensional volume data, and Figure (d) shows the three-dimensional structure model of the asphalt pavement.

[0063] In some preferred embodiments, the perspective views of pores and fissures in the three-dimensional model of the asphalt pavement generated by the present application are as Figure 5 shown. Specifically, it should be understood that in Figure (5), Figure (e) shows perspective view 1 of pores and fissures mixed, Figure (f) shows perspective view 1 of the classified and identified fissures, Figure (g) shows perspective view 2 of pores and fissures mixed, Figure (h) shows perspective view 2 of the classified and identified fissures, Figure (i) shows perspective view 3 of pores and fissures mixed, and Figure (j) shows perspective view 3 of the classified and identified fissures.

[0064] The present application converts two-dimensional data into three-dimensional volume data through the Feldkamp cone-beam reconstruction algorithm, and then generates a three-dimensional model with high spatial resolution, realizing the effective capture and identification of internal defects such as fissures and pores at the cracks of the asphalt pavement.

[0065] In some embodiments, the three-dimensional volume data described in step S103 is extracted by the top-hat transformation method. Through the top-hat transformation method, the morphological contours, positions, quantities, surface areas, volumes, equivalent diameters, etc. of the pores and fissures in the asphalt pavement can be effectively extracted, and the problem of uneven illumination in the image can be corrected and improved, enhancing the details in the image, which plays an important role in detecting and analyzing the pores and fissures in the image.

[0066] In some embodiments, the three-dimensional volume data described in step S103 includes the morphological contours, positions, quantities, surface areas, volumes, equivalent diameters, etc. of the pores and fissures in the asphalt pavement.

[0067] In some embodiments, the formula for calculating the morphological factor in step S104 is as follows:

[0068]

[0069] Wherein, the Area represents the surface area of the pores and fissures in the three-dimensional data; d represents the equivalent diameter of the pores and fissures in the three-dimensional data; and Volume represents the volume of the pores and fissures in the three-dimensional data.

[0070] In some embodiments, in step S104, according to the volume morphological factor, the pores and fissures in the asphalt pavement are classified by using an automatic classification algorithm, as Figure 6 shown, including the following steps:

[0071] S1041, determine whether the volume shape factor is greater than a preset threshold factor parameter;

[0072] S1042a, if the volume shape factor is greater than the preset threshold factor parameter, the classification result is a fissure;

[0073] S1042b, if the volume shape factor is less than or equal to the preset threshold factor parameter, the classification result is a pore.

[0074] Specifically, it should be understood that the setting of the threshold factor parameter usually depends on various factors, such as the amount of image acquisition, the type of asphalt mixture, the actual morphology of pores and fissures, etc. In actual applications, cross-validation or machine learning algorithms can be used to set the threshold factor parameter, so as to classify with different thresholds. This application does not make any restrictions on this.

[0075] In a preferred embodiment, the present application uses an adaptive threshold method to set the preset threshold factor parameter, which can dynamically adjust the threshold according to the local characteristics of the acquired three-dimensional image data, so as to adapt to pores and fissures in different regions and different morphologies.

[0076] In another preferred embodiment, the present application sets the threshold factor parameter to 500, and a structure with a volume shape factor greater than 500 is determined as a crack, automatically classifying the structural units into pores or cracks.

[0077] Specifically, the present application distinguishes pores (usually presented as closed structures close to spherical shape and with a small volume shape factor) from cracks (usually slender or flat connectivity features and with a large volume shape factor). By analyzing the difference in their volume shape factors, it is found that pores usually have a lower volume shape factor value, while cracks show a higher volume shape factor. This difference enables the effective distinction between the two in the three-dimensional model, contributing to more accurate automatic classification.

[0078] In a preferred embodiment, in step S106, converting the three-dimensional data into a visual image to generate a three-dimensional visualization model, using volume rendering technology to convert the three-dimensional data into a visual image to display the distribution of pores and cracks in the asphalt pavement. Through the three-dimensional visualization model, analysts can intuitively observe the morphology, position, and quantity of cracks and pores, and view, confirm, and analyze the results accordingly.

[0079] The present application obtains two-dimensional images of the core samples at the crack locations of the asphalt pavement through a high-precision CT scanning device, ensuring high spatial resolution of the image data, thereby effectively capturing internal defects such as microcracks, small pores, and cracks inside the asphalt pavement. Using a three-dimensional reconstruction algorithm, the two-dimensional image data is converted into three-dimensional volume data, and a three-dimensional structural model of the asphalt pavement is generated, realizing the three-dimensional visualization of the internal structure of the asphalt pavement. By extracting the three-dimensional volume data from the three-dimensional structural model and calculating the volume shape factor, this method can accurately describe the morphological contours, positions, quantities, surface areas, volumes, equivalent diameters, etc. of pores and cracks. Using an automatic classification algorithm, pores and cracks are classified according to the volume shape factor, achieving effective distinction between pores and cracks, and avoiding the subjectivity and errors of manual classification. In addition, this method also adopts an adaptive threshold method to set the preset threshold factor parameter, which can dynamically adjust the threshold according to the local characteristics of the acquired three-dimensional image data, improving the accuracy and adaptability of classification. Finally, converting the three-dimensional data into a visual image to generate a three-dimensional visualization model enables analysts to intuitively observe the morphology, position, and quantity of cracks and pores, providing a reliable basis for subsequent quality assessment and maintenance of the asphalt pavement.

[0080] Embodiment 2

[0081] The present invention provides a recognition system for pores and cracks in an asphalt pavement based on CT images, as Figure 7 shown, including the following modules:

[0082] A two-dimensional image acquisition module, configured to acquire a two-dimensional image of an asphalt pavement; wherein, the two-dimensional image is acquired by scanning a core sample at a crack of the asphalt pavement with a CT device;

[0083] A three-dimensional structure model generation module, configured to process the two-dimensional image by using a three-dimensional reconstruction algorithm, convert the two-dimensional image data into three-dimensional volume data, and generate a three-dimensional structure model of the asphalt pavement;

[0084] A volume shape factor calculation module, configured to extract the three-dimensional volume data in the three-dimensional structure model and calculate the volume shape factor;

[0085] A classification and recognition module, configured to classify the pores and cracks of the asphalt pavement by using an automatic classification algorithm according to the volume shape factor; output a classification result to realize the recognition of the pores and cracks of the asphalt pavement. In some embodiments, the system further includes the following modules:

[0086] A three-dimensional visualization module, configured to convert the three-dimensional volume data into a visual image and generate a three-dimensional visualization model. Through the two-dimensional image acquisition module of the present application, a two-dimensional image of a core sample at a crack of an asphalt pavement can be efficiently acquired, providing a basis for subsequent three-dimensional reconstruction and data analysis. The three-dimensional structure model generation module uses an advanced three-dimensional reconstruction algorithm to convert two-dimensional image data into three-dimensional volume data and generate an accurate three-dimensional structure model, enabling analysts to intuitively understand the internal structure of the asphalt pavement. The volume shape factor calculation module can accurately extract key data in the three-dimensional structure model and calculate the volume shape factor, which comprehensively describes the morphological profiles, positions, quantities, surface areas, volumes, equivalent diameters, etc. of the pores and cracks, providing an important basis for subsequent classification and analysis. The classification module uses an automatic classification algorithm to accurately classify the pores and cracks according to the volume shape factor, effectively distinguishing the pores and cracks and improving the accuracy and efficiency of data analysis. In addition, the three-dimensional visualization module converts the three-dimensional data into a visual image and generates an intuitive three-dimensional visualization model, enabling analysts to more conveniently observe and analyze the distribution of pores and cracks in the asphalt pavement, providing strong support for the quality inspection and maintenance of the asphalt pavement.

[0087] Embodiment 3

[0088] The embodiment of the present invention also provides a computer-readable storage medium, as Figure 8 shown, on which program instructions are stored, and when the program instructions are executed, the method for identifying pores and cracks in an asphalt pavement based on CT images recorded in Embodiment 1 above is implemented.

[0089] Among them, the program instructions are stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including a number of computer program instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present application.

[0090] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and the illustrated and described examples here.

[0091] The devices, electronic devices, non-volatile computer storage media provided by the embodiments of this specification are corresponding to the methods. Therefore, the devices, electronic devices, and non-volatile computer storage media also have beneficial technical effects similar to those of the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding devices, electronic devices, and non-volatile computer storage media will not be repeated here.

[0092] Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, the method steps can be logically programmed to enable the controller to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, this kind of controller can be regarded as a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0093] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0094] For convenience of description, when describing the above device, various units are described separately according to their functions. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0095] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects.

[0096] This specification is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more flows and / or Figure 1 blocks or multiple blocks.

[0097] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more flows and / or Figure 1 blocks or multiple blocks.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more flows and / or Figure 1 blocks or multiple blocks.

[0099] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0100] The memory may include non-permanent memory in the form of computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0101] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0102] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0103] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can refer to the description of the method embodiments.

[0104] The above description is only for the embodiments of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of one or more embodiments of this specification.

Claims

1. A method for identifying pores and cracks in asphalt pavement based on CT images, characterized in that, Specifically, it includes the following steps: Obtain a two-dimensional image of the asphalt pavement; wherein, the two-dimensional image is obtained by scanning a core sample at the crack of the asphalt pavement with a CT device; Process the two-dimensional image using a three-dimensional reconstruction algorithm, convert the two-dimensional image data into three-dimensional volume data, and generate a three-dimensional structure model of the asphalt pavement; Extract the three-dimensional volume data in the three-dimensional structure model and calculate the volume shape factor; Classify the pores and fissures of the asphalt pavement using an automatic classification algorithm based on the volume shape factor; Output the classification result to achieve the identification of the pores and fissures of the asphalt pavement.

2. The method for identifying pores and cracks in asphalt pavement based on CT images according to claim 1, wherein It further includes the following steps: Convert the three-dimensional data into a visual image to generate a three-dimensional visualization model.

3. The method for identifying pores and cracks in asphalt pavement based on CT images according to claim 1, characterized in that, The three-dimensional reconstruction algorithm uses the Feldkamp cone-beam reconstruction algorithm to convert the two-dimensional data in the two-dimensional image into three-dimensional volume data.

4. The method for identifying pores and cracks in asphalt pavement based on CT images according to claim 1, characterized in that, Use the top-hat transformation method to extract the three-dimensional volume data in the three-dimensional structure model.

5. The method for identifying pores and cracks in asphalt pavement based on CT images according to claim 4, characterized in that, The three-dimensional volume data includes characteristic data such as the morphological contours, positions, quantities, surface areas, volumes, equivalent diameters, etc. of the pores and fissures of the asphalt pavement.

6. The method for identifying pores and cracks in asphalt pavement based on CT images according to claim 1, characterized in that The formula for calculating the shape factor is as follows: Wherein, the Area represents the surface area of the pores and fissures in the three-dimensional data; d represents the equivalent diameter of the pores and fissures in the three-dimensional data; Volume represents the volume of the pores and fissures in the three-dimensional data.

7. The method for identifying pores and cracks in asphalt pavement based on CT images according to claim 1, wherein, Classify the pores and fissures of the asphalt pavement using an automatic classification algorithm based on the volume shape factor, including the following steps: Judge whether the volume shape factor is greater than a preset threshold factor parameter; If the volume shape factor is greater than the preset threshold factor parameter, the classification result is pores; If the volume shape factor is less than or equal to the preset threshold factor parameter, the classification result is fissures.

8. An identification system for pores and cracks in asphalt pavement based on CT images, characterized in that, It includes the following modules: A two-dimensional image acquisition module, configured to obtain a two-dimensional image of the asphalt pavement; wherein, the two-dimensional image is obtained by scanning a core sample at the crack of the asphalt pavement with a CT device; A three-dimensional structure model generation module, configured to process the two-dimensional image using a three-dimensional reconstruction algorithm, convert the two-dimensional image data into three-dimensional volume data, and generate a three-dimensional structure model of the asphalt pavement; A volume shape factor calculation module, configured to extract the three-dimensional volume data in the three-dimensional structure model and calculate the volume shape factor; A classification and identification module, configured to classify the pores and fissures of the asphalt pavement using an automatic classification algorithm based on the volume shape factor, output the classification result, and achieve the identification of the pores and fissures of the asphalt pavement.

9. The identification system for pores and cracks of asphalt pavement based on CT images according to claim 8, characterized in that, It further includes the following modules: A three-dimensional visualization module, configured to convert the three-dimensional volume data into a visual image to generate a three-dimensional visualization model.

10. A readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to perform the method for identifying pores and fissures of an asphalt pavement based on CT images according to any one of claims 1-7.

Images