Asphalt pavement crack classification method and device, electronic equipment and storage medium

Abstract

The invention provides an asphalt pavement crack classification method and device, electronic equipment and a storage medium. The method comprises the following steps: firstly, obtaining a to-be-processed target asphalt pavement image; dividing the target asphalt pavement image into a plurality of sub-image blocks with the same size; identifying a crack in the target asphalt pavement image, and determining a target sub-image block comprising the crack from the plurality of sub-image blocks; and finally, performing crack classification on the target asphalt pavement image according to the target sub-image blocks, and by performing division of multiple sub-image blocks on the target asphalt pavement image, the calculation complexity in the crack identification process is reduced, the crack identification efficiency and accuracy are improved, and the crack identification accuracy is improved. And the efficiency and the accuracy of automatically classifying the cracks and the blocky cracks in actual engineering are improved.

Description

Technical Field

[0001] The present application relates to the technical field of asphalt pavement crack classification, and in particular to an asphalt pavement crack classification method, device, electronic equipment and storage medium. Background Art

[0002] Cracks are one of the most important types of diseases in asphalt pavements. They are caused by the repeated effects of vehicle loads and temperature stresses. They are the main form of early damage to pavements and almost accompany the entire life of asphalt pavements. Research and analysis on the causes and development of cracks with different characteristics, timely and effective crack treatment, and the adaptability of each crack to caulking are all very important to prevent their further development and ensure the quality and structural stability of the pavement. These are all based on crack classification. Therefore, quantitative identification and classification of cracks in each section is an important condition for maintenance performance research and an effective basis for the selection of maintenance sections, which is of great significance to improving pavement performance. In the current related technologies, the description of the basis for crack classification is still qualitative. These schemes mainly adopt manual detection, which has technical problems such as strong subjectivity, low efficiency, and low accuracy. Summary of the invention

[0003] In view of this, the purpose of the present application is to propose a method, device, electronic device and storage medium for classifying cracks in asphalt pavement to solve or partially solve the above-mentioned technical problems.

[0004] Based on the above purpose, the present application provides a method for classifying cracks in asphalt pavement, comprising:

[0005] Acquire a target asphalt road surface image to be processed;

[0006] Dividing the target asphalt road surface image into a plurality of sub-image blocks of the same size;

[0007] Identifying cracks in the target asphalt pavement image, and determining a target sub-image block including the cracks from the plurality of sub-image blocks;

[0008] Crack classification is performed on the target asphalt pavement image based on the target sub-image block.

[0009] Based on the same concept, the present application also proposes an asphalt pavement crack classification device, comprising:

[0010] An acquisition module is used to acquire a target asphalt road surface image to be processed;

[0011] A division module, dividing the target asphalt road surface image into a plurality of sub-image blocks of the same size;

[0012] an identification module, for identifying cracks in the target asphalt pavement image, and determining a target sub-image block including the cracks from the plurality of sub-image blocks;

[0013] A classification module is used to classify cracks in the target asphalt pavement image based on the target sub-image block.

[0014] Based on the same concept, the present application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the asphalt pavement crack classification method as described above when executing the program.

[0015] Based on the same concept, the present application also proposes a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the asphalt pavement crack classification method as described above.

[0016] Based on the same concept, the present application also proposes a computer program product, which includes a computer program. The computer program is executed by one or more processors so that the processors execute the asphalt pavement crack classification method as described above.

[0017] As can be seen from the above, the asphalt pavement crack classification method, device, electronic device and storage medium provided by the present application first obtain the target asphalt pavement image to be processed; then divide the target asphalt pavement image into multiple sub-image blocks of the same size; then identify the cracks in the target asphalt pavement image, and determine the target sub-image block including the cracks from the multiple sub-image blocks; finally, classify the cracks of the target asphalt pavement image according to the target sub-image block. By dividing the target asphalt pavement image into multiple sub-image blocks, the computational complexity of the crack recognition process is reduced, the efficiency and accuracy of crack recognition are improved, and the efficiency and accuracy of automatic classification of cracks and block cracks in actual engineering are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic diagram of an application scenario of an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a process of classifying cracks in an asphalt pavement according to an embodiment of the present application;

[0021] Figure 3A schematic diagram of an asphalt road surface image according to an embodiment of the present application;

[0022] Figure 4 A schematic diagram of another asphalt road surface image according to an embodiment of the present application;

[0023] Figure 5 This is a schematic diagram of the structure of an asphalt pavement crack classification device according to an embodiment of the present application;

[0024] Figure 6 A schematic diagram of a specific electronic device hardware structure of an embodiment of the present application. DETAILED DESCRIPTION

[0025] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0026] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with general skills in the field to which the present application belongs. The "first", "second" and similar words used in the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connecting" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, which can be direct connections, indirect connections, wired connections, and wireless connections. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In addition, in the description of the present application, unless otherwise specified, the term "multiple" refers to two or more. The term "and / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0027] The principles and spirit of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present application, and are not intended to limit the scope of the present application in any way. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0028] It is understandable that before using the technical solutions of each embodiment of the present application, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0029] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to the electronic device, application, server, storage medium or other software or hardware that performs the operation of the technical solution of the present application according to the prompt message.

[0030] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0031] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of this application. Other methods that meet relevant laws and regulations may also be applied to the implementation method of this application.

[0032] It should be understood herein that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0033] As mentioned in the background technology section, in the current related technologies, the description of the basis for crack classification is still qualitative. These solutions mainly adopt manual detection, which has technical problems such as strong subjectivity, low efficiency, and low accuracy. For example, in a certain related technology, the main crack size of more than 0.5m is defined as block cracks, and those not larger than 0.5m are defined as tortoise cracks; due to the lack of a clear mathematical definition of block size, the application of algorithms for automatically determining block size is restricted, affecting the efficiency and accuracy of automatic classification of tortoise cracks and block cracks in actual engineering.

[0034] In summary, in order to solve the above problems, the present application embodiment proposes a method for classifying cracks in an asphalt pavement, including:

[0035] First, a target asphalt pavement image to be processed is obtained; then the target asphalt pavement image is divided into a plurality of sub-image blocks of the same size; then cracks in the target asphalt pavement image are identified, and a target sub-image block including cracks is determined from the plurality of sub-image blocks; finally, crack classification is performed on the target asphalt pavement image according to the target sub-image blocks. By dividing the target asphalt pavement image into a plurality of sub-image blocks, the computational complexity in the crack recognition process is reduced, the efficiency and accuracy of crack recognition are improved, and the efficiency and accuracy of automatic classification of cracks and block cracks in actual engineering are improved.

[0036] In some specific application scenarios, the asphalt pavement crack classification method of the present application can be applied to various platforms or systems involving asphalt pavement crack classification. Optionally, in some embodiments, the present application is mainly used for the classification of mesh cracks in asphalt pavements. As an example, the application scenario includes at least one server and at least one terminal. Figure 1 , which is a schematic diagram of an application scenario of an asphalt pavement crack classification method provided by an exemplary embodiment of the present disclosure. The application scenario includes a terminal device 101, a server 102, and a data storage system 103. Among them, the terminal device 101, the server 102 and the data storage system 103 can be connected through a wired or wireless communication network. The terminal device includes but is not limited to a desktop computer, a mobile phone, a mobile computer, a tablet computer, a media player, a smart wearable device, a personal digital assistant (PDA) or other electronic devices capable of realizing the above functions. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server and the terminal can communicate through the network to realize data transmission. Among them, the network can be a wired network or a wireless network, which is not specifically limited in this application.

[0037] The server may be a server that provides various services. Specifically, the server may be used to provide background services for applications running on the terminal. Optionally, in some implementations, the asphalt pavement crack classification method provided in the embodiment of the present application may be executed by the server. Optionally, in some implementations, the asphalt pavement crack classification method provided in the embodiment of the present application may be directly executed by the terminal device.

[0038] Optionally, the server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, software or software modules for providing distributed services), or it can be implemented as a single software or software module. This embodiment of the application does not specifically limit this.

[0039] Optionally, the wireless network or wired network uses standard communication technology and / or protocol. The network is usually the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a dedicated network or any combination of a virtual private network). In some embodiments, the data exchanged through the network is represented by technologies and / or formats including hypertext mark-up language (HTML), extensible markup language (XML), etc. In addition, conventional encryption technologies such as secure socket layer (SSL), transport layer security (TLS), virtual private network (VPN), and Internet protocol security (IPsec) can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above data communication technologies.

[0040] The data storage system stores a large amount of data. Optionally, the data storage system can partition the data according to different uses and data types. The source of the data includes but is not limited to an existing database, data crawled from the Internet, or data uploaded by the user when using the client.

[0041] The following describes the asphalt pavement crack classification method according to the exemplary embodiment of the present application in conjunction with a specific application scenario. It should be noted that the above application scenario is only shown to facilitate understanding of the spirit and principle of the present application, and the implementation of the present application is not limited in this regard. On the contrary, the implementation of the present application can be applied to any applicable scenario.

[0042] refer to Figure 2, is a flow chart of a method for classifying cracks in an asphalt pavement according to an embodiment of the present application, the method comprising the following steps:

[0043] S101, obtaining a target asphalt road surface image to be processed.

[0044] In specific implementation, the target asphalt road surface image can be acquired by acquisition equipment such as a camera. Optionally, the resolution of the acquired road surface image can be determined by a preset value set by the acquisition equipment, or the resolution of the acquired road surface image can be specified as needed, which is not limited.

[0045] S102: Divide the target asphalt road surface image into a plurality of sub-image blocks of the same size.

[0046] In specific implementation, the crack shape is complex and changeable. In order to reduce the computational cost of contour extraction, in this embodiment, the target asphalt pavement image is divided into sub-blocks. As long as the sub-block coverage contains crack pixels, it is a crack block, otherwise it is a background image, that is, the target asphalt pavement image is divided into multiple sub-image blocks of the same size. Optionally, the size of the sub-image block can be set as needed, and there is no limitation on this. For example, the size of the sub-image block can be set to 100 pixels × 100 pixels. Figure 3 , a schematic diagram of an asphalt pavement image of an embodiment of the present application, the pixel value of the asphalt pavement image is 700 pixels×500 pixels, the pixel value of the preset sub-image block is 100 pixels×100 pixels, and the asphalt pavement image is divided into 35 sub-image blocks.

[0047] In order to accurately divide the target asphalt road surface image into sub-blocks, in some embodiments, the target asphalt road surface image is divided into multiple sub-image blocks of the same size, specifically including:

[0048] Acquire a first pixel value of the target asphalt road surface image;

[0049] Determine a second pixel value of a preset sub-image block;

[0050] Performing pixel filling on the target asphalt road surface image based on the first pixel value and the second pixel value, so that the pixel value of the target asphalt road surface image after filling can be divided by the second pixel value;

[0051] The filled target asphalt road surface image is divided into a plurality of sub-image blocks of the same size based on the first pixel value; wherein the pixel value of each sub-image block is the first pixel value.

[0052] In specific implementation, the preset sub-image block determines the size of each sub-image block after division. In order to better divide the target asphalt pavement image into multiple sub-image blocks and ensure that each part of the target asphalt pavement image can be divided, after obtaining the first pixel value of the target asphalt pavement image, the target asphalt pavement image can be pixel-filled based on the first pixel value and the second pixel value, so that the pixel value of the filled target asphalt pavement image can be divided by the second pixel value. For example, the first pixel value of a target asphalt pavement image is 3712 pixels × 1941 pixels, and the second pixel value of the preset sub-image block is 100 pixels × 100 pixels, then the first pixel value can be filled to 3800 pixels × 2000 pixels. Optionally, the color value of the filled pixel can be set as needed, and there is no limitation on this. For example, the grayscale value of the filled pixel can be 255.

[0053] It should be noted that, in some embodiments, the target asphalt road surface image may be directly divided into a plurality of sub-image blocks of the same size without performing pixel filling processing on the target asphalt road surface image, and this is not limited.

[0054] In some embodiments, determining the second pixel value of the preset sub-image block specifically includes:

[0055] A second pixel value of the preset sub-image block is determined based on the first pixel value and a preset division number.

[0056] In specific implementation, considering the different calculation precisions of different asphalt road images, and in order to ensure the calculation efficiency, the second pixel value of the preset sub-image block can be determined according to the first pixel value of the target asphalt road image and the preset number of divisions. That is, the larger the pixel value corresponding to the target asphalt road image is, the larger the second pixel value of the preset sub-image block can be.

[0057] In some embodiments, in order to better distinguish the target sub-image block including cracks from the multiple sub-image blocks, the target asphalt pavement image may be binarized to obtain a binary matrix, and 0 and 1 are used to represent cracks and background, respectively.

[0058] S103, identifying cracks in the target asphalt pavement image, and determining a target sub-image block including the cracks from the multiple sub-image blocks.

[0059] In a specific implementation, after the target asphalt pavement image is divided into a plurality of sub-image blocks of the same size, it is necessary to further identify cracks in the target asphalt pavement image, and determine a target sub-image block including cracks from the plurality of sub-image blocks. It should be noted that cracks in the target asphalt pavement image can be identified by a preset algorithm or by manual annotation. Figure 3 , where the shaded sub-image blocks represent target sub-image blocks identified as including cracks.

[0060] In order to accurately identify cracks, in some embodiments, identifying cracks in the target asphalt pavement image specifically includes:

[0061] The crack recognition model obtained through training is used to identify cracks in the target asphalt pavement image.

[0062] During specific implementation, the process of training the crack recognition model may refer to the recognition process of the neural network model in the related art, and is not limited thereto.

[0063] In some embodiments, reference Figure 4 After the target asphalt pavement image is binarized, the matrix of the target asphalt pavement image before filling is A 20×38 , we can use the binary fill holes function in opencv to fill the holes (i.e. crack areas) in the binary image matrix. The hole elements 0 in the matrix are changed to 1, and the filled binary matrix B is obtained. 20×38 , subtract the matrix before filling from the matrix after filling to get the matrix C 20×38 , we can get the element information of the hole in the matrix; each hole is a separate connected area, and we can further use the connectedComponents function in opencv to assign a unique label to each hole area in the binary image.

[0064] S104: Classify cracks in the target asphalt pavement image based on the target sub-image block.

[0065] In specific implementation, after determining the target sub-image block including cracks, the target asphalt pavement image can be classified into cracks according to the target sub-image block. Optionally, when classifying the cracks in the target asphalt pavement image, appropriate rules can be set according to the number and size of the target sub-image blocks to classify the cracks in the target asphalt pavement image. For example, the ratio of the number of target sub-image blocks to the total number of multiple sub-image blocks into which the target asphalt pavement image is divided can be used to determine whether the crack type of the target asphalt pavement image is a large-area crack. Alternatively, the size of the closed area formed by multiple target sub-image blocks connected together can be used to determine whether the current crack type is a tortoise crack or a block crack.

[0066] In some embodiments, crack classification of the target asphalt pavement image based on the target sub-image block specifically includes:

[0067] Determine one or more crack regions based on the target sub-image block; wherein the crack region is a closed region composed of a plurality of continuous target sub-image blocks;

[0068] For each crack region, determining a first maximum size of the crack region along a first direction, and determining a second maximum size of the crack region along a second direction; determining a target maximum size that is the largest of the first maximum size and the second maximum size; wherein the first direction is perpendicular to the second direction;

[0069] The target asphalt pavement image is subjected to crack classification based on the target maximum size corresponding to each crack region.

[0070] In specific implementation, considering the degree of damage caused by cracks to the asphalt pavement, a closed area composed of multiple target sub-image blocks connected together, i.e., a crack area, can be first identified, and then the maximum extension size of the crack area (i.e., the target maximum size) can be determined, and then the crack type of the current crack area can be determined according to the target maximum size. Optionally, different threshold ranges can be set, and then the crack type of the crack area can be determined by proportionally comparing the target maximum size with the different threshold ranges. Optionally, after determining the crack type of each crack area included in the target asphalt pavement image, the crack type of each crack area can be directly used as the crack type of the target asphalt pavement image. For example, if a target asphalt pavement image includes 2 block cracks and 1 crack, the result of crack classification of the target asphalt pavement image can be that the target asphalt pavement image includes block cracks and cracks. Optionally, in some embodiments, the ratio of the area of ​​each crack region to the total area of ​​the target asphalt pavement image can also be counted. When the ratio of the area of ​​a certain type of crack region to the total area of ​​the target asphalt pavement image is greater than a certain threshold, for example, one of the three, the crack type of the target asphalt pavement image can be determined by the crack type of the crack region. For example, if the above-mentioned certain type of crack region is a block crack, then the crack classification result of the target asphalt pavement image can be correspondingly determined as a block crack.

[0071] It should be noted that the first direction and the second direction can be set as needed. For example, the first direction can be set to the horizontal direction and the second direction can be set to the vertical direction. Optionally, in some embodiments, taking the horizontal direction as an example, scanning starts from the left end of the first row of the crack area until the rightmost end of the crack is scanned, and the crack size h is calculated along the x-axis direction. x1 ,h x1 is the number of 1s in the x-axis matrix of the first row; go to the next row and repeat the above process until the scanning process of each row is completed. The horizontal size is recorded as H x =max(h x1 ,hx2 ,…,h xm )×a, m is the number of rows of the crack in the element area of ​​the matrix, and a represents the size of the row image block; similarly, along the y-axis direction, the vertical dimension H can be obtained y =max(h y1 ,h y2 ,…,h yn )×a, n is the number of columns of the crack element area in the matrix; based on this, the blockiness feature H of the crack in two directions can be counted, let H=(max{H x ,H y}+1)×a, it is obvious that the larger the H is, the more obvious the cracks in the longitudinal and transverse directions are.

[0072] In some embodiments, the first maximum size is determined by the following formula:

[0073] H x =max(h x1 ,h x2 ,…,h xm )×a;

[0074] Among them, H x represents the first maximum size, h xm represents the number of the target sub-image blocks included in the m-th row of the crack area along the first direction, and a represents the size of the target sub-image block along the first direction.

[0075] In some embodiments, the second maximum size is determined by the following formula:

[0076] H y =max(h y1 ,h y2 ,…,h yn )×β;

[0077] Among them, H y represents the second largest size, h yn represents the number of the target sub-image blocks included in the n-th row of the crack area along the first direction, and β represents the size of the target sub-image block along the second direction.

[0078] It should be noted that Figure 4 Taking the leftmost crack area as an example, H x =700, H y =1100, block feature H = 1200 (in pixels, a is 100 pixels), according to the correspondence between pixels and the actual size of the road surface, the crack area can be characterized by the actual physical size. In this example, 1 pixel represents 1mm, and the blue marked crack area H is 1.2m; similarly, Figure 4The crack area in the middle is 0.5m, and the crack area on the right is 0.6m.

[0079] The asphalt pavement crack classification method provided in the present application first obtains a target asphalt pavement image to be processed; then divides the target asphalt pavement image into multiple sub-image blocks of the same size; then identifies cracks in the target asphalt pavement image, and determines a target sub-image block including the cracks from the multiple sub-image blocks; finally, classifies the target asphalt pavement image for cracks according to the target sub-image blocks. By dividing the target asphalt pavement image into multiple sub-image blocks, the computational complexity in the crack identification process is reduced, the efficiency and accuracy of crack identification are improved, and the efficiency and accuracy of automatic classification of cracks and block cracks in actual engineering are improved.

[0080] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an asphalt pavement crack classification device.

[0081] refer to Figure 5 , the asphalt pavement crack classification device comprises:

[0082] An acquisition module 201 is used to acquire a target asphalt road surface image to be processed;

[0083] A division module 202 divides the target asphalt road surface image into a plurality of sub-image blocks of the same size;

[0084] An identification module 203 is configured to identify cracks in the target asphalt pavement image and determine a target sub-image block including cracks from the plurality of sub-image blocks;

[0085] The classification module 204 classifies cracks in the target asphalt pavement image based on the target sub-image block.

[0086] In some embodiments, the identification module is specifically used to:

[0087] The crack recognition model obtained through training is used to identify cracks in the target asphalt pavement image.

[0088] In some embodiments, the partitioning module includes:

[0089] An acquisition unit, for acquiring a first pixel value of the target asphalt road surface image;

[0090] A determining unit, which determines a second pixel value of a preset sub-image block;

[0091] a filling unit, performing pixel filling on the target asphalt road surface image based on the first pixel value and the second pixel value, so that the pixel value of the target asphalt road surface image after filling can be divided by the second pixel value;

[0092] A dividing unit is configured to divide the filled target asphalt pavement image into a plurality of sub-image blocks of the same size based on the first pixel value; wherein the pixel value of each sub-image block is the first pixel value.

[0093] In some embodiments, the determining unit is specifically configured to:

[0094] A second pixel value of the preset sub-image block is determined based on the first pixel value and a preset division number.

[0095] In some embodiments, the classification module is specifically used to:

[0096] Determine one or more crack regions based on the target sub-image block; wherein the crack region is a closed region composed of a plurality of continuous target sub-image blocks;

[0097] For each crack region, determining a first maximum size of the crack region along a first direction, and determining a second maximum size of the crack region along a second direction; determining a target maximum size that is the largest of the first maximum size and the second maximum size; wherein the first direction is perpendicular to the second direction;

[0098] The target asphalt pavement image is subjected to crack classification based on the target maximum size corresponding to each crack region.

[0099] In some embodiments, the first maximum size is determined by the following formula:

[0100] H x =max(h x1 ,h x2 ,…,h xm )×a;

[0101] Among them, H x represents the first maximum size, h xm represents the number of the target sub-image blocks included in the m-th row of the crack area along the first direction, and a represents the size of the target sub-image block along the first direction.

[0102] In some embodiments, the second maximum size is determined by the following formula:

[0103] H y =max(h y1 ,h y2 ,…,h yn )×β;

[0104] Among them, H y represents the second largest size, h ynrepresents the number of the target sub-image blocks included in the n-th row of the crack area along the first direction, and β represents the size of the target sub-image block along the second direction.

[0105] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0106] The asphalt pavement crack classification device of the above-mentioned embodiment is used to implement the corresponding asphalt pavement crack classification method in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding asphalt pavement crack classification method embodiment, which will not be repeated here.

[0107] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the asphalt pavement crack classification method described in any of the above-mentioned embodiments is implemented.

[0108] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0109] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0110] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040 and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030 and the communication interface 1040 are connected to each other in communication within the device through the bus 1050.

[0111] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0112] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0113] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0114] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and the power equipment. The communication module can realize communication through a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WIFI, Bluetooth, etc.).

[0115] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0116] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0117] The electronic device of the above embodiment is used to implement the corresponding asphalt pavement crack classification method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0118] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the asphalt pavement crack classification method as described in any of the above embodiments.

[0119] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0120] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the asphalt pavement crack classification method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0121] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present application also provides a computer program product, which includes a computer program. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the asphalt pavement crack classification method described in the above-mentioned embodiments. Corresponding to the execution subject corresponding to each step in each embodiment of the asphalt pavement crack classification method, the processor that executes the corresponding step can belong to the corresponding execution subject.

[0122] To simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the following fact, that is, the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuit) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0123] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0124] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present application as described above, which are not provided in detail for the sake of simplicity.

[0125] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for classifying cracks in asphalt pavement, characterized in that: include: Acquire a target asphalt road surface image to be processed; Dividing the target asphalt road surface image into a plurality of sub-image blocks of the same size; Identifying cracks in the target asphalt pavement image, and determining a target sub-image block including the cracks from the plurality of sub-image blocks; Crack classification is performed on the target asphalt pavement image based on the target sub-image block.

2. The method according to claim 1, characterized in that Identifying cracks in the target asphalt pavement image specifically includes: The crack recognition model obtained through training is used to identify cracks in the target asphalt pavement image.

3. The method according to claim 1, characterized in that Dividing the target asphalt road surface image into a plurality of sub-image blocks of the same size specifically includes: Acquire a first pixel value of the target asphalt road surface image; Determine a second pixel value of a preset sub-image block; Performing pixel filling on the target asphalt road surface image based on the first pixel value and the second pixel value, so that the pixel value of the target asphalt road surface image after filling can be divided by the second pixel value; The filled target asphalt road surface image is divided into a plurality of sub-image blocks of the same size based on the first pixel value; wherein the pixel value of each sub-image block is the first pixel value.

4. The method according to claim 3, characterized in that Determining a second pixel value of a preset sub-image block specifically includes: A second pixel value of the preset sub-image block is determined based on the first pixel value and a preset division number.

5. The method according to claim 1, characterized in that The method of performing crack classification on the target asphalt pavement image based on the target sub-image block specifically includes: Determine one or more crack regions based on the target sub-image block; wherein the crack region is a closed region composed of a plurality of continuous target sub-image blocks; For each crack region, determining a first maximum size of the crack region along a first direction, and determining a second maximum size of the crack region along a second direction; determining a target maximum size that is the largest of the first maximum size and the second maximum size; wherein the first direction is perpendicular to the second direction; The target asphalt pavement image is subjected to crack classification based on the target maximum size corresponding to each crack region.

6. The method according to claim 5, characterized in that The first maximum dimension is determined by the following formula: H x =max(h x1 ,h x2 ,…,h xm )×a; Among them, H x represents the first maximum size, h xm represents the number of the target sub-image blocks included in the m-th row of the crack area along the first direction, and a represents the size of the target sub-image block along the first direction.

7. The method according to claim 5, characterized in that The second maximum dimension is determined by the following formula: H y =max(h y1 ,h y2 ,…,h yn )×β; Among them, H y represents the second largest size, h yn represents the number of the target sub-image blocks included in the n-th row of the crack area along the first direction, and β represents the size of the target sub-image block along the second direction.

8. An asphalt pavement crack classification device, characterized in that: include: An acquisition module is used to acquire a target asphalt road surface image to be processed; A division module, dividing the target asphalt road surface image into a plurality of sub-image blocks of the same size; an identification module, for identifying cracks in the target asphalt pavement image, and determining a target sub-image block including the cracks from the plurality of sub-image blocks; A classification module is used to classify cracks in the target asphalt pavement image based on the target sub-image block.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

Images