Airfield pavement friction coefficient prediction method and device based on image analysis

Abstract

The invention provides an airfield pavement friction coefficient prediction method and device based on image analysis, and belongs to the technical field of airfield pavement performance prediction.The method comprises the steps that various airfield pavement image data with known surface friction coefficients are obtained, preprocessing the image data by using an image processing and analysis technology, and extracting feature information capable of reflecting the anti-skid performance of the pavement from the image; establishing a mapping relation model between the known surface friction coefficient and the feature information of the image; and obtaining to-be-predicted airport pavement image data, and inputting the to-be-predicted airport pavement image data into the mapping relation model to obtain a predicted friction coefficient value. The airfield pavement friction coefficient testing efficiency is greatly improved, and the airfield pavement friction coefficient can be monitored in real time.

Description

Technical Field

[0001] The present application belongs to the technical field of airport pavement performance testing, and in particular relates to an airport pavement friction coefficient image analysis prediction method and device. Background Art

[0002] The anti-skid performance of airport pavement is one of the key factors to ensure the safety of aircraft take-off and landing, and the friction coefficient is an important indicator to measure the anti-skid performance of the pavement. Traditional friction coefficient testing methods mostly rely on on-site tests, such as the braking distance method and the wheel-locking trailer method. These methods are not only time-consuming and labor-intensive, but also difficult to achieve real-time monitoring and prediction of the friction coefficient of airport pavements.

[0003] Therefore, a fast and accurate method to predict the friction coefficient of airport pavement is needed. Summary of the invention

[0004] The purpose of the present application is to provide a method and device for predicting the friction coefficient of an airport pavement based on image analysis, so as to solve or alleviate at least one problem in the background technology.

[0005] On the one hand, the technical solution of the present application is: a method for predicting the friction coefficient of an airport pavement based on image analysis, comprising:

[0006] Obtain image data of various airport pavements with known surface friction coefficients, and use image processing and analysis technology to pre-process the image data and extract characteristic information from the image that can reflect the anti-skid performance of the pavement;

[0007] Establish a mapping relationship model between the known surface friction coefficient and the feature information of the image;

[0008] The image data of the airport pavement to be predicted is obtained, and the image data of the airport pavement to be predicted is input into the mapping relationship model to obtain the predicted friction coefficient value.

[0009] In an optional implementation manner of the present application, an airport pavement is imaged by a high-definition camera to obtain airport pavement image data, wherein the image data includes texture information, color information, and humidity information of the airport pavement.

[0010] In an optional implementation manner of the present application, the process of preprocessing various types of airport pavement image data includes denoising, contrast enhancement, and image segmentation.

[0011] In an optional implementation of the present application, a machine learning algorithm or a deep learning algorithm is used to establish a mapping relationship model between a known surface friction coefficient and image features.

[0012] In an optional implementation manner of the present application, it also includes:

[0013] After obtaining the predicted friction coefficient value, the accuracy and reliability of the mapping relationship model are confirmed by comparing it with the friction coefficient value actually measured on the airport pavement. When the mapping relationship model meets the requirements, step S30 is repeated to obtain the subsequent pavement friction coefficient value to be predicted.

[0014] On the other hand, the present application provides an airport pavement friction coefficient prediction device based on image analysis, comprising:

[0015] The feature information extraction module is used to obtain image data of various airport pavements with known surface friction coefficients, and to extract feature information from the image that can reflect the anti-skid performance of the pavement after pre-processing the image data using image processing and analysis technology;

[0016] A model building module is used to build a mapping relationship model between known surface friction coefficient and feature information of the image;

[0017] The prediction module is used to obtain the airport pavement image data to be predicted, input the airport pavement image data to be predicted into the mapping relationship model, and obtain the predicted friction coefficient value.

[0018] In an optional implementation manner of the present application, an airport pavement is imaged by a high-definition camera to obtain airport pavement image data, wherein the image data includes texture information, color information, and humidity information of the airport pavement.

[0019] In an optional implementation manner of the present application, the process of preprocessing various types of airport pavement image data includes denoising, contrast enhancement, and image segmentation.

[0020] In an optional implementation of the present application, a machine learning algorithm or a deep learning algorithm is used to establish a mapping relationship model between a known surface friction coefficient and image features.

[0021] In an optional implementation manner of the present application, it also includes:

[0022] The model correction module is used to confirm the accuracy and reliability of the mapping relationship model by comparing the predicted friction coefficient value with the friction coefficient value actually measured on the airport pavement after obtaining the predicted friction coefficient value. When the mapping relationship model meets the requirements, the content of the prediction module is repeatedly executed to obtain the subsequent pavement friction coefficient value to be predicted.

[0023] In a third aspect, the present application provides an electronic device, including:

[0024] one or more processors;

[0025] Memory;

[0026] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to implement the airport pavement friction coefficient prediction method based on image analysis as described in any of the above items.

[0027] In the final aspect, the present application provides a computer storage medium, which stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the airport pavement friction coefficient prediction method based on image analysis as described in any of the above items.

[0028] This application uses image analysis technology to quickly predict the friction coefficient of the airport pavement, greatly improving the test efficiency, and can realize real-time monitoring of the friction coefficient of the airport pavement, providing technical and accurate reference information for the safe operation of the airport. Non-contact measurement: Compared with the traditional contact measurement method, the method of this application is non-contact measurement, which can avoid damage to the pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solution provided by the present application, the following is a brief introduction to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present application.

[0030] Figure 1 This is a schematic diagram of the airport pavement friction coefficient prediction method based on image analysis in this application.

[0031] Figure 2 This is a schematic diagram of the airport pavement friction coefficient prediction device based on image analysis in this application.

[0032] Figure 3 Schematic diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the implementation of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below in conjunction with the drawings in the embodiments of this application.

[0034] In order to overcome the problems of time-consuming, labor-intensive and inefficient airport pavement friction coefficient measurement in the prior art, the present application provides an airport pavement friction coefficient prediction method and device based on image analysis.

[0035] like Figure 1 As shown, the airport pavement friction coefficient prediction method based on image analysis of the present application includes the following process:

[0036] Step S10, acquiring image data of various airport pavements with known surface friction coefficients, preprocessing the image data using image processing and analysis techniques, and extracting characteristic information that can reflect the anti-skid performance of the pavement from the preprocessed images.

[0037] In this application, the image data of various airport pavements with known surface friction coefficients can be obtained by using image acquisition equipment such as high-definition cameras to collect image data of the airport pavement regularly or in real time. The collected image data should include key information such as the texture, color, and humidity of the airport pavement.

[0038] In the present application, the process of preprocessing image data includes denoising, contrast enhancement, image segmentation, etc., so as to improve image quality and extract useful feature information therefrom, and the feature information includes texture features, color features, humidity features, etc.

[0039] Step S20, establishing a mapping relationship model between the known surface friction coefficient and the feature information of the image.

[0040] In this application, machine learning algorithms, deep learning algorithms, etc. can be used to establish a mapping relationship model between the known surface friction coefficient and image features. The mapping relationship model should be able to predict the friction coefficient value of the corresponding road surface based on the input image feature information.

[0041] Step S30, obtaining the airport pavement image data to be predicted, inputting the airport pavement image data to be predicted into the mapping relationship model, and obtaining the predicted friction coefficient value.

[0042] Furthermore, after obtaining the predicted friction coefficient value in the present application, it can be verified by comparing it with the friction coefficient value actually measured on the airport pavement to confirm the accuracy and reliability of the mapping relationship model. When the mapping relationship model is reliable, repeating step S30 can obtain the subsequent pavement friction coefficient characteristics to be predicted.

[0043] This application uses image analysis technology to quickly predict the friction coefficient of the airport pavement, greatly improving the test efficiency, and can realize real-time monitoring of the friction coefficient of the airport pavement, providing technical and accurate reference information for the safe operation of the airport. Non-contact measurement: Compared with the traditional contact measurement method, the method of this application is non-contact measurement, which can avoid damage to the pavement.

[0044] The method of the present application is not only applicable to the prediction of the friction coefficient of airport pavement, but can also be extended to the evaluation of the anti-skid performance of other roads or ground surfaces and is widely used.

[0045] On this basis, if Figure 2As shown, the present application provides an airport pavement friction coefficient prediction device based on image analysis, and the device 100 includes:

[0046] The feature information extraction module 101 is used to obtain various types of airport pavement image data with known surface friction coefficients, and to extract feature information that can reflect the anti-skid performance of the pavement after pre-processing the image data using image processing and analysis technology;

[0047] A model building module 102 is used to build a mapping relationship model between the known surface friction coefficient and the feature information of the image;

[0048] The prediction module 103 is used to obtain the airport pavement image data to be predicted, input the airport pavement image data to be predicted into the mapping relationship model, and obtain the predicted friction coefficient value.

[0049] Furthermore, the device also includes a model correction module 104, which is used to confirm the accuracy and reliability of the mapping relationship model by comparing and verifying the predicted friction coefficient value with the friction coefficient value actually measured on the airport pavement after obtaining the predicted friction coefficient value. When the mapping relationship model meets the requirements, the content of the prediction module is repeatedly executed to obtain the subsequent pavement friction coefficient value to be predicted.

[0050] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 200, which includes a processor 201, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 202 or a program loaded from a storage device 108 to a random access memory (RAM) 203. Processor 101 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. Processor 201 may also include an onboard memory for caching purposes. Processor 201 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0051] In RAM 203, various programs and data required for the operation of electronic device 200 are stored. Processor 201, ROM 202 and RAM 203 are connected to each other via bus 204. Processor 201 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 202 and / or RAM 203. It should be noted that the program can also be stored in one or more memories other than ROM 202 and RAM 203. Processor 201 can also perform various operations of the method or flow according to the embodiment of the application by executing the program stored in the one or more memories.

[0052] According to an embodiment of the present application, the electronic device 200 may further include an input / output (I / O) interface 205, which is also connected to the bus 204. The electronic device 200 may further include one or more of the following components connected to the I / O interface 205: an input device 206 including a keyboard, a mouse, etc.; an output device 207 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage device 208 including a hard disk, etc.; and a communication device 209 including a network interface card such as a LAN card, a modem, etc. The communication device 209 performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 205 as needed. Removable media, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that the computer program read therefrom is installed into the storage device 208 as needed.

[0053] The present application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, various operations of the above methods or processes according to the embodiments of the present application are implemented.

[0054] According to an embodiment of the present application, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present application, a computer-readable storage medium may include the ROM 202 and / or RAM 203 described above and / or one or more memories other than ROM 102 and RAM 203.

[0055] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for predicting airport pavement friction coefficient based on image analysis, characterized in that: include: Obtain image data of various airport pavements with known surface friction coefficients, and use image processing and analysis technology to pre-process the image data and extract characteristic information from the image that can reflect the anti-skid performance of the pavement; Establish a mapping relationship model between the known surface friction coefficient and the feature information of the image; The image data of the airport pavement to be predicted is obtained, and the image data of the airport pavement to be predicted is input into the mapping relationship model to obtain the predicted friction coefficient value.

2. The method for predicting the friction coefficient of an airport pavement based on image analysis according to claim 1, characterized in that: The airport pavement is imaged by a high-definition camera to obtain airport pavement image data, wherein the image data includes texture information, color information, and humidity information of the airport pavement.

3. The method for predicting the friction coefficient of airport pavement based on image analysis according to claim 2, characterized in that: The process of preprocessing various airport pavement image data includes denoising, contrast enhancement, and image segmentation.

4. The method for predicting the friction coefficient of an airport pavement based on image analysis according to claim 1, characterized in that: A machine learning algorithm or a deep learning algorithm is used to establish a mapping relationship model between the known surface friction coefficient and the image features.

5. The method for predicting the friction coefficient of airport pavement based on image analysis according to claim 1, characterized in that: Also includes: After obtaining the predicted friction coefficient value, the accuracy and reliability of the mapping relationship model are confirmed by comparing it with the friction coefficient value actually measured on the airport pavement. When the mapping relationship model meets the requirements, step S30 is repeated to obtain the subsequent pavement friction coefficient value to be predicted.

6. An airport pavement friction coefficient prediction device based on image analysis, characterized in that: include: The feature information extraction module is used to obtain image data of various airport pavements with known surface friction coefficients, and to extract feature information from the image that can reflect the anti-skid performance of the pavement after pre-processing the image data using image processing and analysis technology; A model building module is used to build a mapping relationship model between known surface friction coefficient and feature information of the image; The prediction module is used to obtain the airport pavement image data to be predicted, input the airport pavement image data to be predicted into the mapping relationship model, and obtain the predicted friction coefficient value.

7. The device for predicting the friction coefficient of an airport pavement based on image analysis according to claim 6, characterized in that: The airport pavement is imaged by a high-definition camera to obtain airport pavement image data, wherein the image data includes texture information, color information, and humidity information of the airport pavement.

8. The device for predicting the friction coefficient of an airport pavement based on image analysis according to claim 7, characterized in that: The process of preprocessing various airport pavement image data includes denoising, contrast enhancement, and image segmentation.

9. The device for predicting the friction coefficient of an airport pavement based on image analysis according to claim 6, characterized in that: A machine learning algorithm or a deep learning algorithm is used to establish a mapping relationship model between the known surface friction coefficient and the image features.

10. The device for predicting the friction coefficient of an airport pavement based on image analysis according to claim 6, characterized in that: Also includes: The model correction module is used to confirm the accuracy and reliability of the mapping relationship model by comparing the predicted friction coefficient value with the friction coefficient value actually measured on the airport pavement after obtaining the predicted friction coefficient value. When the mapping relationship model meets the requirements, the content of the prediction module is repeatedly executed to obtain the subsequent pavement friction coefficient value to be predicted.

11. An electronic device, characterized in that: include: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the one or more processors, and the one or more applications are configured to implement the airport pavement friction coefficient prediction method based on image analysis as described in any one of claims 1 to 5.

12. A computer storage medium, characterized in that: The computer storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the airport pavement friction coefficient prediction method based on image analysis as described in any one of claims 1 to 5.

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