Road axle load spectrum monitoring method and system based on multi-source data

Abstract

The invention provides a road axle load spectrum monitoring method and system based on multi-source data, and belongs to the technical field of road detection. Comprising the steps of obtaining a road condition image of a current road surface, obtaining a vehicle body size and a vehicle position of a vehicle in the road condition image according to a pre-training recognition model, and determining a real vehicle body position according to obtained vehicle GPS data; correcting the vehicle body size to obtain a corrected size, and obtaining the load capacity of the vehicle according to the corrected size; according to the load capacity, the load magnitude of the vehicle on the current road surface is determined; and obtaining load magnitudes of different vehicles on the current road surface, performing statistics, and generating an axle load spectrum of the current road surface corresponding to the real position of the vehicle body. According to the invention, automatic and accurate road axle load spectrum monitoring is realized, the labor cost and the labor intensity are reduced, and the monitoring accuracy and the monitoring working efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of road detection, and in particular to a method and system for monitoring highway axle load spectra based on multi-source data. Background Art

[0002] The large axle weight of heavy transport vehicles (large heavy vehicles) will damage the road structure, causing the road to easily develop structural diseases during long-term use. In order to improve the situation of highway structural diseases, it is necessary to first determine the causes of road diseases and the sections prone to them.

[0003] The road axle load spectrum (axle load spectrum is the abbreviation of vehicle load spectrum, which is one of the most effective parameters reflecting the performance of automobiles and road conditions) can provide important information for road management and maintenance. It plays an important role in road structure design, performance evaluation, quality status assessment and road structure disease analysis. Therefore, studying the road structure disease identification and classification method and the road axle load spectrum extraction method and evaluation system is an important basic work to ensure good road performance.

[0004] In the existing scheme, manual methods are mostly used for investigation. By classifying vehicles, the types and axles of each type are counted separately, and the axle load level, circumference, wheelbase, etc. are calculated according to the vehicle's own weight and the circumference of the tire. However, manual methods have disadvantages such as large workload and low efficiency. Summary of the invention

[0005] In order to address the deficiencies of the prior art, the present invention provides a highway axle load spectrum monitoring method and system based on multi-source data, which performs vehicle body size correction to obtain a corrected size, and obtains the vehicle's load capacity based on the corrected size, and determines the load level of the vehicle on the current road surface based on the load capacity, obtains the load levels of different vehicles on a specified road surface, and performs statistics to generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body, thereby realizing automated and accurate highway axle load spectrum monitoring.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for monitoring axle load spectrum of a highway based on multi-source data, comprising the following process: Obtain the current road condition image, obtain the vehicle body size and vehicle position in the road condition image based on the pre-trained recognition model, and determine the actual vehicle body position based on the obtained vehicle GPS data; Correcting the vehicle body size to obtain a corrected size, and obtaining the vehicle load capacity according to the corrected size; Determining the load level of the vehicle on the current road surface according to the load weight; Obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

[0007] In a second aspect, the present invention provides a highway axle load spectrum monitoring system based on multi-source data, comprising: The vehicle body size recognition unit is configured to: obtain a road condition image of the current road surface, obtain the vehicle body size and vehicle position in the road condition image according to a pre-trained recognition model, and determine the actual position of the vehicle body according to the obtained vehicle GPS data; The load calculation unit is configured to: perform a body size correction to obtain a corrected size, and obtain the vehicle load according to the corrected size; A load magnitude calculation unit is configured to: determine the load magnitude of the vehicle on the current road surface according to the load weight; The axle load spectrum generating unit is configured to obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

[0008] In a third aspect, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the highway axle load spectrum monitoring method based on multi-source data as described in the first aspect is implemented.

[0009] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the highway axle load spectrum monitoring method based on multi-source data as described in the first aspect.

[0010] In a fifth aspect, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the highway axle load spectrum monitoring method based on multi-source data as described in the first aspect.

[0011] The beneficial effects of the present invention include: automatically identifying the size of a vehicle based on the collected image, and further obtaining the vehicle's load capacity; determining the load magnitude of the vehicle on the current road surface based on the load capacity; obtaining the load magnitudes of different vehicles on the current road surface, and performing statistics to generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body, thereby realizing automated and accurate highway axle load spectrum monitoring, reducing labor costs and labor intensity, and improving monitoring accuracy and monitoring work efficiency.

[0012] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0014] Figure 1 This is a flow chart of the highway axle load spectrum monitoring method based on multi-source data described in Example 1 of the present invention.

[0015] Figure 2 This is a functional principle block diagram of the highway axle load spectrum monitoring system based on multi-source data described in Example 2 of the present invention.

[0016] Figure 3 This is a functional principle block diagram of the computer device described in Example 3 of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0018] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0019] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other. Example 1

[0020] like Figure 1 As shown, this implementation proposes a highway axle load spectrum monitoring method based on multi-source data, which includes the following process: S1: Detect the road condition of the preset path, obtain the road condition image of the current road surface, obtain the body size and vehicle position of the vehicle in the road condition image based on the pre-trained recognition model, and determine the actual position of the vehicle body based on the obtained vehicle GPS data.

[0021] It can be understood that in order to improve the monitoring accuracy of the axle load spectrum of a specified road surface, this embodiment is implemented based on multi-source data, wherein the multi-source data includes road condition images and GPS data. In this embodiment, the recognition accuracy is improved by fusing the road condition images and GPS data.

[0022] Optionally, in this implementation, a camera may be used to collect road condition images. The camera may be an ordinary camera, a recognition camera, or a surveillance camera equipped with a recognition camera.

[0023] Optionally, the camera for collecting road condition images may be a plurality of road condition monitoring cameras, and each road condition monitoring camera is distributed at preset intervals. The distribution of the road condition monitoring cameras includes, but is not limited to, straight lines, loops, and curves, and the preset interval may be adjusted according to the area to be photographed. For example, when the area to be photographed is too large, a plurality of road condition monitoring cameras may be set, and the plurality of road condition monitoring cameras may be road condition monitoring cameras on a plurality of loops. The layout relationship of each road condition monitoring camera is determined according to the preset interval to determine the camera parameters of the road condition monitoring camera, and then the image is acquired according to the camera parameters. The road condition monitoring camera has adjustable camera parameters, and the camera parameters include, but are not limited to, clarity, position, and the like.

[0024] In this embodiment, the recognition process of the pre-trained recognition model includes: Image preprocessing: read road condition images and perform necessary preprocessing on the images, such as scaling, denoising, contrast enhancement, etc., to improve the accuracy of the recognition model; License plate recognition, using a pre-trained license plate recognition model (such as a convolutional neural network-based object detection model) to detect the license plate area in the image, extract the image of the license plate area, and use OCR (optical character recognition) technology to recognize the license plate number; Vehicle localization: Use the pre-trained vehicle detection model to locate the vehicle area in the image, extract the bounding box of the vehicle area, which defines the position and size of the vehicle in the image, and determine the actual position of the vehicle in combination with the acquired vehicle GPS data.

[0025] Vehicle size estimation: Assume that the size of the vehicle in the image is proportional to its actual size. This usually requires a known reference or landmark to determine the scale in the image; if the image contains an object of known size (such as a road sign, parking line, etc.), the pixel size and actual size of the object can be used to calculate the scale, including: ; Use the scale and pixel size of the vehicle bounding box to estimate the actual size of the vehicle, including: ; .

[0026] S2: Correcting the vehicle body size to obtain a corrected size, and obtaining the vehicle load capacity according to the corrected size.

[0027] In order to correct the size of the vehicle body according to the average tilt angle of the camera, it is necessary to take into account the perspective effect in the image. The perspective effect causes objects far away from the camera to appear smaller than they actually are, while objects close to the camera appear larger. This effect is directly related to the tilt angle of the camera.

[0028] Without the actual distance from the camera to the vehicle d In the case of a vehicle, it is usually impossible to directly use the inclination angle to accurately correct the vehicle body size. In this embodiment, a simplified assumption can be made that there is a certain proportional relationship between the vehicle size in the image and the inclination angle of the camera. This relationship may vary depending on factors such as the camera model, focal length, and installation height, but in the absence of more detailed information, an approximate correction factor can be used to determine the proportional relationship between the vehicle size and the inclination angle of the camera.

[0029] Therefore, in this embodiment, a correction factor is introduced k , which is calculated based on the average tilt of the cameras and is used to adjust the vehicle dimensions measured in the image: ; in, is the average tilt angle of the camera.

[0030] Use this correction factor To adjust the vehicle dimensions measured in the image: ; ; in, L corrected and W corrected is the corrected vehicle length and vehicle width.

[0031] The vehicle's load capacity is obtained based on the corrected dimensions, including: ; in, is the estimated load capacity, k 1 、k 2 and C are constant coefficients that need to be learned from the vehicle size and weight dataset through regression analysis.

[0032] S3: Determine the load level of the vehicle on the current road surface according to the load weight.

[0033] Set the light load, medium load and heavy load levels, including: Light load: W load ≤ W 1; Medium load:W 1< W load ≤ W 2; Overload: W load > W 2.

[0034] According to the calculated load W load The classification is divided into levels, among which, W 1 and W 2 represents two thresholds.

[0035] S4: Obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

[0036] Perform statistical analysis on the collected load level data, including calculating the number of vehicles at each load level, the proportion of vehicles at each load level to the total number of vehicles, etc. Based on the statistical analysis results, generate an axle load spectrum chart of the current road surface corresponding to the actual position of the vehicle body, intuitively showing the distribution of vehicle axle loads on the road surface. Example 2

[0037] like Figure 2 As shown, this implementation provides a highway axle load spectrum monitoring system based on multi-source data, the system comprising: The vehicle body size recognition unit is configured to: obtain a road condition image of the current road surface, obtain the vehicle body size and vehicle position in the road condition image according to a pre-trained recognition model, and determine the actual position of the vehicle body according to the obtained vehicle GPS data; The load calculation unit is configured to: perform a body size correction to obtain a corrected size, and obtain the vehicle load according to the corrected size; A load magnitude calculation unit is configured to: determine the load magnitude of the vehicle on the current road surface according to the load weight; The axle load spectrum generating unit is configured to obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

[0038] The detailed working process of each unit is described in Example 1 and will not be repeated here.

[0039] It is understandable that the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (some) of the units can be further divided into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0040] According to another embodiment of the present application, the system described in this embodiment can be constructed, and the method of Example 1 of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein. Example 3

[0041] like Figure 3 As shown, the present implementation provides an electronic device, which includes a processor, a communication interface, and a computer-readable storage medium. The processor, the communication interface, and the computer-readable storage medium may be connected via a bus or other means.

[0042] Among them, the communication interface is used to receive and send data, the computer-readable storage medium can be stored in the memory of the electronic device, the computer-readable storage medium is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer-readable storage medium.

[0043] A processor (or CPU (Central Processing Unit)) is the computing core and control core of an electronic device, which is suitable for implementing one or more instructions, and specifically suitable for loading and executing one or more instructions to implement corresponding method flows or corresponding functions.

[0044] The processor is configured to perform the following process: Obtain the current road condition image, obtain the vehicle body size and vehicle position in the road condition image based on the pre-trained recognition model, and determine the actual vehicle body position based on the obtained vehicle GPS data; Correcting the vehicle body size to obtain a corrected size, and obtaining the vehicle load capacity according to the corrected size; Determining the load level of the vehicle on the current road surface according to the load weight; Obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

[0045] The detailed working process is described in Example 1 and will not be repeated here. Example 4

[0046] This implementation provides a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides a storage space that stores the processing system of the electronic device.

[0047] In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory; optionally, it may also be at least one computer-readable storage medium located away from the aforementioned processor.

[0048] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process: Obtain the current road condition image, obtain the vehicle body size and vehicle position in the road condition image based on the pre-trained recognition model, and determine the actual vehicle body position based on the obtained vehicle GPS data; Correcting the vehicle body size to obtain a corrected size, and obtaining the vehicle load capacity according to the corrected size; Determining the load level of the vehicle on the current road surface according to the load weight; Obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

[0049] The detailed working process is described in Example 1 and will not be repeated here. Example 5

[0050] The present implementation provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the following process: Obtain the current road condition image, obtain the vehicle body size and vehicle position in the road condition image based on the pre-trained recognition model, and determine the actual vehicle body position based on the obtained vehicle GPS data; Correcting the vehicle body size to obtain a corrected size, and obtaining the vehicle load capacity according to the corrected size; Determining the load level of the vehicle on the current road surface according to the load weight; Obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

[0051] The detailed working process is described in Example 1 and will not be repeated here.

[0052] A person skilled in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0053] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server, a data center, etc. that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A highway axle load spectrum monitoring method based on multi-source data, characterized in that: The process includes: Obtain the current road condition image, obtain the vehicle body size and vehicle position in the road condition image based on the pre-trained recognition model, and determine the actual vehicle body position based on the obtained vehicle GPS data; Correcting the vehicle body size to obtain a corrected size, and obtaining the vehicle load capacity according to the corrected size; Determining the load level of the vehicle on the current road surface according to the load weight; Obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

2. The highway axle load spectrum monitoring method based on multi-source data according to claim 1 is characterized in that: According to the pre-trained recognition model, the vehicle body size and vehicle position in the road condition image are obtained, and the real position of the vehicle body is determined according to the obtained vehicle GPS data, including: Calculate the scale from the pixel dimensions and the actual size of any given object: ； Use the scale and the pixel dimensions of the vehicle's bounding box to estimate the actual body size of the vehicle: ； 。 3. The highway axle load spectrum monitoring method based on multi-source data according to claim 1 is characterized in that: The vehicle body size is corrected according to the average inclination angle of the camera to obtain the corrected size, including: ； ； in, L corrected and W corrected is the corrected vehicle length and vehicle width, is the average tilt angle of the camera, .

4. The highway axle load spectrum monitoring method based on multi-source data according to claim 3 is characterized in that: The vehicle's load capacity is obtained based on the corrected dimensions, including: ； in, W load is the estimated load capacity, k 1 、k 2 and C is a constant coefficient.

5. The highway axle load spectrum monitoring method based on multi-source data according to claim 4 is characterized in that: Determining the load level of the vehicle on the current road surface according to the load weight includes: Set light, medium and heavy load levels, including: Light load: W load ≤ W 1; Medium load: W 1< W load ≤ W 2; Overload: W load > W 2. According to the calculated W load Level division, W 1 and W 2 are threshold values.

6. The highway axle load spectrum monitoring method based on multi-source data according to claim 1 is characterized in that: Obtain the load magnitude of different vehicles on the current road surface, perform statistics, and generate the axle load spectrum of the current road surface corresponding to the actual position of the vehicle body, including: Perform statistical analysis on the collected load level data, including calculating the number of vehicles at each load level and the proportion of vehicles at each load level to the total number of vehicles. Based on the statistical analysis results, generate an axle load spectrum chart of the current road surface corresponding to the actual position of the vehicle body, intuitively showing the distribution of vehicle axle loads on the road surface.

7. A highway axle load spectrum monitoring system based on multi-source data, characterized in that: include: The vehicle body size recognition unit is configured to: obtain a road condition image of the current road surface, obtain the vehicle body size and vehicle position in the road condition image according to a pre-trained recognition model, and determine the actual position of the vehicle body according to the obtained vehicle GPS data; The load calculation unit is configured to: perform a body size correction to obtain a corrected size, and obtain the vehicle load according to the corrected size; A load magnitude calculation unit is configured to: determine the load magnitude of the vehicle on the current road surface according to the load weight; The axle load spectrum generating unit is configured to obtain the load magnitudes of different vehicles on the current road surface, perform statistics, and generate an axle load spectrum of the current road surface corresponding to the actual position of the vehicle body.

8. A computer device, characterized in that: include: a processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the highway axle load spectrum monitoring method based on multi-source data as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the highway axle load spectrum monitoring method based on multi-source data as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the highway axle load spectrum monitoring method based on multi-source data as described in any one of claims 1 to 6.

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