CN112991311A - Vehicle overweight detection method, device and system and terminal equipment - Google Patents

Vehicle overweight detection method, device and system and terminal equipment Download PDF

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CN112991311A
CN112991311A CN202110333233.3A CN202110333233A CN112991311A CN 112991311 A CN112991311 A CN 112991311A CN 202110333233 A CN202110333233 A CN 202110333233A CN 112991311 A CN112991311 A CN 112991311A
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curved surface
vehicle
surface image
tire
similarity
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CN112991311B (en
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孙晓辉
洪成雨
胡明伟
陈曦
彭永燊
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Shenzhen University
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Abstract

The application is suitable for the technical field of vehicle detection, and provides a vehicle overweight detection method, a device, a system and terminal equipment, wherein the vehicle overweight detection method comprises the following steps: acquiring the number of axles of a vehicle, a first curved surface image of a target tire on each axle and the number of tires on the axle where the target tire is located; acquiring the similarity between the first curved surface image and a curved surface image in a tire database, and determining that the curved surface image with the maximum similarity to the first curved surface image in the tire database is a second curved surface image, wherein the tire database comprises curved surface images of tires under different loads; and determining an overweight detection result of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located. The technical scheme of this application can solve among the prior art overweight check out test set of vehicle and easily take place fatigue failure, increase cost of maintenance's problem.

Description

Vehicle overweight detection method, device and system and terminal equipment
Technical Field
The application belongs to the technical field of vehicle detection, and particularly relates to a vehicle overweight detection method, device, system and terminal equipment.
Background
With the development of artificial intelligence technology, the management of road surface safety is more and more intelligent, wherein the management of road surface safety mainly comprises the detection of the driving quality, the road surface damage condition and the road surface bearing capacity of the road surface. At present, the main factor influencing the road surface safety management is that vehicles run overweight, the overweight running of the vehicles can not only cause road surface damage and bridge breakage, but also cause the service life of the road surface to be greatly shortened, and in order to increase the service life of the road as much as possible, various vehicle overweight detection methods are adopted to detect the load of the vehicles at present.
The current vehicle overweight detection method mainly comprises the following steps: the vehicle overweight detection device is buried in a road base layer to detect the road running condition and the vehicle load, or weighing devices such as a wagon balance and the like are arranged, or a deceleration strip is arranged in the center of the road in a protruding mode, a weighing plate and a pressure sensor are arranged in the deceleration strip area, and the vehicle load is calculated through acquiring data of the pressure sensor. The vehicle overweight detection method easily causes fatigue damage to vehicle overweight detection equipment, increases maintenance cost and has certain limit on vehicle speed. Therefore, it is an important problem to be solved urgently to provide a novel vehicle overweight detection method.
Disclosure of Invention
The embodiment of the application provides a vehicle overweight detection method, device and system and terminal equipment, and can solve the problems that in the prior art, the vehicle overweight detection equipment is easy to generate fatigue damage and the maintenance cost is increased.
A first aspect of an embodiment of the present application provides a vehicle overweight detection method, including:
acquiring the number of axles of a vehicle, a first curved surface image of a target tire on each axle and the number of tires on the axle where the target tire is located;
acquiring the similarity between the first curved surface image and a curved surface image in a tire database, and determining that the curved surface image with the maximum similarity to the first curved surface image in the tire database is a second curved surface image, wherein the tire database comprises curved surface images of tires under different loads;
and determining an overweight detection result of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
A second aspect of embodiments of the present application provides a vehicle overweight detection device, comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring the number of axles of a vehicle, a first curved surface image of a target tire on each axle and the number of tires on the axle where the target tire is located;
the similarity determining module is used for acquiring the similarity between the first curved surface image and a curved surface image in a tire database, and determining that the curved surface image with the maximum similarity to the first curved surface image in the tire database is a second curved surface image, wherein the tire database comprises curved surface images of tires under different loads;
and the result determining module is used for determining the overweight detection result of the vehicle according to the load of the tire corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
A third aspect of the embodiments of the present application provides a vehicle overweight detection system, which includes an image acquisition device, a vehicle model identification device and the vehicle overweight detection device as described in the second aspect, wherein the image acquisition device and the vehicle model identification device are respectively connected with the vehicle overweight detection device;
the image acquisition equipment is used for acquiring a first curved surface image of a target tire on each axle of the vehicle and transmitting the first curved surface image to the vehicle overweight detection device;
the vehicle model identification device is used for identifying the number of axles of the vehicle and the number of tires on each axle of the vehicle and transmitting the number of axles of the vehicle and the number of tires on each axle of the vehicle to the vehicle overweight detection device;
the vehicle overweight detection device is used for acquiring the similarity between the first curved surface image and a curved surface image in a tire database, and determining the curved surface image with the maximum similarity to the first curved surface image in the tire database as a second curved surface image, wherein the tire database comprises the curved surface images of tires under different loads; and determining an overweight detection result of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
A fourth aspect of the embodiments of the present application provides a terminal device, including: a processor and a computer program operable on the processor, the processor implementing the method for detecting a vehicle overweight of a vehicle according to the first aspect when executing the computer program.
A fifth aspect of embodiments of the present application provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the steps of the vehicle overweight detection method according to the first aspect.
A sixth aspect of embodiments of the present application provides a computer program product, which, when run on a terminal device, causes the terminal device to execute the vehicle overweight detection method of the first aspect.
Compared with the prior art, the embodiment of the application has the advantages that: according to the method, the similarity between the first curved surface image and the curved surface in the tire database is obtained by obtaining the first curved surface image of the target tire on each axle of the vehicle, the image with the maximum similarity to the first curved surface image is obtained in the tire database and is the second curved surface image, and the second curved surface image contains the load of the corresponding tire, so that whether the vehicle is overweight can be detected according to the load of the tire corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a vehicle overweight detection method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an application scenario of the vehicle overweight detection method on a one-way road;
FIG. 3 is a schematic diagram of an application scenario of the vehicle overweight detection method in a dual lane;
fig. 4 is a schematic flow chart of a vehicle overweight detection method according to the second embodiment of the present application;
fig. 5 is a schematic structural diagram of a vehicle overweight detection device according to a third embodiment of the present application;
fig. 6 is a schematic structural diagram of a vehicle overweight detection system provided by the fourth embodiment of the application;
fig. 7 is a schematic structural diagram of a terminal device according to a fifth embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
It should be understood that, the sequence numbers of the steps in this embodiment do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation to the implementation process of the embodiment of the present application.
In order to explain the technical solution of the present application, the following description is given by way of specific examples.
Referring to fig. 1, which shows a schematic flow chart of a vehicle overweight detection method provided in an embodiment of the present application, as shown in fig. 1, the vehicle overweight detection method may include the following steps:
step 101, obtaining the number of axles of a vehicle, a first curved surface image of a target tire on each axle and the number of tires on the axle where the target tire is located.
The first curved surface image of the target tire can be a curved surface image acquired by the terminal device in real time in the vehicle running process, and can also be a curved surface image acquired by the terminal device from a video recorded by the depth camera. The manner of acquiring the first curved surface image of the target tire may be adjusted according to a specific application environment, and is not specifically limited herein.
Specifically, the process of acquiring the first curved surface image of the target tire by the terminal device in real time during the running process of the vehicle may be as follows: the method comprises the steps of firstly, collecting a first curved surface image of a target tire through image collection equipment, then transmitting the first curved surface image to terminal equipment, and finally receiving the first curved surface image through the terminal equipment. The image acquisition device may be any device capable of acquiring a curved surface image, such as a three-dimensional laser scanner, a depth camera, and the like.
Specifically, the process of the terminal device obtaining the first curved surface image of the target tire from the video recorded by the depth camera may be: firstly, carrying out image sampling on a video recorded by a depth camera, extracting a first curved surface image of a target tire, transmitting the first curved surface image to a terminal device, and finally receiving the first curved surface image by the terminal device.
In the embodiment of the application, when the number of axles of the vehicle is obtained, the model of the vehicle can be obtained by the terminal device, and then the number of axles corresponding to the model of the vehicle can be obtained from the vehicle information database according to the model of the vehicle. Similarly, when the number of tires on the axle where the target tire is located is obtained, the vehicle model can be obtained by the terminal device, and then the number of tires on each axle is obtained from the vehicle information database according to the vehicle model, so that the number of tires on the axle where the target tire is located can be obtained. The vehicle information database comprises vehicle models, vehicle structure information corresponding to the vehicle models and rated load corresponding to the vehicle models, wherein the vehicle structure information comprises but is not limited to the number of axles of the vehicle, the number of tires on each axle and the total number of the tires.
In the embodiment of the application, the target tire on each axle refers to a tire on the same side of each axle as the overweight detection device, and the position of the target tire can be adjusted according to the position of the overweight detection device of the vehicle.
For example, as shown in fig. 2, the vehicle a is running on a one-way road, the vehicle a is a two-axle vehicle, the position of the vehicle overweight detecting device is the right side of the vehicle advancing direction, and the tire on the same side as the vehicle overweight detecting device is the target tire at this time, and for the vehicle a, the target tires are two, and according to the vehicle a shown in fig. 2, the axle of the vehicle a includes a first axle and a second axle, the first target tire may be a tire on the first axle and on the same side as the vehicle overweight detecting device, and the second target tire may be a tire on the second axle and on the same side as the vehicle overweight detecting device.
For example, as shown in fig. 3, the vehicle B and the vehicle C travel on a two-way road, the vehicle B and the vehicle C are three-axle vehicles, one vehicle overweight detection device is respectively arranged on both sides of the road, the vehicle overweight detection device on the lane a1 is positioned on the left side of the road, the vehicle overweight detection device on the lane a2 is positioned on the right side of the road, for the vehicle B traveling on the lane a1, the number of target tires is three, and the positions of the target tires are all positioned on the left side of the advancing direction of the vehicle body (i.e., on the same side as the vehicle overweight detection device), and since the vehicle B is a three-axle vehicle, the tires on the same side as the vehicle overweight detection devices on the three axles are all target tires. Similarly, for the vehicle C traveling on the lane a2, the number of target tires is also three, and the tires on the same side as the vehicle overweight detection device on the three axles are all the target tires, but the position of the target tire of the vehicle C is located on the right side in the vehicle body advancing direction (i.e., on the same side as the vehicle overweight detection device).
And 102, acquiring the similarity between the first curved surface image and the curved surface image in the tire database, and determining the curved surface image with the maximum similarity with the first curved surface image in the tire database as a second curved surface image.
The curved surface images in the tire database are curved surface images of tires of different models, different sizes, different tire pressures, different vehicle speeds and different loads.
In this embodiment of the application, the method for calculating the similarity between the first curved surface image and the curved surface image in the tire database is not unique, the similarity may be calculated directly according to the image characteristics between the first curved surface image and the curved surface image in the tire database, the similarity may also be calculated according to the comparison between the deformation parameter of the target tire corresponding to the first curved surface image and the reference deformation parameter (i.e., the deformation parameter corresponding to the curved surface image in the tire database), and there are many algorithms for calculating the similarity based on the deformation parameter or the image characteristics, such as an artificial neural network and a cosine similarity calculation method. Any similarity calculation method can be adopted for calculating the similarity between the first curved surface image and the curved surface image in the tire database, and the application is not particularly limited herein.
Taking the artificial neural network as an example, the similarity between the first curved surface image and the curved surface image in the tire database is calculated. The calculation process is as follows: firstly, the deformation parameters of a target tire are extracted from a first curved surface image through an image feature extraction algorithm, and the reference deformation parameters of all curved surface images are obtained from a tire database. And secondly, taking the deformation parameter of the target tire and the reference deformation parameter as the input of the artificial neural network, and outputting the reference deformation parameter and the similarity between the reference deformation parameter and the deformation parameter of the target tire after convolution operation of the artificial neural network. And the terminal equipment acquires the reference deformation parameter output by the artificial neural network and the similarity between the reference deformation parameter and the deformation parameter of the target tire, compares the similarities and determines the reference deformation parameter corresponding to the maximum similarity. And finally, obtaining a curved surface image corresponding to the reference deformation parameter according to the reference deformation parameter matching corresponding to the maximum similarity, and determining that the curved surface image is a second curved surface image with the maximum similarity with the first curved surface image of the target tire.
It should be understood that, in the embodiment of the present application, the reference deformation parameter and the deformation parameter of the target tire include, but are not limited to, deformation curved surface data such as a tire bottom subsidence, a tire lateral expansion, an expansion curve, and the like, where the tire bottom subsidence refers to a data value of a bottom of the target tire sunken compared to a bottom of the target tire in an unloaded condition, the tire lateral expansion refers to a maximum value of a lateral expansion of the target tire in a vehicle traveling process compared to a lateral expansion of the target tire in the unloaded condition, and the expansion curve refers to a curve line of a lateral expansion of the target tire in the vehicle traveling process compared to a lateral expansion of the target tire in the unloaded condition.
And 103, determining an overweight detection result of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axles where the target tires are located.
In the embodiment of the present application, the second curved surface image belongs to the tire database and is a curved surface image of the tire obtained under different tire pressures, different vehicle speeds and different loads, so that the load of the tire corresponding to the second curved surface image can be obtained by an inversion algorithm, and since the similarity between the second curved surface image and the first curved surface image of the target tire is the highest, the load of the tire corresponding to the second curved surface image can be determined as the weight borne by the target tire in the actual traveling process.
Optionally, determining the overweight detection result of the vehicle according to the load of the tire, the number of axles of the vehicle and the number of tires on the axle where the target tire is located corresponding to the second curved surface image comprises:
calculating the actual load of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axles where the target tires are located;
if the actual load of the vehicle is less than or equal to the rated load of the vehicle, determining that the overweight detection result of the vehicle is not overweight;
and if the actual load of the vehicle is greater than the rated load of the vehicle, determining that the overweight detection result of the vehicle is overweight.
In the embodiment of the present application, the load of the tire corresponding to the second curved surface image refers to the weight borne by the target tire during actual traveling. According to the weight born by the target tire in the actual running process, the number of axles of the vehicle and the number of tires on the axle where the target tire is located, the load (namely the actual load) of the vehicle in the actual running process can be calculated, after the actual load of the vehicle is calculated, the actual load is compared with the rated load of the vehicle, and the overweight detection result of the vehicle can be determined.
The rated load of the vehicle can be obtained from the vehicle information database, specifically, the vehicle model can be obtained through the terminal device, and the rated load corresponding to the vehicle model is obtained from the vehicle information database after the vehicle model is obtained.
For example, taking the vehicle D and the vehicle E traveling on the road as an example, the calculation process of the actual load of the vehicle is described:
the vehicle D is a two-axle vehicle (such as a minibus, a bus, etc.), and the front axle of the vehicle D is provided with two tires, and the rear axle is provided with four tires. Firstly, two target tires are obtained, wherein the two target tires are respectively the front axle tire and the rear axle tire which are positioned on the same side with the vehicle overweight detection device; secondly, a curved surface image 1 (namely, a first curved surface image of a target tire on the front axle) and a curved surface image 2 (namely, a first curved surface image of a target tire on the rear axle) can be obtained from a tire database through a similarity calculation method, the actual load a of the target tire on the front axle can be determined according to the curved surface image 1, the actual load b of the target tire on the rear axle can be determined according to the curved surface image 2, and the actual load of the vehicle D can be determined to be 2a +4b because the front axle has two tires and the rear axle has four tires.
Vehicle E is a three-axle vehicle (e.g., truck) with two tires on the front axle, four tires on the center axle, and four tires on the rear axle. Firstly, three target tires are obtained, the method is the same as that of the vehicle D, the three target tires are respectively tires which are positioned on the same side with the vehicle overweight detection device in the front axle tire, the middle axle tire and the rear axle tire, and because the middle axle and the rear axle are respectively provided with four tires and two tires which are positioned on the same side with the vehicle overweight detection device are respectively provided with two tires, any one of the two tires which are positioned on the same side with the vehicle overweight detection device on the middle axle and the rear axle can be obtained to be used as the target tire on the middle axle and the rear axle; secondly, a curved surface image 3 (namely, a first curved surface image of a target tire on the front axle), a curved surface image 4 (namely, a first curved surface image of a target tire on the middle axle) and a curved surface image 5 (namely, a first curved surface image of a target tire on the rear axle) can be obtained from a tire database through a similarity calculation method, the actual load c of the target tire on the front axle can be determined according to the curved surface image 3, the actual load d of the target tire on the middle axle can be determined according to the curved surface image 4, and the actual load E of the target tire on the rear axle can be determined according to the curved surface image 5.
In the embodiment of the application, the similarity between the first curved surface image and the curved surface in the tire database is calculated by acquiring the first curved surface image of the target tire on each axle of the vehicle, and the image with the maximum similarity to the first curved surface image is acquired in the tire database as the second curved surface image. Because the second curved surface image comprises the load of the corresponding tire, whether the vehicle is overweight can be detected according to the load of the tire corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
Referring to fig. 4, a flowchart of a vehicle overweight detection method provided by the second embodiment of the present application is shown, and as shown in fig. 4, the vehicle overweight detection method may include the following steps:
step 401, obtaining the number of axles of the vehicle, the first curved surface image of the target tire on each axle and the number of tires on the axle where the target tire is located.
Step 401 of this embodiment is similar to step 101 of the previous embodiment, and reference may be made to this embodiment, which is not described herein again.
And 402, determining the tire size of the target tire and the deformation parameter of the target tire according to the first curved surface image.
The tire size can refer to the tire width and the tire thickness, and the deformation parameter can refer to the tire bottom subsidence, the tire side expansion and the expansion curve.
In the embodiment of the present application, the tire size of the target tire can be extracted from the first curved surface image by performing three-dimensional image feature extraction on the first curved surface image. According to the three-dimensional data information contained in the first curved surface image, the deformation parameters of the target tire can be obtained.
At step 403, a first candidate surface image is determined from the tire database.
And the tire size corresponding to the first candidate curved surface image is the same as that of the target tire.
In the embodiment of the present application, in order to reduce the amount of similarity calculation in the method for detecting an overweight condition of a vehicle, the curved surface image having the same size as the target tire may be obtained from the tire database, and then step 404 is performed.
The process of obtaining the curved surface image with the same size as the target tire from the tire database is as follows:
and comparing the width and the thickness of the tire extracted from the first curved surface image with the width and the thickness of the tire corresponding to each curved surface image in the tire database, acquiring a curved surface image with the same size as the tire corresponding to the first curved surface image, and determining the curved surface image as a first candidate curved surface image.
It should be understood that the number of first candidate surface images determined from the tire database is at least one.
And step 404, determining the similarity between the first curved surface image and the first candidate curved surface image according to the deformation parameter of the target tire and the deformation parameter of the tire corresponding to the first candidate curved surface image.
Under the condition that the size of the curved surface image is the same as that of the target tire, the deformation parameter of the target tire is compared with the deformation parameter of the tire corresponding to the first candidate curved surface image, the deformation similarity between the target tire and the tire is calculated, and when the deformation similarity between the target tire and the tire is calculated, because the first candidate images (the number of the first candidate images is smaller than that of the curved surface images in the tire database) are already selected from the tire database in the step 403, the calculation amount of the deformation similarity is reduced, and the operation speed of the terminal device is increased.
In the embodiment of the present application, determining the similarity of deformation between the first curved surface image and the first candidate curved surface image based on the artificial neural network may be adopted. Wherein, the artificial neural network is a trained artificial neural network.
And taking the deformation parameter of the target tire and the reference deformation parameter of the first candidate curved surface image as the input of the first artificial neural network, and outputting the reference deformation parameter of the first candidate curved surface image and the deformation similarity between the reference deformation parameter of the first candidate curved surface image and the deformation parameter of the target tire through the convolution operation of the first artificial neural network. The terminal equipment obtains a reference deformation parameter output by the artificial neural network and a deformation similarity between the reference deformation parameter and the deformation parameter of the target tire, wherein the deformation similarity is the similarity between the first curved surface image and the first candidate curved surface image. Optionally, determining the similarity between the first curved surface image and the first candidate curved surface image according to the similarity of the deformation includes:
determining a second candidate curved surface image from the at least one first candidate curved surface image, wherein the second candidate curved surface image is the first candidate curved surface image with the deformation similarity larger than the similarity threshold;
acquiring the curved surface similarity of the first curved surface image and the second candidate curved surface image;
and determining the similarity of the curved surface as the similarity of the first curved surface image and the first candidate curved surface image.
In this embodiment of the application, the deformation similarity between the first curved surface image and the first candidate curved surface image can be obtained through the first artificial neural network, the terminal device can obtain the deformation similarity, and the reference deformation parameter of the first candidate curved surface image corresponding to the deformation similarity, after the terminal device obtains the deformation similarity and the reference deformation parameter of the first candidate curved surface image corresponding to the deformation similarity, the deformation similarity is compared with the similarity threshold, the first deformation similarity with the deformation similarity greater than the similarity threshold is determined, and the reference deformation parameter corresponding to the first deformation similarity is obtained. And finally, according to the reference deformation parameter of the first deformation similarity, determining the curved surface image corresponding to the reference deformation parameter as a second candidate curved surface image.
The second candidate curved surface images are screened from the first candidate curved surface images, and the number of the second candidate curved surface images is at least one.
In the embodiment of the present application, after the second candidate curved surface image is obtained, a different label (for example, a number) is automatically set for the second candidate curved surface image. And taking the second candidate curved surface image and the first curved surface image as the input of a second artificial neural network, and outputting the curved surface similarity of the second candidate curved surface image and the first curved surface image and the label of the second candidate curved surface image corresponding to the curved surface similarity through the convolution operation of the second artificial neural network. The terminal equipment obtains the curved surface similarity between the second candidate curved surface image and the first curved surface image and the label of the second candidate curved surface image corresponding to the curved surface similarity, and determines that the curved surface similarity is the similarity between the first curved surface image and the first candidate curved surface image.
It should be understood that the convolution operation of the first artificial neural network is to calculate deformation similarity, and the convolution operation of the second artificial neural network is to calculate surface similarity, wherein the surface similarity refers to similarity between three-dimensional surface images.
It should also be understood that other similarity algorithms may be employed in performing the similarity calculation, not limited to artificial neural networks.
And step 405, acquiring a curved surface image with the maximum similarity to the first curved surface image from the tire database as a second curved surface image.
The tire database comprises finite element models of common tires (namely finite element models established according to information such as material, width and thickness of the tires), and finite element analysis is carried out on the tire models of different specifications and models to obtain curved surface images of the tires of different tire pressures, different vehicle speeds and different loads.
In the embodiment of the present application, according to the similarity calculated in step 404, a curved surface image with the largest similarity to the first curved surface may be obtained from the curved surface images in the tire database, the curved surface image with the largest similarity may be determined as the second curved surface image,
it should be understood that since the second curved surface image has the greatest similarity to the first curved surface image, the tire pressure, the vehicle speed, and the load of the vehicle derived from the second curved surface image may be regarded as the vehicle speed, the tire pressure of the target tire, and the load of the vehicle during actual running.
And step 406, determining an overweight detection result of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axles where the target tires are located.
Step 406 of this embodiment is similar to step 103 of the previous embodiment, and reference may be made to this embodiment, which is not described herein again.
Step 407, if the vehicle is overweight, determining overweight information of the vehicle and outputting the overweight information.
The overweight information of the vehicle comprises an overweight detection result, excess weight, overweight percentage, vehicle model and license plate number. The excess weight is the difference between the actual load and the rated load of the vehicle. The overweight percentage refers to the percentage of the difference between the actual load and the rated load in the rated load.
It should be noted that, the embodiment of the present application may display the overweight information in real time, or may display the overweight information when the overweight percentage reaches a preset percentage (e.g., 50%), which is not limited herein. Because the actual load of the target tire on the axle is equal to the actual load of other tires on the same axle, and a certain error exists, the overweight information is displayed when the overweight percentage reaches the preset percentage, so that the extremely overweight (for example, overweight by more than 50%) vehicle can be detected, the occupation of the running memory of the terminal equipment can be reduced, and the running speed of the terminal equipment is accelerated.
It should be further noted that the terminal device may output the overweight information to its own display screen, and display the overweight information on the display screen; the overweight information can also be output to an additional display, and the display displays the overweight information after receiving the overweight information.
Compared with the first embodiment, in the first embodiment, the first candidate curved surface image with the same size as the target tire is screened from the tire database according to the tire size of the target tire, and then the similarity between the deformation parameter of the first candidate curved surface image and the deformation parameter of the target tire is compared, so that the calculation amount of the similarity can be reduced, and the system operation speed is increased. And after the deformation similarity of the first candidate curved surface image and the first curved surface image is calculated, determining the curved surface image with the deformation similarity larger than the similarity threshold as a second candidate curved surface image, comparing the curved surface similarity of the second candidate curved surface image with the curved surface similarity of the first curved surface image, obtaining the second candidate curved surface image with the maximum curved surface similarity as the second curved surface image, and using the second curved surface image in the tire database as the image which is most matched with the first curved surface image.
Referring to fig. 5, a schematic structural diagram of a vehicle overweight detection device provided in the third embodiment of the present application is shown, and for convenience of description, only the parts related to the third embodiment of the present application are shown, and the vehicle overweight detection device may specifically include the following modules.
The acquiring module 501 is configured to acquire the number of axles of a vehicle, a first curved surface image of a target tire on each axle, and the number of tires on the axle where the target tire is located;
a similarity determining module 502, configured to obtain similarity between the first curved surface image and a curved surface image in a tire database, and determine that the curved surface image with the greatest similarity to the first curved surface image in the tire database is a second curved surface image, where the tire database includes curved surface images of tires under different loads;
the result determining module 503 determines the overweight detection result of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
In an embodiment of the present application, the vehicle overweight detection device further comprises:
the parameter determining module is used for determining the tire size of the target tire and the deformation parameter of the target tire according to the first curved surface image;
and the size determining module is used for determining a first candidate curved surface image from the tire database, wherein the tire size corresponding to the first candidate curved surface image is the same as the tire size of the target tire.
In this embodiment of the present application, the similarity determining module 502 may specifically include the following sub-modules:
and the similarity determining submodule is used for determining the similarity between the first curved surface image and the first candidate curved surface image according to the deformation parameter of the target tire and the deformation parameter of the tire corresponding to the first candidate curved surface image.
In this embodiment of the present application, the similarity determination sub-module may specifically include the following units:
the deformation acquisition unit is used for acquiring deformation similarity, wherein the deformation similarity refers to the similarity between the deformation parameter of the target tire and the deformation parameter of the tire corresponding to the first candidate curved surface image;
and the deformation determining unit is used for determining the similarity between the first curved surface image and the first candidate curved surface image according to the deformation similarity.
In this embodiment of the present application, the deformation determining unit may be specifically configured to:
and determining the deformation similarity as the similarity of the first curved surface image and the first candidate curved surface image.
In this embodiment of the application, the deformation determining unit may be further specifically configured to:
determining a second candidate curved surface image from the at least one first candidate curved surface image, wherein the second candidate curved surface image is the first candidate curved surface image with the deformation similarity larger than the similarity threshold;
acquiring the curved surface similarity of the first curved surface image and the second candidate curved surface image;
and determining the similarity of the curved surface as the similarity of the first curved surface image and the first candidate curved surface image.
In this embodiment of the present application, the result determining module 503 may specifically include the following sub-modules:
the load calculation submodule is used for calculating the actual load of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axles where the target tires are located;
the non-overweight determination submodule is used for determining that the overweight detection result of the vehicle is not overweight if the actual load of the vehicle is less than or equal to the rated load of the vehicle;
and the overweight determining submodule is used for determining that the overweight detection result of the vehicle is overweight if the actual load of the vehicle is greater than the rated load of the vehicle.
In an embodiment of the present application, the vehicle overweight detection device further includes:
and the overweight display module is used for determining the overweight information of the vehicle and outputting the overweight information if the vehicle is overweight.
The vehicle overweight detection method provided by the embodiment of the application can be applied to the method embodiments, and details are referred to the description of the method embodiments and are not repeated herein.
Referring to fig. 6, a schematic structural diagram of a vehicle overweight detection system provided in the fourth embodiment of the present application is shown, and for convenience of description, only the parts related to the embodiment of the present application are shown, and the vehicle overweight detection system includes the following structures:
the vehicle overweight detection system comprises image acquisition equipment, vehicle model identification equipment and a vehicle overweight detection device provided in the third embodiment of the application, wherein the image acquisition equipment and the vehicle model identification equipment are respectively connected with the vehicle overweight detection device.
The image acquisition equipment is used for acquiring a first curved surface image of a target tire on each axle of the vehicle and transmitting the first curved surface image to the vehicle overweight detection device, wherein the image acquisition equipment can be a three-dimensional laser scanner or a depth camera and can also adopt other equipment capable of acquiring three-dimensional images.
And the vehicle model identification module is used for identifying the number of axles of the vehicle and the number of tires on each axle of the vehicle, and transmitting the number of axles of the vehicle and the number of tires on each axle of the vehicle to the vehicle overweight detection device. The vehicle model identification module comprises but is not limited to an image acquisition module and an image processing module, the image acquisition module is used for acquiring images of the vehicle, and the image processing module is used for analyzing the images of the vehicle and identifying the number of axles of the vehicle and the number of tires on each axle of the vehicle.
The vehicle overweight detection device performs data processing according to the first curved surface image acquired by the image acquisition device, the number of axles of the vehicle and the number of tires on each axle of the vehicle, which are identified by the vehicle model identification module, and the vehicle overweight detection method corresponding to the software program in the vehicle overweight detection device is described in detail in the embodiments of the vehicle overweight detection device and the embodiments of the vehicle overweight detection method, and the embodiments are not specifically described again.
Optionally, the vehicle overweight detection system further comprises: display, speed measuring equipment, dead lever, base.
And the display is connected with the vehicle overweight detection device and used for displaying the overweight information of the vehicle transmitted by the vehicle overweight detection device, wherein the overweight information comprises an overweight detection result, the exceeding weight, the overweight percentage, the vehicle model and the license plate number. The type and the license plate number of the vehicle are identified by the vehicle type identification module, the display also comprises a plurality of types, and in practical application, the display has a plurality of names, such as: liquid crystal displays, Light-Emitting Diode (LED) display screens, Organic Light-Emitting Diode (OLED) display screens, and the like.
And the speed measuring equipment is used for measuring the speed of the vehicle.
The speed measuring device includes, but is not limited to, a laser speed measuring sensor, a doppler speed measuring instrument, etc. The speed measuring equipment transmits the speed of a vehicle to the vehicle overweight detection device after measuring the speed of the vehicle, and the vehicle overweight detection device can transmit the speed of the vehicle to the display to display after receiving the speed of the vehicle.
And the fixing rod is used for fixing the vehicle overweight detection device, the image acquisition equipment, the vehicle model identification equipment, the display and the speed measurement equipment. The image acquisition equipment is arranged on the fixed rod, and the installation position of the image acquisition equipment can enable the image acquisition equipment to acquire a complete first curved surface image of the target tire; the vehicle model identification equipment is arranged on the fixed rod, and the installation position of the vehicle model identification equipment can enable the vehicle model identification equipment to acquire a complete vehicle image. .
And the base is fixedly connected with the fixed rod and used for arranging the fixed rod at the edge of the road.
Optionally, the vehicle overweight detection device further comprises: an illumination device.
An illumination device for illuminating a road.
The road is illuminated through the illuminating device in application scenes such as night, cloudy days and foggy days, and the detection precision of the vehicle overweight detection system can be improved.
Fig. 7 is a schematic structural diagram of a terminal device according to a fifth embodiment of the present application. As shown in fig. 7, the terminal device 700 of this embodiment includes: at least one processor 710 (only one shown in fig. 7), a memory 720, and a computer program 721 stored in the memory 720 and operable on the at least one processor 710, the processor 710 implementing the steps in any of the various vehicle overweight detection method embodiments described above when executing the computer program 721.
The terminal device 700 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 710, a memory 720. Those skilled in the art will appreciate that fig. 7 is merely an example of the terminal device 700, and does not constitute a limitation of the terminal device 700, and may include more or less components than those shown, or combine some of the components, or different components, such as an input-output device, a network access device, etc.
The Processor 710 may be a Central Processing Unit (CPU), and the Processor 710 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 720 may in some embodiments be an internal storage unit of the terminal device 600, such as a hard disk or a memory of the terminal device 700. The memory 720 may also be an external storage device of the terminal device 700 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 700. Further, the memory 720 may also include both an internal storage unit and an external storage device of the terminal device 700. The memory 720 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 720 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the above-described terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
When the computer program product runs on a terminal device, the terminal device can implement the steps in the method embodiments.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (10)

1. A vehicle overweight detection method, characterized in that the vehicle overweight detection method comprises:
acquiring the number of axles of a vehicle, a first curved surface image of a target tire on each axle and the number of tires on the axle where the target tire is located;
acquiring the similarity between the first curved surface image and a curved surface image in a tire database, and determining that the curved surface image with the maximum similarity to the first curved surface image in the tire database is a second curved surface image, wherein the tire database comprises curved surface images of tires under different loads;
and determining an overweight detection result of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
2. The vehicle overweight detection method of claim 1 wherein prior to obtaining the similarity of the first curved image to curved images in a tire database, further comprising:
determining the tire size of the target tire and the deformation parameter of the target tire according to the first curved surface image;
determining a first candidate curved surface image from the tire database, wherein the tire size corresponding to the first candidate curved surface image is the same as the tire size of the target tire;
the obtaining of the similarity between the first curved surface image and the curved surface image in the tire database includes:
and determining the similarity between the first curved surface image and the first candidate curved surface image according to the deformation parameter of the target tire and the deformation parameter of the tire corresponding to the first candidate curved surface image.
3. The vehicle overweight detection method of claim 2 wherein the determining the similarity of the first curved image and the first candidate curved image based on the deformation parameter of the target tire and the deformation parameter of the tire corresponding to the first candidate curved image comprises:
obtaining deformation similarity, wherein the deformation similarity refers to the similarity between the deformation parameter of the target tire and the deformation parameter of the tire corresponding to the first candidate curved surface image;
and determining the similarity between the first curved surface image and the first candidate curved surface image according to the deformation similarity.
4. The vehicle overweight detection method of claim 3 wherein the determining the similarity of the first curved image and the first candidate curved image based on the deformation similarity comprises:
and determining the deformation similarity as the similarity of the first curved surface image and the first candidate curved surface image.
5. The vehicle overweight detection method of claim 3 wherein the number of the first curved surface image candidates is at least one, and wherein the determining the similarity of the first curved surface image and the first curved surface image candidate based on the deformation similarity comprises:
determining a second candidate curved surface image from at least one first candidate curved surface image, wherein the second candidate curved surface image refers to the first candidate curved surface image with the deformation similarity larger than a similarity threshold value;
acquiring the curved surface similarity of the first curved surface image and the second candidate curved surface image;
and determining the curved surface similarity as the similarity of the first curved surface image and the first candidate curved surface image.
6. The method for detecting the overweight of vehicle according to any of claims 1 to 5, wherein the determining the overweight detection result of the vehicle according to the load of the tire corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located comprises:
calculating the actual load of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axles where the target tires are located;
if the actual load of the vehicle is smaller than or equal to the rated load of the vehicle, determining that the overweight detection result of the vehicle is not overweight;
and if the actual load of the vehicle is greater than the rated load of the vehicle, determining that the overweight detection result of the vehicle is overweight.
7. The vehicle overweight detection method according to any one of claims 1 to 5, further comprising:
and if the vehicle is overweight, determining overweight information of the vehicle and outputting the overweight information.
8. A vehicle overweight detection device, characterized in that the vehicle overweight detection device comprises:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring the number of axles of a vehicle, a first curved surface image of a target tire on each axle and the number of tires on the axle where the target tire is located;
the similarity determining module is used for acquiring the similarity between the first curved surface image and a curved surface image in a tire database, and determining that the curved surface image with the maximum similarity to the first curved surface image in the tire database is a second curved surface image, wherein the tire database comprises curved surface images of tires under different loads;
and the result determining module is used for determining the overweight detection result of the vehicle according to the load of the tire corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
9. A vehicle overweight detection system, characterized in that the vehicle overweight detection system comprises an image acquisition device, a vehicle model identification device and the vehicle overweight detection device as claimed in claim 8, the image acquisition device and the vehicle model identification device are respectively connected with the vehicle overweight detection device;
the image acquisition equipment is used for acquiring a first curved surface image of a target tire on each axle of the vehicle and transmitting the first curved surface image to the vehicle overweight detection device;
the vehicle model identification device is used for identifying the number of axles of the vehicle and the number of tires on each axle of the vehicle and transmitting the number of axles of the vehicle and the number of tires on each axle of the vehicle to the vehicle overweight detection device;
the vehicle overweight detection device is used for acquiring the similarity between the first curved surface image and a curved surface image in a tire database, and determining the curved surface image with the maximum similarity to the first curved surface image in the tire database as a second curved surface image, wherein the tire database comprises the curved surface images of tires under different loads; and determining an overweight detection result of the vehicle according to the load of the tires corresponding to the second curved surface image, the number of axles of the vehicle and the number of tires on the axle where the target tire is located.
10. A terminal device comprising a processor and a computer program operable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1 to 7.
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