CN116147745B - Vehicle overload detection method, device, storage medium and electronic device - Google Patents
Vehicle overload detection method, device, storage medium and electronic device Download PDFInfo
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- CN116147745B CN116147745B CN202211733315.8A CN202211733315A CN116147745B CN 116147745 B CN116147745 B CN 116147745B CN 202211733315 A CN202211733315 A CN 202211733315A CN 116147745 B CN116147745 B CN 116147745B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
- G01G19/03—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing during motion
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
本申请公开了一种车辆超限检测方法、装置、存储介质和电子装置，其中，上述方法包括：通过设置在路面下方的路面传感器获取经过路面的车辆的压力信息，并基于压力信息确定车辆的第一车轴信息；通过设置在路侧的非接触传感器获取车辆外廓信息，并根据车辆外廓信息确定车辆的第二车轴信息；根据第一车轴信息和第二车轴信息判断车辆是否存在悬浮轴，得到悬浮轴判断结果；依据悬浮轴判断结果和车辆的车辆重量判断车辆是否超限。
The present application discloses a vehicle overload detection method, device, storage medium and electronic device, wherein the above method includes: obtaining pressure information of a vehicle passing through a road surface through a road surface sensor arranged under the road surface, and determining the first axle information of the vehicle based on the pressure information; obtaining the vehicle outline information through a non-contact sensor arranged on the side of the road, and determining the second axle information of the vehicle based on the vehicle outline information; judging whether the vehicle has a suspended axle based on the first axle information and the second axle information, and obtaining a suspended axle judgment result; judging whether the vehicle is overloaded based on the suspended axle judgment result and the vehicle weight.
Description
Technical Field
The present application relates to the field of vehicle identification, and in particular, to a vehicle overrun detection method, device, storage medium and electronic device.
Background
The suspension shaft is a suspension shaft which can be lifted and lowered at will by one or more air bags or hydraulic control in the truck with multiple load axles. In order to reduce energy consumption and reduce wear of tires when a vehicle with a suspended axle is traveling empty, the suspended axle may be lifted. When the goods are loaded, the pressure of the goods on the road surface can be relieved by putting down the suspension shaft.
In vehicle ultra-treatment applications, corresponding ultra-limit standards and charging standards are set for the axle type. The maximum weight that can be carried by the vehicle is related to the number of axles and the axle type, for example, each additional double-tire axle increases, the upper limit that can be carried by the vehicle correspondingly increases, and in addition, if the suspension axle is positioned at the position of the triple-axle or double-axle part, the upper limit that can be carried also has different lifting, for example, the maximum carrying mass of the double-axle double-tire is 18t, and the maximum carrying mass of the triple-axle double-tire is 22t, therefore, the specific information of the suspension axle needs to be considered in the vehicle super-treatment application.
Disclosure of Invention
The embodiment of the application provides a vehicle overrun detection method, a device, a storage medium and an electronic device, which at least solve the technical problem that whether a suspended shaft is used by a vehicle or not cannot be accurately identified due to the fact that the suspended shaft is suspended by the vehicle in the related art, so that whether overrun behavior of the vehicle exists or not is difficult to judge.
According to one aspect of the embodiment of the application, a vehicle overrun detection method is provided, and the vehicle overrun detection method comprises the steps of obtaining pressure information of a vehicle passing through a road surface sensor arranged below the road surface, determining first axle information of the vehicle based on the pressure information, obtaining vehicle outline information through a non-contact sensor arranged on a road side, determining second axle information of the vehicle according to the vehicle outline information, judging whether a suspended axle exists in the vehicle according to the first axle information and the second axle information, obtaining a suspended axle judging result, and judging whether the vehicle overrun according to the suspended axle judging result and the vehicle weight of the vehicle.
The method comprises the steps of calculating the vehicle weight of the vehicle based on pressure information, determining the suspension axle position of the vehicle according to the pressure information and vehicle outline information when the suspension axle judging result indicates that the suspension axle exists in the vehicle, judging whether the vehicle is overrun or not based on the suspension axle position and the vehicle weight of the vehicle, judging the contact state of each axle and a road surface according to the pressure information of each axle in the suspension axle judging result when the suspension axle judging result indicates that the suspension axle does not exist in the vehicle, wherein the contact state comprises a full contact state and a half contact state, the half contact state is the state that the pressure information of the axle does not reach an upper threshold, the upper threshold is the minimum value of the pressure information of the axle determined by a road surface sensor, and judging whether the vehicle is overrun or not based on the position of the axle in the half contact state and the vehicle weight when the axle is in the half contact state.
Optionally, the method further comprises the steps of correcting the weight of the vehicle based on all pressure information corresponding to the axles in the semi-contact state and effective pressure information corresponding to other axles when the axles are in the semi-contact state, and judging whether overrun behavior exists according to the corrected weight of the vehicle.
Optionally, the non-contact sensor comprises any one of a laser sensor and an image sensor, the vehicle contour information comprises vehicle point cloud data or vehicle image data, the vehicle point cloud data is acquired by the laser sensor in the case that the non-contact sensor is the laser sensor, and the vehicle image data is acquired by the image sensor in the case that the non-contact sensor is the image sensor.
Optionally, determining the first axle information of the vehicle based on the pressure information comprises generating a pressure change graph of the road surface sensor in a target period according to the pressure information, determining a plurality of peaks in the pressure change graph, determining the time corresponding to each peak, determining the peak as a target peak corresponding to the passing of the axle when the peak is larger than or equal to a nominal threshold value, and determining the first axle information according to the number of the target peaks, the difference value of the time corresponding to the adjacent target peaks and the vehicle speed.
Optionally, under the condition that the non-contact sensor is an image sensor, determining second vehicle axis information of the vehicle according to vehicle outline information comprises the steps of extracting feature data of each frame of image in multiple frames of images of the vehicle to obtain multiple feature data, calculating mutual information of any two of the multiple feature data through a mutual information registration algorithm to obtain multiple mutual information, carrying out image registration on the multiple frames of images according to the multiple mutual information to obtain a target image, inputting the target image into a preset neural network model to obtain the second vehicle axis information, wherein the preset neural network model is trained by multiple groups of training samples, and each group of training samples comprises a historical vehicle image and axle information.
The method comprises the steps of judging whether a vehicle has a suspension shaft according to first axle information and second axle information, determining whether the vehicle does not have the suspension shaft under the condition that the first axle information and the second axle information are identical, determining that the vehicle has the suspension shaft under the condition that the first axle information and the second axle information are different, and determining the shaft sequence number of the suspension shaft according to the first axle information and the second axle information, wherein the shaft sequence number is the position information of the suspension shaft installed on the vehicle.
According to another aspect of the embodiment of the application, there is also provided a vehicle overrun detecting device, which includes a first acquiring unit configured to acquire pressure information of a vehicle passing through a road surface sensor provided below the road surface and determine first axle information of the vehicle based on the pressure information, a second acquiring unit configured to acquire vehicle profile information through a non-contact sensor provided on a road side and determine second axle information of the vehicle based on the vehicle profile information, a first judging unit configured to judge whether a suspended axle exists in the vehicle based on the first axle information and the second axle information, to obtain a suspended axle judging result, and a second judging unit configured to judge whether the vehicle overrun based on the suspended axle judging result and a vehicle weight of the vehicle.
According to yet another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to perform the above-described vehicle overrun detecting method when run.
According to still another aspect of the embodiment of the present application, there is further provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the above-mentioned vehicle overrun detecting method through the computer program.
The method comprises the steps of obtaining pressure information of a vehicle passing through a road surface sensor arranged below the road surface, determining first axle information of the vehicle based on the pressure information, obtaining outline information of the vehicle through a non-contact sensor arranged on a road side, determining second axle information of the vehicle according to the outline information of the vehicle, judging whether a suspension axle exists in the vehicle according to the first axle information and the second axle information to obtain a suspension axle judging result, judging whether the vehicle is out of limit according to the suspension axle judging result and the vehicle weight of the vehicle, and accurately identifying whether the suspension axle is used by the vehicle to determine whether the vehicle is out of limit or not.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic diagram of a hardware environment of an alternative vehicle overrun detection method according to an embodiment of the present application;
FIG. 2 is a flow chart of an alternative vehicle overrun detection method according to an embodiment of the present application;
FIG. 3 is an alternative pressure variation graph according to an embodiment of the present application;
FIG. 4 is a block diagram of an alternative vehicle overrun detection device according to an embodiment of the present application;
fig. 5 is a block diagram of an alternative electronic device according to an embodiment of the application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an aspect of an embodiment of the present application, a vehicle overrun detection method is provided. Alternatively, in the present embodiment, the above-described vehicle overrun detecting method may be applied to a hardware environment including the detecting section 102 and the data processor 104 as shown in fig. 1. As shown in fig. 1, the data processor 104 is connected to the detecting unit 102 through a network, and may be used for identifying vehicle information based on the detection data of the detecting unit 102, for example, identifying contour information of a vehicle, weight information of the vehicle, etc., and a data storage unit (data may be stored through a database) may be provided on the data processor or separately from the data processor, for providing a data storage service for the data processor 104. Here, the detection section 102 and the data processor 104 may both belong to a vehicle information detection system.
The network may include, but is not limited to, at least one of a wired network and a wireless network. The wired network may include, but is not limited to, at least one of a wide area network, a metropolitan area network, and a local area network, and the wireless network may include, but is not limited to, at least one of WIFI (WIRELESS FIDELITY ), bluetooth. In addition to being connected via a network, the detection component 102 and the data processor 104 can also be connected via a network cable or serial port. The detection component 102 may include two lateral scanning components, a longitudinal scanning component, and a row of load cells, wherein the lateral scanning components and the longitudinal scanning component may be, but are not limited to, scanned laser sensors, and the load cells may include, but are not limited to, narrow strip sensors, and the like.
The vehicle overrun detecting method according to the embodiment of the present application may be executed by the data processor 104, or may be executed by both the data processor 104 and the detecting unit 102. Taking the data processor 104 as an example to execute the vehicle overrun detecting method in this embodiment, fig. 2 is a schematic flow chart of an alternative vehicle overrun detecting method according to an embodiment of the present application, as shown in fig. 2, the flow chart of the method may include the following steps:
Step S202, pressure information of a vehicle passing through a road surface is acquired by a road surface sensor provided below the road surface, and first axle information of the vehicle is determined based on the pressure information.
Specifically, the road surface sensor may be a strain type pressure sensor, and the strain type pressure sensor is used for collecting deformation information of the road surface when the vehicle passes over the strain type pressure sensor, so as to generate pressure information according to the deformation information. The strain type pressure sensor is a sensor based on measuring the strain generated by the stressed deformation of an object, the strain change is converted into resistance change by utilizing a resistance strain gauge, and the pressure change value acquired by the pressure sensor is output through the resistance change. The stress information of rolling of the vehicle above the road surface is sensed in real time through the strain sensor, so that axle type information, axle base information and axle number of the vehicle passing above the road surface, namely first axle information, are calculated.
Step S204, acquiring vehicle profile information by a non-contact sensor disposed on the road side, and determining second axle information of the vehicle according to the vehicle profile information.
Specifically, the non-contact sensor may be a laser sensor or an image sensor, when the non-contact sensor is an image sensor, the image sensor is installed beside a road surface, when a vehicle passes through the road surface, the image sensor is used for shooting multiple frames of pictures of the vehicle, each collected frame of pictures needs to contain an axle part of the vehicle, and as one frame of pictures cannot contain all axles of the vehicle, the multiple frames of pictures of all axle parts of the collected vehicle need to be subjected to image registration, so that a target picture containing all axles of the vehicle is obtained. The position information of each axle of the vehicle in the target picture is automatically identified by inputting the target picture into a pre-trained deep learning model, and the axle type information, the axle base information and the axle number of the vehicle, namely second axle information, are output.
Step S206, judging whether a suspension shaft exists in the vehicle according to the first vehicle shaft information and the second vehicle shaft information, and obtaining a suspension shaft judging result.
Specifically, the first axle information acquired through the road surface sensor is compared with the second axle information acquired through the non-contact sensor, whether the first axle information is identical with the second axle information is judged, if the comparison result shows that the first axle information is identical with the second axle information, the vehicle is not used for the suspension axle, and if the comparison result shows that the first axle information is different from the second axle information, the vehicle is used for the suspension axle.
Step S208, judging whether the vehicle is overrun or not according to the suspension shaft judging result and the vehicle weight of the vehicle.
Specifically, the upper load bearing limit of a vehicle using a suspension shaft is different from the upper load bearing limit of a vehicle not using a suspension shaft, and a suspension shaft may be used for some vehicles for fuel saving in transportation, however, there may be an overrun situation in which these vehicles use a suspension shaft. Therefore, the current upper bearing limit of the vehicle is determined according to the suspension shaft judgment result of the vehicle, and whether the vehicle exceeds the limit is judged according to the current upper bearing limit and the vehicle weight.
In this embodiment, the axle information may include the number of axles, and may further include an axle type and an axle distance, and may be specifically set according to actual needs. The method comprises the steps of S202 to S208, acquiring pressure information of a vehicle passing through a road surface sensor arranged below the road surface, determining first axle information of the vehicle based on the pressure information, acquiring vehicle outline information through a non-contact sensor arranged on a road side, determining second axle information of the vehicle according to the vehicle outline information, judging whether a suspension axle exists in the vehicle according to the first axle information and the second axle information to obtain a suspension axle judging result, judging whether the vehicle is overrun according to the suspension axle judging result and the vehicle weight of the vehicle, and solving the problem that whether the suspension axle is used by the vehicle cannot be accurately identified due to suspension of the suspension axle in the prior art, so that whether the suspension axle is overrun is used by the vehicle cannot be accurately identified, and achieving the technical effect of accurately identifying whether the suspension axle is used by the vehicle or not, thereby judging whether the suspension axle is overrun or not.
In one exemplary embodiment, optionally, judging whether the vehicle is overrun according to a suspension shaft judging result and the vehicle weight of the vehicle comprises calculating the vehicle weight of the vehicle based on pressure information, determining the suspension shaft position of the vehicle according to the pressure information and vehicle outline information when the suspension shaft judging result represents that the suspension shaft exists in the vehicle, judging whether the vehicle is overrun according to the suspension shaft position and the vehicle weight of the vehicle, judging the contact state of the axle and the road surface according to the pressure information of each axle in the suspension shaft judging result when the suspension shaft judging result represents that the suspension shaft does not exist in the vehicle, wherein the contact state comprises a full contact state and a half contact state, the half contact state is that the pressure information of the axle does not reach an upper threshold, the upper threshold is a minimum value of the pressure information of the axle determined by a road surface sensor, and judging whether the vehicle is overrun based on the position of the axle in the half contact state and the vehicle weight of the vehicle when the axle exists in the half contact state.
Specifically, the pressure information of each axle of the vehicle acquired by the road surface sensor calculates the vehicle weight. The maximum weight that can be borne by the vehicle is linearly related to the number of axles, and the upper limit that can be borne by the vehicle is correspondingly increased when one double-tire axle is added, and if the suspension axle is positioned at the position of the three-axle or the double-axle, the upper limit that can be borne is also lifted differently, for example, the maximum bearing mass of the double-axle double-tire is 18t, and the maximum bearing mass of the three-axle double-tire is 22t. It is therefore necessary to determine whether the vehicle is overrun by determining the upper load bearing limit of the vehicle by whether the vehicle is using the suspension axle and the suspension axle position and then determining whether the vehicle weight exceeds the upper load bearing limit.
Because the existence of the suspension shaft is an important basis for judging the overrun of the vehicle, when the suspension shaft exists, the contact state of the suspension shaft and the road end is the same as the suspension shaft, and the suspension shaft is put down and is in half contact with the road surface, namely the axle and the road surface are in half contact. At this time, it may be shown that there is no suspension axis according to the suspension axis judgment result. In this case, it is necessary to determine the contact state between each axle of the vehicle and the road surface, and if the axle of the vehicle is in the semi-contact state, the weight of the axle in the semi-contact state is not calculated when the weight of the vehicle is calculated according to the pressure information, that is, the weight of the vehicle is not accurate, so that it is necessary to re-correct the weight of the vehicle according to the pressure information of the axle in the semi-contact state, and then determine whether the vehicle is overrun according to the corrected weight of the vehicle and the upper load bearing limit.
In one exemplary embodiment, optionally, the method further comprises correcting the vehicle weight based on all pressure information corresponding to the axle in the semi-contact state and effective pressure information corresponding to other axles when the axle is in the semi-contact state, and judging whether the overrun behavior exists according to the corrected vehicle weight.
It can be understood that when the weight of the vehicle is calculated by using the pressure information, the obtained pressure information is firstly subjected to denoising processing to obtain effective pressure information, and the denoising processing process can generally use the part of all the pressure information, of which the amplitude of the pressure signal is greater than the pressure threshold, as the effective pressure information so as to determine the vehicle data. However, when the vehicle has abnormal driving behavior such as intentional jump of the balance, the tire may not be in full contact with the in-eye sensor, and the pressure data corresponding to the tire is relatively small and cannot be used for calculating the weight of the vehicle, so that the calculated weight of the vehicle deviates from the actual weight of the vehicle. According to the method, the weight of the axle in the half-contact state is calculated according to the corresponding pressure information of the vehicle in the half-contact state, the weight of the axle in the half-contact state is added to the total weight of the axle in the full-contact state, and the corrected weight of the vehicle is obtained.
In one exemplary embodiment, optionally, the non-contact sensor includes any one of a laser sensor and an image sensor, and the vehicle profile information includes vehicle point cloud data or vehicle image data, the vehicle point cloud data is acquired by the laser sensor in the case where the non-contact sensor is the laser sensor, and the vehicle image data is acquired by the image sensor in the case where the non-contact sensor is the image sensor.
Specifically, the vehicle profile information, that is, the appearance information of the vehicle, is obtained by obtaining the image data of the vehicle to obtain the second axle information of the vehicle. And if the non-contact sensor is a laser sensor, acquiring point cloud data of the vehicle through the laser sensor, and analyzing the point cloud data to obtain second axle information. And if the non-contact sensor is an image sensor, acquiring second vehicle axis information through multiple frames of pictures of the vehicle acquired by the image sensor.
Generating a pressure change map from the pressure information, calculating first axle information from peak characteristics in the pressure change map, and in one exemplary embodiment, optionally, determining the first axle information of the vehicle based on the pressure information includes generating a pressure change map of the road surface sensor in a target period from the pressure information, determining a plurality of peaks in the pressure change map, determining a time corresponding to each peak, determining the peak as a target peak corresponding to the axle passing when the peak is equal to or greater than a nominal threshold, and determining the first axle information from the number of target peaks, a difference between times corresponding to adjacent target peaks, and the vehicle speed.
Specifically, the first axle information may include a first number of axles and a first axle distance, and the target period may be a period of time when the vehicle completely passes through the image capturing device, and fig. 3 is an alternative pressure change chart according to an embodiment of the present application, where, as shown in fig. 3, a horizontal axis in the pressure change chart represents a capturing time of the pressure information, and a vertical axis represents a magnitude of a road surface bearing pressure. The pressure information of the two wheels of the vehicle as they pass over the road is taken in fig. 3. Each wheel is characterized by a wheel axle passing pressure sensor. the dashed lines indicated by t1 and t2, i.e. the position of the target peak, also have smaller peaks in the pressure change diagram, since the road surface itself will also have pressure changes. The pressure change caused by the non-axle passing is excluded by setting a first threshold value. And taking the peak value larger than the first threshold value as a target peak value, wherein the number of the target peak values is the first axle number. And calculating the difference between t1 and t2 to obtain the time difference of the two axles passing through the pressure sensor, and calculating the product of the difference of the moment corresponding to the target peak value and the vehicle speed by combining the running speed of the vehicle to obtain the first axle distance. The first axle information is determined from the pressure change map to provide analytical data for determining whether the vehicle is using the floating axle.
In order to acquire the second axle information, the multi-frame pictures are subjected to image registration to obtain target pictures, and then the second axle information is determined according to the target pictures, alternatively, the second axle information of the vehicle is determined according to the vehicle outline information under the condition that the non-contact sensor is an image sensor, the method comprises the steps of extracting characteristic data of each frame of picture in the multi-frame pictures of the vehicle to obtain a plurality of characteristic data, calculating mutual information of any two of the plurality of characteristic data through a mutual information registration algorithm to obtain a plurality of mutual information, performing image registration on the multi-frame pictures according to the plurality of mutual information to obtain the target pictures, and inputting the target pictures into a preset neural network model to obtain the second axle information, wherein the preset neural network model is obtained by training a plurality of groups of training samples, and each group of training samples comprises a historical vehicle picture and axle information.
Specifically, the second axle information may be a second axle number and a second axle distance, the feature data may be a set of pixel blocks in each frame of picture, and the mutual information refers to a measure of interdependence between two frames of pictures. The image registration refers to a process of matching and overlapping two or more images acquired at different time and under different sensors or under different conditions, a mutual information registration algorithm is that an image registration mode based on mutual information calculates mutual information of any two pixel block sets in a plurality of pixel block sets in a multi-frame picture, the mutual information is used as a matching standard between the images, namely overlapping pixel blocks corresponding to the same axle in the multi-frame picture, and fusing all pixel blocks corresponding to the same vehicle structure to finally obtain a target picture for completely displaying all structural characteristics of the vehicle. And acquiring a target picture containing all axle information of the vehicle through image registration, and acquiring second axle information.
It should be noted that, when comparing the first axle information and the second axle information, it is necessary to determine that the two are the same corresponding vehicle, so when acquiring multiple frames of pictures, it is necessary to acquire pictures in the same time as the target acquisition time period corresponding to the first axle information.
The target neural network model can be a deep learning model, historical vehicle picture data and corresponding vehicle axle numbers and axle distances are used as training sets to be input into the deep learning model for training, and the target neural network model can output the vehicle axle numbers and the axle distances of vehicles in the target picture according to the input target picture. The deep learning model counts second axle information and second axle spacing of the vehicle by identifying the location of the tire in the target image and detecting the location of the tire. And inputting the target picture into a target neural network model to obtain second vehicle axis information and second vehicle axis distance, so as to provide comparison data for judging whether the vehicle uses the suspension axis.
The method comprises the steps of judging whether a vehicle has a suspension shaft according to first axle information and second axle information, determining whether the vehicle does not have the suspension shaft under the condition that the first axle information and the second axle information are identical, determining that the vehicle has the suspension shaft under the condition that the first axle information and the second axle information are different, and determining the shaft sequence number of the suspension shaft according to the first axle information and the second axle information, wherein the shaft sequence number is the position information of the suspension shaft installed on the vehicle.
Specifically, whether the vehicle uses the suspension axle is determined mainly based on the number of axles and the axle spacing. And thus whether the first axle information and the second axle information are identical or not is taken as a comparison result. Under the condition that the vehicle uses the suspension axle, if the vehicle is suspended and retracted, the first axle information detected by the pressure sensor is less than the actual vehicle axle number, and the second axle information is acquired by the image acquisition device to be the complete actual vehicle axle number, so that the first axle information and the second axle information are different. If the vehicle slightly floats the axle, that is, the pressure of the axle using the suspension axle acquired by the pressure sensor is greatly different from that of other axles. And comparing the pressure change patterns of the axles through the pressure sensor, so as to judge whether the vehicle uses the suspension axle or not. If the first axle information and the second axle information are the same, it is indicated that the vehicle does not use the suspension axle. And judging whether the vehicle uses the suspension shaft or not according to the comparison result to improve the overrun detection accuracy of the vehicle.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM (Read-Only Memory)/RAM (Random Access Memory), magnetic disk, optical disk) and including instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method of the embodiments of the present application.
According to another aspect of the embodiment of the present application, there is also provided a vehicle overrun detecting device for implementing the above vehicle overrun detecting method. Fig. 4 is a block diagram of an alternative vehicle overrun detecting device according to an embodiment of the present application, and as shown in fig. 4, the vehicle overrun detecting device may include:
A first acquisition unit 10 for acquiring pressure information of a vehicle passing through a road surface by a road surface sensor provided below the road surface, and determining first axle information of the vehicle based on the pressure information;
A second acquisition unit 20 for acquiring vehicle profile information by a non-contact sensor provided on a road side, and determining second axle information of the vehicle from the vehicle profile information;
a first judging unit 30 for judging whether the vehicle has a suspension axle according to the first axle information and the second axle information, and obtaining a suspension axle judging result;
And a second judging unit 40 for judging whether the vehicle is overrun or not based on the suspension axis judgment result and the vehicle weight of the vehicle.
The vehicle overrun detecting device comprises a first acquiring unit 10, a second acquiring unit 20, a first judging unit 30 and a second judging unit 40, wherein the first acquiring unit acquires pressure information of a vehicle passing through a road surface sensor arranged below the road surface and determines first axle information of the vehicle based on the pressure information, the second acquiring unit 20 acquires vehicle outline information through a non-contact sensor arranged on a road side and determines second axle information of the vehicle according to the vehicle outline information, the first judging unit 30 judges whether the vehicle has a suspension axle according to the first axle information and the second axle information to obtain a suspension axle judging result, and the second judging unit 40 judges whether the vehicle is overrun according to the suspension axle judging result and the vehicle weight of the vehicle.
In one exemplary embodiment, the second judging unit 40 includes a first calculating module for calculating a vehicle weight of the vehicle based on the pressure information, a first determining module for determining a suspension axis position of the vehicle based on the pressure information and the vehicle profile information in a case where the suspension axis judging result characterizes the presence of the suspension axis of the vehicle, and judging whether or not to overrun based on the suspension axis position and the vehicle weight of the vehicle, and a first judging module for judging a contact state of each axle with the road surface based on the pressure information of the axle in the suspension axis judging result in a case where the suspension axis judging result characterizes the absence of the suspension axis of the vehicle, wherein the contact state includes a full contact state and a half contact state, the half contact state being a minimum value of the pressure information of the axle determined as the axle by the road surface sensor, and judging whether or not to overrun based on the position of the axle in the half contact state and the vehicle weight of the vehicle when the axle is in the half contact state is present.
In an exemplary embodiment, optionally, the device further comprises a correction unit, when the axle is in the semi-contact state, the correction unit is used for correcting the weight of the vehicle based on all the pressure information corresponding to the axle in the semi-contact state and the effective pressure information corresponding to other axles, and judging whether overrun behavior exists according to the corrected weight of the vehicle.
In one exemplary embodiment, optionally, the non-contact sensor includes any one of a laser sensor and an image sensor, and the vehicle profile information includes vehicle point cloud data or vehicle image data, the vehicle point cloud data is acquired by the laser sensor in the case where the non-contact sensor is the laser sensor, and the vehicle image data is acquired by the image sensor in the case where the non-contact sensor is the image sensor.
In an exemplary embodiment, the first obtaining unit 10 optionally includes a generating module configured to generate a pressure change chart of the road surface sensor in a target period according to the pressure information, a second determining module configured to determine a plurality of peaks in the pressure change chart and determine a time corresponding to each peak, a third determining module configured to determine the peak as a target peak corresponding to the passing of the axle when the peak is equal to or greater than a nominal threshold, and a fourth determining module configured to determine the first axle information according to the number of target peaks, a difference between times corresponding to adjacent target peaks, and the vehicle speed.
In an exemplary embodiment, optionally, in the case that the non-contact sensor is an image sensor, the second obtaining unit 20 includes an extracting module configured to extract feature data of each frame of image in multiple frames of images of the vehicle to obtain multiple feature data, a second calculating module configured to calculate mutual information of any two feature data in the multiple feature data through a mutual information registration algorithm to obtain multiple mutual information, a registration module configured to perform image registration on the multiple frames of images according to the multiple mutual information to obtain a target image, and an input module configured to input the target image into a preset neural network model to obtain second axle information, where the preset neural network model is obtained by training multiple sets of training samples, and each set of training samples includes one historical vehicle image and axle information.
In an exemplary embodiment, the first judging unit 30 optionally includes a third judging module for judging whether the first axle information and the second axle information are the same, a fifth determining module for determining that the vehicle does not have a suspension axle if the first axle information and the second axle information are the same, and a sixth determining module for determining that the vehicle has a suspension axle if the first axle information and the second axle information are different, and determining an axle number of the suspension axle according to the first axle information and the second axle information, wherein the axle number is position information where the suspension axle is mounted on the vehicle.
It should be noted that the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the above embodiments. It should be noted that the above modules may be implemented in software or in hardware as part of the apparatus shown in fig. 1, where the hardware environment includes a network environment.
According to yet another aspect of an embodiment of the present application, there is also provided a storage medium. Alternatively, in the present embodiment, the above-described storage medium may be used to execute the program code of any one of the above-described vehicle overrun detecting methods in the embodiments of the present application.
Alternatively, in this embodiment, the storage medium may be located on at least one network device of the plurality of network devices in the network shown in the above embodiment.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of:
s1, acquiring pressure information of a vehicle passing through a road surface sensor arranged below the road surface, and determining first axle information of the vehicle based on the pressure information;
s2, acquiring vehicle outline information through a non-contact sensor arranged on a road side, and determining second axle information of the vehicle according to the vehicle outline information;
S3, judging whether a suspension shaft exists in the vehicle according to the first vehicle shaft information and the second vehicle shaft information, and obtaining a suspension shaft judging result;
and S4, judging whether the vehicle is overrun or not according to the suspension shaft judging result and the vehicle weight of the vehicle.
Alternatively, specific examples in the present embodiment may refer to examples described in the above embodiments, which are not described in detail in the present embodiment.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to, a USB flash disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk, etc., which may store the program code.
According to still another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the above-mentioned vehicle overrun detecting method, which may be a server, a terminal, or a combination thereof.
Fig. 5 is a block diagram of an alternative electronic device, according to an embodiment of the present application, including a processor 502, a communication interface 504, a memory 506, and a communication bus 508, as shown in fig. 5, wherein the processor 502, the communication interface 504, and the memory 506 communicate with each other via the communication bus 508, wherein,
A memory 506 for storing a computer program;
the processor 502 is configured to execute the computer program stored in the memory 506, and implement the following steps:
s1, acquiring pressure information of a vehicle passing through a road surface sensor arranged below the road surface, and determining first axle information of the vehicle based on the pressure information;
s2, acquiring vehicle outline information through a non-contact sensor arranged on a road side, and determining second axle information of the vehicle according to the vehicle outline information;
S3, judging whether a suspension shaft exists in the vehicle according to the first vehicle shaft information and the second vehicle shaft information, and obtaining a suspension shaft judging result;
and S4, judging whether the vehicle is overrun or not according to the suspension shaft judging result and the vehicle weight of the vehicle.
Alternatively, the communication bus may be a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, or the like. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus. The communication interface is used for communication between the electronic device and other equipment.
The memory may include RAM or may include non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including but not limited to a CPU (Central Processing Unit ), NP (Network Processor, network processor), DSP (DIGITAL SIGNAL Processing unit), ASIC (Application SPECIFIC INTEGRATED Circuit), FPGA (Field-Programmable gate array) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is only illustrative, and the device implementing the vehicle overrun detecting method may be a terminal device, and the terminal device may be a smart phone (such as an Android Mobile phone, an iOS Mobile phone, etc.), a tablet computer, a palm computer, a Mobile internet device (Mobile INTERNET DEVICES, MID), a PAD, etc. Fig. 5 is not limited to the structure of the electronic device. For example, the electronic device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 5, or have a different configuration than shown in FIG. 5.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute on associated hardware, and the program may be stored in a computer readable storage medium, where the storage medium may include a flash disk, a ROM, a RAM, a magnetic disk, an optical disk, or the like.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
The integrated units in the above embodiments may be stored in the above-described computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the method described in the embodiments of the present application.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In several embodiments provided by the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution provided in the present embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or at least two units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.
Claims (9)
1. A vehicle overrun detection method, characterized by comprising:
Acquiring pressure information of a vehicle passing through a road surface by a road surface sensor arranged below the road surface, and determining first axle information of the vehicle based on the pressure information;
Acquiring vehicle outline information through a non-contact sensor arranged on a road side, and determining second axle information of the vehicle according to the vehicle outline information;
Judging whether a suspension shaft exists in the vehicle according to the first vehicle shaft information and the second vehicle shaft information, and obtaining a suspension shaft judging result;
Judging whether the vehicle is overrun according to the suspension shaft judging result and the vehicle weight of the vehicle, wherein the non-contact sensor comprises any one of a laser sensor and an image sensor, the vehicle outline information comprises vehicle point cloud data or vehicle image data, the vehicle point cloud data are acquired through the laser sensor when the non-contact sensor is the laser sensor, and the vehicle image data are acquired through the image sensor when the non-contact sensor is the image sensor.
2. The method according to claim 1, wherein determining whether the vehicle is overrun based on the suspension axle determination result and a vehicle weight of the vehicle, comprises:
Calculating a vehicle weight of the vehicle based on the pressure information;
Determining a suspension axis position of the vehicle according to the pressure information and the vehicle outline information under the condition that the suspension axis judgment result represents that the vehicle has a suspension axis, and judging whether the suspension axis position and the vehicle weight of the vehicle exceed limits or not based on the suspension axis position and the vehicle weight;
Judging the contact state of each axle and the road surface according to the pressure information of each axle in the suspension axle judging result under the condition that the suspension axle judging result indicates that the vehicle does not have a suspension axle, wherein the contact state comprises a full contact state and a half contact state, the half contact state is that the pressure information of the axle does not reach a threshold, and the threshold is the minimum value of the pressure information of the axle determined by the road surface sensor;
When there is an axle in a half-contact state, it is determined whether or not overrun is exceeded based on the position of the axle in the half-contact state and the vehicle weight of the vehicle.
3. The method as recited in claim 2, further comprising:
When the axle is in the semi-contact state, correcting the weight of the vehicle based on all pressure information corresponding to the axle in the semi-contact state and effective pressure information corresponding to other axles;
and judging whether the overrun behavior exists according to the corrected vehicle weight.
4. The method of claim 1, wherein determining first axle information of the vehicle based on the pressure information comprises:
generating a pressure change chart of the pavement sensor in a target period according to the pressure information;
determining a plurality of peaks in the pressure change graph, and determining the corresponding moment of each peak;
under the condition that the peak value is larger than or equal to the upper threshold value, determining the peak value as a corresponding target peak value when an axle passes;
And determining the first axle information according to the number of the target peaks, the difference value of the moments corresponding to the adjacent target peaks and the vehicle speed.
5. The method according to claim 1, wherein determining the second axle information of the vehicle from the vehicle profile information in the case where the non-contact sensor is an image sensor, comprises:
Extracting characteristic data of each frame of picture in the multi-frame pictures of the vehicle to obtain a plurality of characteristic data;
calculating mutual information of any two feature data in the plurality of feature data through a mutual information registration algorithm to obtain a plurality of pieces of mutual information;
Performing image registration on the multi-frame pictures according to the mutual information to obtain target pictures;
and inputting the target picture into a preset neural network model to obtain the second vehicle axle information, wherein the preset neural network model is trained by a plurality of groups of training samples, and each group of training samples comprises a historical vehicle picture and vehicle axle information.
6. The method of claim 1, wherein determining whether the vehicle has a floating axle based on the first axle information and the second axle information comprises:
judging whether the first axle information and the second axle information are the same;
Determining that the vehicle does not have a suspended axle if the first axle information and the second axle information are the same;
And determining that a suspension shaft exists in the vehicle under the condition that the first vehicle shaft information and the second vehicle shaft information are different, and determining the shaft serial number of the suspension shaft according to the first vehicle shaft information and the second vehicle shaft information, wherein the shaft serial number is the position information of the suspension shaft installed on the vehicle.
7. A vehicle overrun detecting device, characterized by comprising:
A first acquisition unit configured to acquire pressure information of a vehicle passing through a road surface by a road surface sensor provided below the road surface, and determine first axle information of the vehicle based on the pressure information;
a second acquisition unit configured to acquire vehicle profile information by a non-contact sensor provided on a road side, and determine second axle information of the vehicle according to the vehicle profile information;
The first judging unit is used for judging whether a suspension shaft exists in the vehicle according to the first vehicle shaft information and the second vehicle shaft information, and obtaining a suspension shaft judging result;
A second judging unit for judging whether the vehicle is overrun or not according to the suspension shaft judging result and the vehicle weight of the vehicle;
The non-contact sensor comprises any one of a laser sensor and an image sensor, the vehicle outline information comprises vehicle point cloud data or vehicle image data, the vehicle point cloud data is acquired through the laser sensor when the non-contact sensor is the laser sensor, and the vehicle image data is acquired through the image sensor when the non-contact sensor is the image sensor.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored program, wherein the program when run performs the vehicle overrun detection method of any one of claims 1 to 7.
9. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the vehicle overrun detection method according to any one of claims 1-7 by means of the computer program.
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| US4207692A (en) * | 1979-02-09 | 1980-06-17 | Westinghouse Air Brake Company | Two engine four-wheel drive scraper with main propulsion at rear axle |
| DE3523335A1 (en) * | 1985-06-29 | 1987-01-08 | Gruemer Eisenwerk | CHASSIS FOR BUMPER BRAKE TRAILER |
| CN106595827B (en) * | 2016-11-11 | 2019-07-05 | 江苏开放大学 | A kind of highway weight calculation anti-escape charging method of abnormal alarm system |
| EP3669694A1 (en) * | 2018-12-20 | 2020-06-24 | STAR-TAGS Textilimport- und Merchandising GmbH | Trolley case |
| CN110221615A (en) * | 2019-06-18 | 2019-09-10 | 长春理工大学 | A kind of auxiliary vehicle drive method based on road conditions identification |
| CN114624726B (en) * | 2022-03-17 | 2023-05-16 | 南通探维光电科技有限公司 | Axle identification system and axle identification method |
| CN218120931U (en) * | 2022-05-18 | 2022-12-23 | 南通科瑞恩智能装备有限公司 | Upper and lower telescopic type automobile chassis overrun detection mechanism for stereo garage |
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