CN113139410A - Road surface detection method, device, equipment and storage medium - Google Patents
Road surface detection method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a road surface detection method, a road surface detection device, road surface detection equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps of acquiring road video data acquired by road side camera equipment, determining at least two vehicles running on a target road and the running track of each vehicle based on the road video data, and determining the road surface detection result of the target road according to the running tracks of the at least two vehicles. According to the technical scheme, a worker does not need to go to a field for detection specially, the labor cost is low, the running track of the vehicle is used for judgment, the error is small, and the detection efficiency of the road flatness is improved.
Description
Technical Field
The present disclosure relates to the field of signal processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting a road surface.
Background
The road surface flatness refers to a deviation value of a longitudinal concave-convex amount of a road surface, which is an important index for road surface evaluation, is related to safety performance and comfort performance of a road, is seriously affected by road surface damage, and is an important content in road surface maintenance. Thus, how to detect the road surface is the key to determine the road quality.
In the prior art, the simplest method is that by means of a straight ruler, a worker flatly places the straight ruler on the road surface of a target road, inserts a wedge block into a ruler bottom gap of the straight ruler, and reads a measured value on the wedge block to calculate the qualified rate and the maximum gap average value so as to determine whether the target road is flat or not. Although the detection method is simple and easy to implement, the method is high in labor cost and excessively depends on subjective judgment of workers, and the error is large.
Disclosure of Invention
The application provides a road surface detection method, a road surface detection device, road surface detection equipment and a storage medium, and aims to solve the problems of high cost and large error of the conventional road surface detection method.
In a first aspect, the present application provides a road surface detection method, including:
acquiring road video data acquired by road side camera equipment;
determining at least two vehicles traveling on a target road and a travel track of each vehicle based on the road video data;
and determining a road surface detection result of the target road according to the running tracks of the at least two vehicles.
In one possible design of the first aspect, the determining, based on the road video data, at least two vehicles traveling on a target road and a traveling track of each vehicle includes:
detecting the road video data, and determining at least two vehicles running on a target road and a vehicle body characteristic area of each vehicle;
analyzing the road video data, and determining a track point set formed by a vehicle body characteristic region of each vehicle in the traveling process of the vehicle to which the vehicle belongs to obtain the track point set of each vehicle;
and sequentially connecting all track points in the track point set of each vehicle to obtain the running track of each vehicle.
In another possible design of the first aspect, the determining a road surface detection result of the target road according to the driving tracks of the at least two vehicles includes:
determining whether each vehicle has an abnormal shaking event in the advancing process according to the linear characteristic of the running track of each vehicle;
if at least two vehicles have abnormal shaking events in the traveling process, determining that a road surface abnormal area exists in the target road, wherein the road surface abnormal area comprises the following steps: uneven road sections and/or broken road sections.
In the above possible design of the first aspect, the method further includes:
determining the road position of the vehicle to which the running track belongs when the abnormal shaking event occurs on the running track according to the position of the abnormal shaking event on the running track of each vehicle and the position information of the road side camera equipment;
and determining the road surface abnormal area on the target road according to the road position of each vehicle when all vehicles with abnormal shaking events appear on the driving track and the abnormal shaking events appear.
In yet another possible design of the first aspect, the method further includes:
presenting the road surface abnormal area on a map in a map rendering mode; and/or
And pushing an alarm notice of the road surface abnormal area.
In a second aspect, the present application provides a road surface detecting device, comprising: the device comprises an acquisition module, a processing module and a determination module;
the acquisition module is used for acquiring road video data acquired by the road side camera equipment;
the processing module is used for determining at least two vehicles running on a target road and a running track of each vehicle based on the road video data;
the determining module is used for determining the road surface detection result of the target road according to the running tracks of the at least two vehicles.
In a possible design of the second aspect, the processing module is specifically configured to detect the road video data, determine at least two vehicles running on the target road and a vehicle body feature region of each vehicle, analyze the road video data, determine a track point set formed in a running process of a vehicle to which the vehicle body feature region of each vehicle belongs, obtain a track point set of each vehicle, and sequentially connect all track points in the track point set of each vehicle to obtain a running track of each vehicle.
In another possible design of the second aspect, the determining module is specifically configured to determine whether an abnormal shaking event occurs during the traveling process of each vehicle according to a linear characteristic of a traveling track of each vehicle, and determine that a road surface abnormal area exists in the target road when at least two vehicles have the abnormal shaking event during the traveling process, where the road surface abnormal area includes: uneven road sections and/or broken road sections.
In the above possible design of the second aspect, the processing module is further configured to determine, according to the position of the abnormal shaking event occurring on the travel track of each vehicle and the position information of the roadside camera device, a road position where a vehicle to which the travel track belongs is located when the abnormal shaking event occurs on the travel track, and determine the abnormal road surface area on the target road according to the road position where each vehicle is located when all vehicles with the abnormal shaking event occurs on the travel track.
In yet another possible design of the second aspect, the apparatus further includes: the presenting module is used for presenting the road surface abnormal area on a map in a map rendering mode; and/or
The device further comprises: a push module;
and the pushing module is used for pushing the alarm notice of the road surface abnormal area.
In a third aspect, the present application provides an electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method according to the first aspect and possible designs.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein instructions which, when run on a computer, cause the computer to perform the method according to the first aspect and possible designs.
According to the road surface detection method, the road surface detection device, the road surface detection equipment and the storage medium, the road video data collected by the road side camera equipment are obtained, the running tracks of at least two vehicles running on the target road and each vehicle are determined based on the road video data, the road surface detection result of the target road is determined according to the running tracks of the at least two vehicles, the situation that a worker specially goes to a site for detection is not needed, the labor cost is low, the running tracks of the vehicles are used for judging, the error is small, and the road surface detection efficiency is improved.
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Fig. 1 is a schematic view of an application scenario of a road surface detection method provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a first embodiment of a road surface detection method provided in the embodiment of the present application;
fig. 3 is a schematic flow chart of a second road surface detection method according to an embodiment of the present application;
fig. 4 is a schematic flow chart of a third embodiment of a road surface detection method provided in the embodiment of the present application;
fig. 5 is a schematic structural diagram of an embodiment of a road surface detection device provided in the embodiment of the present application;
fig. 6 is a schematic structural diagram of an embodiment of an electronic device provided in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flatness of the road surface is one of the main technical indexes for evaluating the quality of the road surface, and relates to the safety and the comfort of driving, the size of impact force borne by the road surface and the service life of the road surface, the uneven road surface can increase the driving resistance and enable a vehicle to generate additional vibration action, the vibration action can cause bumping during driving, the speed and the safety of driving are influenced, the stability of driving and the comfort of passengers are influenced, meanwhile, the vibration action can also exert impact force on the road surface, so that the damage of the road surface and vehicle components and the abrasion of tires are aggravated, and the consumption of oil is increased. Therefore, in order to reduce the vibration impact, increase the driving speed and improve the driving comfort and safety, the road surface should maintain a certain flatness.
The flatness directly reflects the driving comfort of the vehicle and the safety and service life of the road surface. The detection of the pavement evenness can provide important information for decision makers, so that the decision makers can make optimization decisions for pavement maintenance, overhaul and the like. In addition, the detection of the pavement evenness can accurately provide information of the pavement construction quality, and an objective index for quality evaluation is provided for pavement construction.
The method comprises the steps that a road side camera device collects road video data, at least two vehicles running on a target road and the running track of each vehicle can be determined, and then the road surface detection result of the target road is determined according to the running track of each vehicle.
Specifically, road video data in a visible area of road-side camera equipment arranged on a road can be collected in real time, so that the road video data on a target road can comprise road information in the visible area of the road-side camera equipment and running information of vehicles running in the visible area, the running tracks of the vehicles and the vehicles on the target road are identified by processing the road video data, whether the road surface of the target road is abnormal or not can be determined by analyzing the running tracks, and special workers do not need to detect by means of special instruments, so that labor cost and detection errors are reduced.
Optionally, in this embodiment, the road surface abnormality may include road surface unevenness, road surface damage, and the like, and therefore, the road surface detection of this embodiment may include different aspects such as road surface flatness detection, road surface damage detection, and the like, and when it is determined that the road surface of the road is abnormal, contents such as a road maintenance warning may be started, so as to improve the usability of the road surface.
Fig. 1 is a schematic view of an application scenario of the road surface detection method according to the embodiment of the present application. As shown in fig. 1, the application scenario may include: a target road 11, a roadside image pickup device 12 provided on the target road 11, a vehicle 131 and a vehicle 132 traveling on the target road, and an electronic device 14 that can communicate with the roadside image pickup device 12. Optionally, when the vehicle 131 and the vehicle 132 are running on the target road 11 and located in the visible area of the roadside camera device 12, the roadside camera device 12 collects road video data including the vehicle 131 and the vehicle 132.
It can be understood that the size of the visible area is determined by the visible angle of the roadside camera device 12, and the visible angles of different roadside camera devices may be different or the same, which may be determined according to the basic attribute of each camera device, and are not described herein again.
In the embodiment of the application, the roadside camera device 12 may transmit the road video data acquired in real time to the electronic device 14, and the electronic device 14 may store or process the received road video data, so as to determine the road surface detection result of the target road through analysis.
For example, the electronic device 14 may be a monitoring system in the cloud, for example, including: a processor 141 and a display 142, wherein the processor 141 is used for processing the received road video data, and the display 142 is used for presenting the processing result of the processor 141. The electronic device in fig. 1 only exemplarily shows one processor and one display, and the actual composition of the electronic device may be determined according to the actual situation, which is not described herein again.
In the application scenario diagram shown in fig. 1, 1 roadside image capture device provided on a target road and 2 vehicles traveling on the target road are explained. It can be understood that the number of the roadside camera devices disposed on the target road may be determined according to the visible range of the roadside camera devices, and the vehicles traveling on the target road may be determined according to the actual traveling information, so that the number of the roadside camera devices disposed on the target road and the number of the vehicles traveling on the target road are not limited in the embodiments of the present application, and may be limited according to the actual situation, and details are not repeated here.
It can be understood that the electronic device in the embodiment of the present application may be composed of a plurality of modules, for example, a vehicle trajectory analysis module, a vehicle shake analysis module, a coordinate calculation module, a coordinate statistics module, a GIS map module, a maintenance early warning module, and the like.
The vehicle track analysis module can receive a real-time video generated by the road side camera equipment in real time and identify a vehicle in the video and a driving track of the vehicle, wherein the driving track comprises a normal linear track and a normal curve track.
The vehicle shake analysis module can receive the track data from the track analysis module, generates a shake event by judging track jump in the linear track process of the vehicle, and associates information corresponding to the roadside camera device.
A coordinate calculation module: the module converts the jitter position on the driving track into an actual GPS coordinate, and the GPS coordinate of the roadside camera equipment can be calibrated in a GIS map in advance.
A coordinate counting module: and correspondingly recording the jitter position of the jitter event, and performing corresponding addition statistics on the coordinate point region range after receiving the event every time (the frequent jitter of a certain GPS coordinate region indicates that the flatness of the region is abnormal).
And a GIS map module: the GIS module displays a road map and abnormal areas of the road surface, and can be identified by different colors.
Maintenance early warning module: and receiving suspicious coordinate information from the coordinate counting module, and judging whether to generate early warning according to the warning threshold value.
The concrete implementation of this scheme can effectively utilize current monitoring resources to supervise the investigation to the road condition of leveling, can effectively reduce the work of personnel's on-the-spot investigation, promotes the efficiency of personnel's investigation, promotes working quality and work efficiency, provides new thinking for the wisdom city.
In the embodiment of the present application, it can be understood that the electronic device in the application scenario illustrated in fig. 1 may be a terminal device, for example, a computer, a tablet computer, or the like, or may also be a server, for example, a background processing platform, or the like. The embodiment explains the execution subject of the road surface detection method as the electronic device, and it can be determined according to actual conditions as to whether the electronic device is specifically a terminal device or a server.
The technical solution of the present application will be described in detail below with reference to specific examples. It should be noted that the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
Fig. 2 is a schematic flow chart of a first embodiment of a road surface detection method provided in the embodiment of the present application. As shown in fig. 2, the method may include the steps of:
step 21: and acquiring road video data acquired by road side camera equipment.
With the rapid development of technology, roadside cameras are generally disposed on both sides of a road to collect information on the road and information on vehicles driving to the road in real time. Thus, in the present embodiment, the roadside image pickup device disposed beside the target road may collect road video data within its visible range, which may include information of the target road, information of vehicles traveling on the target road.
In this embodiment, after the road video data is collected by the roadside camera device, as an example, the roadside camera device may transmit the road video data to the intelligent transportation system for storage, and then process the road video data by the processing device in the intelligent transportation system. As another example, the roadside image capture device may also directly transmit road video data to the electronic device to cause the electronic device to process it.
It is understood that, in the embodiments of the present application, the target road may refer to an intersection, a road section, a highway gate or a road security gate in public transportation, and may also refer to various zones such as a residential underground garage, a public parking lot, and the like.
In this embodiment, the roadside image capturing device may capture a road video under a certain trigger condition, for example, when a vehicle passes by, the roadside image capturing device may also capture the road video in real time. The embodiment of the application does not limit the triggering mode or the collecting mode for collecting the road video data by the road side camera equipment, and the triggering mode or the collecting mode can be determined according to actual conditions.
Step 22: based on the road video data, at least two vehicles traveling on the target road and a travel track of each vehicle are determined.
In the embodiment of the application, the vehicle image monitored by the monitoring camera is an image of a vehicle and its surrounding environment, the monitoring location can be an intersection or a highway section in public traffic, or a zone with vehicle monitoring requirements such as a residential underground garage and a public parking lot, and the state of the vehicle in the monitored vehicle image can be a driving state or a parking state. The embodiment of the application does not limit the state of the vehicle.
In this embodiment, after acquiring the road video data, the electronic device may perform frame-by-frame decoding on the road video data, identify at least two vehicles in the road video data, determine a vehicle body feature area of the vehicle according to the detected component of the vehicle, and determine a travel track of each vehicle based on a track change of the determined vehicle body feature area.
It is to be understood that, at least two vehicles in the embodiment of the present application may be vehicles that travel on the target road at different time periods, or may also be vehicles that appear on the target road at the same time for a certain time period, which is not limited in the embodiment of the present application and may be determined according to actual situations. In addition, the vehicle body characteristic region of the vehicle may be a whole vehicle, or may be a part of the vehicle, such as a lamp, a wheel, a window, and other regions with obvious characteristics.
Step 23: and determining a road surface detection result of the target road according to the running tracks of the at least two vehicles.
In general, when a vehicle normally travels on a road with good flatness, a formed travel track is generally a continuous curve, and the curvature of the curve is continuous, and the change of the curvature is continuous, that is, the vehicle track during normal travel should be a smooth straight line or a smooth curve, however, when the vehicle travels on a road with poor flatness, the formed track curve generally has points which are obviously deviated from the track, namely, track jump points, so in the embodiment of the present application, the flatness information of a target road on which the vehicle travels can be determined based on the travel track of the vehicle, and then the road surface detection result of the target road can be obtained.
Specifically, if the driving tracks of all vehicles are continuous and smooth line segments, it may be determined that the road surface of the target road is flat, but if points that are obviously deviated from the tracks appear in the driving tracks of a plurality of vehicles, that is, abrupt points or burrs appear, it is considered that the vehicles have transient jumps in the linear motion process, and it is determined that the target road on which the vehicles are driving may be uneven.
According to the road surface detection method provided by the embodiment of the application, the road video data acquired by the road side camera device is acquired, the at least two vehicles running on the target road and the running track of each vehicle are determined based on the road video data, and then the road surface detection result of the target road is determined according to the running tracks of the at least two vehicles. According to the technical scheme, a worker does not need to go to a field for detection specially, the labor cost is low, the running track of the vehicle is used for judgment, the error is small, and the detection efficiency of the road flatness is improved.
Exemplarily, on the basis of the above embodiments, fig. 3 is a schematic flow chart of a second embodiment of the road surface detection method provided in the embodiment of the present application. As shown in fig. 3, in the present embodiment, the step 22 can be implemented by:
step 31: the road video data are detected, and at least two vehicles driving on the target road and a vehicle body characteristic area of each vehicle are determined.
Optionally, in an embodiment of the application, the electronic device may perform target detection on the acquired road video data by using a preset identification algorithm, so as to determine at least two vehicles included in the road video data. It is to be noted that the object detection in the present embodiment may include both the vehicle and the constituent parts of the vehicle.
It is understood that the vehicle and the components of the vehicle may be obtained through a single inspection process or may be obtained through a plurality of inspection processes. The specific detection processing process may be determined according to actual conditions, and is not described herein again.
Optionally, the following explanation is given by taking vehicle identification in practical application as an example: the multi-frame images included in the acquired road video data are processed by using a pattern recognition algorithm, so that vehicles and/or components of the vehicles included in the road video data, such as lamps, windows, wheels, logos, license plates and the like of the vehicles, can be determined, and further the components can be determined to be one component of the vehicle body feature area based on the feature significance of each component. The specific component to be used as the characteristic region of the vehicle body may be determined according to actual conditions, and is defined herein.
In the present embodiment, the preset recognition method is not limited to a specific pattern recognition algorithm, and any suitable algorithm may be used, such as a convolutional neural network, adaboost, a key point positioning method, and the like, and the present application is not limited to obtaining the vehicle and the vehicle components through several detections.
Step 32: and analyzing the road video data, and determining a track point set formed by the vehicle body characteristic region of each vehicle in the advancing process of the vehicle to which the vehicle belongs to obtain the track point set of each vehicle.
In this embodiment, after at least two vehicles running on the target road and the vehicle body feature area of each vehicle are determined, the acquired road video data are analyzed, the vehicle body feature area of each vehicle is tracked in real time, the motion track of the vehicle body feature area of each vehicle is recorded frame by frame, and a track linked list of each vehicle is formed, that is, a track point set is formed in the vehicle body feature area of each vehicle in the vehicle running process, so that the track point set of each vehicle is obtained.
Specifically, a plurality of moving track points of the vehicle body feature region of each vehicle are obtained by analyzing all frame images included in the road video data according to the position information of a certain fixed point on the vehicle body feature region of each vehicle in each frame image, and the set of all the moving track points is a track point set formed in the traveling process of the vehicle to which the vehicle body feature region belongs.
Step 33: and sequentially connecting all track points in the track point set of each vehicle to obtain the running track of each vehicle.
Illustratively, for each track point in the track point set of each vehicle, sequentially connecting each track point according to the acquisition sequence of the video frames to form a track linked list, thereby obtaining the running track of each vehicle.
According to the road surface detection method provided by the embodiment of the application, at least two vehicles running on a target road and the vehicle body characteristic area of each vehicle are determined by detecting the road video data, the road video data are analyzed, the track point set formed by the vehicle body characteristic area of each vehicle in the running process of the vehicle to which the vehicle belongs is determined, the track point set of each vehicle is obtained, and then all track points in the track point set of each vehicle are sequentially connected to obtain the running track of each vehicle. According to the technical scheme, the driving track obtained by tracking the track of the vehicle body characteristic area lays a foundation for obtaining an accurate road surface detection result subsequently.
Optionally, on the basis of any of the above embodiments, fig. 4 is a schematic flow chart of a third embodiment of the road surface detection method provided in the embodiment of the present application. As shown in fig. 4, in this embodiment, the step 23 can be implemented by:
step 41: and determining whether each vehicle has an abnormal shaking event in the process of traveling according to the linear characteristic of the traveling track of each vehicle.
In the embodiment, considering that the formed driving track is a smooth straight line or curve when the vehicles drive on a road with good flatness, whether a track jumping point which is obviously deviated from the driving track occurs in the driving track of each vehicle, namely whether the vehicles have short jump during the linear motion process is analyzed according to the linear characteristic of the driving track of each vehicle, so as to determine whether each vehicle has an abnormal shaking event during the driving process.
Step 42: if at least two vehicles have abnormal shaking events in the traveling process, determining that a road surface abnormal area exists in the target road, wherein the road surface abnormal area comprises the following steps: uneven road sections and/or broken road sections.
For example, since the abnormal shaking event refers to a short jump in the driving track of the vehicle, which is usually caused by the unevenness of the road and/or the breakage of the road surface on which the vehicle is driving, but may also be caused by human factors or environmental factors, it is necessary to combine the driving tracks of multiple vehicles to determine, that is, if at least two vehicles have an abnormal shaking event during the driving process, it may be determined that there is an abnormal road surface area on the target road. Alternatively, uneven road surface may mean that the road surface has a convex characteristic or the like.
It should be noted that, in this embodiment, in order to improve the accuracy of road surface detection, the road video data acquired by the roadside camera device when the multiple vehicles travel on the target road is analyzed, and the road surface detection result of the target road is comprehensively determined according to the track analysis results of the multiple vehicles, so that the problem of abnormal shaking events occurring in the travel process due to unsmooth travel tracks caused by human factors and/or environmental factors can be avoided.
Further, referring to fig. 4, when it is determined that uneven road sections exist in the target road, the road surface detection method may further include the steps of:
step 43: and determining the road position of the vehicle to which the running track belongs when the abnormal shaking event occurs on the running track according to the position of the abnormal shaking event on the running track of each vehicle and the position information of the road side camera equipment.
Optionally, in a possible design of the present application, a related person may determine a conversion relationship between a geographic position coordinate and an image coordinate based on the position information of the road-side image capturing device, the visible area of the road-side image capturing device, and the size of the video and/or image captured by the road-side image capturing device, and store the conversion relationship in the electronic device, so that after determining a position (image coordinate) where an abnormal shaking event occurs on a driving track based on the image captured by the road-side image capturing device, the electronic device may convert the position (track jump point) where the abnormal shaking event occurs in the image into the geographic position coordinate according to the conversion relationship, thereby determining a road position where a vehicle belongs to the driving track when the abnormal shaking event occurs on the driving track.
Optionally, in another possible design of the present application, when determining that an abnormal shaking event occurs on a driving track of a certain vehicle, the electronic device may calculate position information of each point in a visible area of the road-side camera device, and determine, by combining with a position of the abnormal shaking event occurring on the driving track, a road position where the vehicle is located when the abnormal shaking event occurs, so as to determine a specific position of the uneven road section.
Optionally, this step 43 may be implemented by the following steps a1 and a 2:
a1: and determining the position information of each point in the visible area of the roadside imaging equipment according to the position information of the roadside imaging equipment.
Alternatively, in this embodiment, as an example, information of each roadside image capturing device, for example, an ID, a position (GPS coordinate information), position information of a visible range, and the like, may be stored in the electronic device in advance, and thus, position information of each point within the visible range of the roadside image capturing device may be obtained by calculation from the stored information of the roadside image capturing device.
As another example, when the electronic device detects a vehicle in road video data, in a normal case, the model of the vehicle may be determined, and then the frame width of the vehicle may be determined by querying a vehicle database storing basic information of the vehicle such as various vehicle models, and then the position information of each point in the visible range of the roadside camera device may be obtained by calculation according to the position information of the visible range of the roadside camera device.
A2: and determining the road position of the vehicle when the abnormal shaking event occurs according to the position information of each point in the visible area of the road side camera equipment and the position of the abnormal shaking event on the driving track of each vehicle.
In the present embodiment, the position information of the abnormal shaking event, i.e., the locus jump point on the travel locus can be determined by analyzing the travel locus of each vehicle.
Therefore, by combining the position of the abnormal shaking event on the driving track and the position information of each point in the visible range of the road side camera equipment, the road position of the vehicle to which the driving track belongs when the abnormal shaking event occurs on the driving track can be determined.
Step 44: and determining the road surface abnormal area on the target road according to the road position of each vehicle when all vehicles with abnormal shaking events appear on the running track and the abnormal shaking events appear.
Optionally, in this embodiment, in order to determine the abnormal road surface area on the target road, when the electronic device determines that an abnormal shaking event occurs on the driving track, and after the road position of the vehicle to which the driving track belongs is determined, the abnormal road surface area on the target road may be determined by combining all the road position information.
Optionally, the specific implementation of this step can be implemented in at least any one of the following three implementation manners:
as an example, the electronic device may determine a plurality of possible abnormal areas based on a road position where each vehicle is located and a preset mark radius when an abnormal shaking event occurs on a driving track of a plurality of vehicles, and further, take an intersection of all the possible abnormal areas, and finally determine a road surface abnormal area, that is, the road surface abnormal area may be an intersection of the plurality of road surface abnormal areas.
Specifically, the manner of determining the possible abnormal area on a per-vehicle basis may be as follows: when an abnormal shaking event occurs on the running track of the vehicle, the GPS coordinate of the road position where the vehicle is located is used as the center of a circle, the preset mark radius is used as the radius to draw a circle, and the area corresponding to the circle is used as the possible abnormal area determined based on the vehicle.
It can be understood that the preset marking radius may be adjusted according to actual needs to achieve different marking accuracies, and the embodiment of the present application does not limit specific values of the marking radius.
As another example, the electronic device may analyze whether all road positions have a convergence tendency according to a distribution of the road positions of all vehicles based on the road position where each vehicle is located when an abnormal shaking event occurs on the driving tracks of the plurality of vehicles, and if so, take the convergence range of all road positions as the abnormal road surface area on the target road.
As another example, the electronic device determines, by analyzing road video data, a road position where a first vehicle is located when an abnormal shaking event occurs on a driving track of the first vehicle, performs a circle drawing with a GPS coordinate where the road position is located as a center of the circle and a preset mark radius as a radius, and takes an area corresponding to the circle as a possible road surface abnormal area of the target road, and further determines, according to the abnormal shaking event occurring on the driving track of another vehicle, that the possible road surface abnormal area is considered as a possible road surface abnormal area of the target road if the abnormal road positions of a preset number of other vehicles are all located in the possible road surface abnormal area.
Specifically, when analyzing road video data of a target road and determining whether a road surface abnormal area appears on the target road, if abnormal road positions determined based on a plurality of vehicles are all located in the road surface abnormal area, the electronic device marks the road surface abnormal area once after each determination, sequentially accumulates the marked times of the road surface abnormal area, and considers the road surface abnormal area as the road surface abnormal area of the target road when the marked times of the road surface abnormal area are greater than a preset marking threshold.
In the embodiment, when at least two vehicles have abnormal shaking events in the traveling process, the road position of each vehicle is used, and the road surface abnormal area in the target road is determined, so that shaking caused by human factors and environmental factors can be prevented, and false alarm is reduced.
Further, in the embodiment of the application, after the abnormal road surface area on the target road is determined, the information of the road abnormality can be notified to relevant personnel, so that the relevant personnel can review or repair the target road in time. Illustratively, the present embodiment can be implemented in any one or two of the following manners:
as an example, the road surface abnormal region is presented on a map by a form of map rendering.
In this embodiment, in order to enable the relevant person to know the flatness information of the target road, the road surface abnormal area may be presented on the map in a map rendering mode.
It can be understood that, for the same target road, if the same area is determined to be the abnormal road surface area by analyzing the driving tracks of the vehicles in the road video data, the color of the area on the map can be deepened, so that the relevant people can find the area in time.
Optionally, the road map is used for displaying the area with the abnormal road surface, and the area can be identified by different colors, so that related personnel can distinguish the abnormal conditions with different degrees conveniently.
As another example, an alarm notification of the road surface abnormality area is pushed.
Optionally, when the electronic device analyzes the road video data of the target road and determines the abnormal road area on the target road, the electronic device pushes the alarm notification of the abnormal road area to enable corresponding maintenance personnel to perform maintenance in time.
According to the road surface detection method provided by the embodiment of the application, according to the linear characteristic of the running track of each vehicle, the abnormal shaking event occurring in the running process of each vehicle is determined, then the road surface abnormal area existing in the target road is determined, then the road position where the vehicle belonging to the running track belongs to when the abnormal shaking event occurs on the running track is determined according to the position where the abnormal shaking event occurs on the running track of each vehicle and the position information of the road side camera equipment, and then the road surface abnormal area on the target road is determined according to the road position where each vehicle exists when the abnormal shaking event occurs on all vehicles with the abnormal shaking event occurring on the running track, wherein the road surface abnormal area comprises the uneven road sections. According to the technical scheme, the abnormal area of the road surface can be accurately determined, and the flatness detection precision and the detection efficiency are improved.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Fig. 5 is a schematic structural diagram of an embodiment of a road surface detection device provided in the embodiment of the present application. The device can be integrated in the electronic equipment, and can also be realized by the electronic equipment. As shown in fig. 5, the apparatus may include: an acquisition module 51, a processing module 52 and a determination module 53.
The acquiring module 51 is configured to acquire road video data acquired by a road-side camera device;
the processing module 52 is configured to determine at least two vehicles traveling on the target road and a traveling track of each vehicle based on the road video data;
the determining module 53 is configured to determine a road surface detection result of the target road according to the driving tracks of the at least two vehicles.
In a possible design of the embodiment of the present application, the processing module 52 is specifically configured to detect the road video data, determine at least two vehicles traveling on the target road and the vehicle body feature area of each vehicle, analyze the road video data, determine the track point set formed by the vehicle body feature area of each vehicle in the traveling process of the belonging vehicle, obtain the track point set of each vehicle, and sequentially connect all track points in the track point set of each vehicle to obtain the traveling track of each vehicle.
In another possible design of the embodiment of the present application, the determining module 53 is specifically configured to determine whether an abnormal shaking event occurs during the traveling process of each vehicle according to a linear characteristic of a traveling track of each vehicle, and determine that a road surface abnormal area exists in the target road when at least two vehicles have the abnormal shaking event during the traveling process, where the road surface abnormal area includes: uneven road sections and/or broken road sections.
In the above possible design of the embodiment of the application, the processing module 52 is further configured to determine, according to the position of the abnormal shaking event occurring on the running track of each vehicle and the position information of the roadside camera device, a road position where the vehicle belongs to the running track when the abnormal shaking event occurs on the running track, and determine the road surface abnormal region on the target road according to the road position where each vehicle exists when all vehicles with the abnormal shaking event occur on the running track when the abnormal shaking event occurs.
In yet another possible design of the embodiment of the present application, the apparatus further includes: the presenting module is used for presenting the road surface abnormal area on the map in a map rendering mode;
and/or
The device further comprises: a push module;
the pushing module is used for pushing the alarm notice of the road surface abnormal area.
The apparatus provided in the embodiment of the present application may be used to execute the method in the embodiments shown in fig. 2 to fig. 4, and the implementation principle and the technical effect are similar, which are not described herein again.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the determining module may be a processing element separately set up, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the function of the determining module is called and executed by a processing element of the apparatus. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Fig. 6 is a schematic structural diagram of an embodiment of an electronic device provided in the embodiment of the present application. As shown in fig. 6, the apparatus may include: a processor 61, a memory 62, a communication interface 63 and a system bus 64, wherein the memory 62 and the communication interface 63 are connected to the processor 61 through the system bus 64 and perform communication with each other, the memory 62 is used for storing computer execution instructions, the communication interface 63 is used for communicating with other devices, and the processor 61 implements the scheme of the embodiment shown in fig. 2 to 4 when executing the computer program.
The system bus mentioned in fig. 6 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The memory may comprise Random Access Memory (RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor may be a general-purpose processor, including a central processing unit CPU, a Network Processor (NP), and the like; but also a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
Optionally, an embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer is caused to execute the method according to the embodiment shown in fig. 2 to 4.
Optionally, an embodiment of the present application further provides a chip for executing the instruction, where the chip is configured to execute the method in the embodiment shown in fig. 2 to 4.
Embodiments of the present application further provide a program product, where the program product includes a computer program, where the computer program is stored in a computer-readable storage medium, and the computer program can be read by at least one processor from the computer-readable storage medium, and the at least one processor can implement the method in the embodiments shown in fig. 2 to fig. 4 when executing the computer program.
In the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship; in the formula, the character "/" indicates that the preceding and following related objects are in a relationship of "division". "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
It is to be understood that the various numerical references referred to in the embodiments of the present application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of the present application.
It should be understood that, in the embodiment of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiment of the present application.
Finally, it should be noted that: the above 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 or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (11)
1. A road surface detection method, characterized by comprising:
acquiring road video data acquired by road side camera equipment;
determining at least two vehicles traveling on a target road and a travel track of each vehicle based on the road video data;
and determining a road surface detection result of the target road according to the running tracks of the at least two vehicles.
2. The method of claim 1, wherein determining at least two vehicles traveling on a target road and a travel trajectory of each vehicle based on the road video data comprises:
detecting the road video data, and determining at least two vehicles running on a target road and a vehicle body characteristic area of each vehicle;
analyzing the road video data, and determining a track point set formed by a vehicle body characteristic region of each vehicle in the traveling process of the vehicle to which the vehicle belongs to obtain the track point set of each vehicle;
and sequentially connecting all track points in the track point set of each vehicle to obtain the running track of each vehicle.
3. The method according to claim 1 or 2, wherein determining the road surface detection result of the target road according to the traveling tracks of the at least two vehicles comprises:
determining whether each vehicle has an abnormal shaking event in the advancing process according to the linear characteristic of the running track of each vehicle;
if at least two vehicles have abnormal shaking events in the traveling process, determining that a road surface abnormal area exists in the target road, wherein the road surface abnormal area comprises the following steps: uneven road sections and/or broken road sections.
4. The method of claim 3, further comprising:
determining the road position of the vehicle to which the running track belongs when the abnormal shaking event occurs on the running track according to the position of the abnormal shaking event on the running track of each vehicle and the position information of the road side camera equipment;
and determining the road surface abnormal area on the target road according to the road position of each vehicle when all vehicles with abnormal shaking events appear on the driving track and the abnormal shaking events appear.
5. The method of claim 4, further comprising:
presenting the road surface abnormal area on a map in a map rendering mode; and/or
And pushing an alarm notice of the road surface abnormal area.
6. A road surface detecting device characterized by comprising: the device comprises an acquisition module, a processing module and a determination module;
the acquisition module is used for acquiring road video data acquired by the road side camera equipment;
the processing module is used for determining at least two vehicles running on a target road and a running track of each vehicle based on the road video data;
the determining module is used for determining the road surface detection result of the target road according to the running tracks of the at least two vehicles.
7. The device according to claim 6, wherein the processing module is specifically configured to detect the road video data, determine at least two vehicles traveling on the target road and a vehicle body feature region of each vehicle, analyze the road video data, determine a track point set formed by the vehicle body feature region of each vehicle in a traveling process of the vehicle to which the vehicle belongs, obtain a track point set of each vehicle, and sequentially connect all track points in the track point set of each vehicle to obtain a traveling track of each vehicle.
8. The device according to claim 6 or 7, wherein the determining module is specifically configured to determine whether each vehicle has an abnormal shaking event during the traveling process according to a linear characteristic of a traveling track of each vehicle, and determine that a road surface abnormal area exists in the target road when at least two vehicles have the abnormal shaking event during the traveling process, and the road surface abnormal area includes: uneven road sections and/or broken road sections.
9. The apparatus according to claim 8, wherein the processing module is further configured to determine, according to a position of an abnormal shaking event occurring on a travel track of each vehicle and position information of the roadside camera device, a road position where a vehicle belonging to the travel track belongs when the abnormal shaking event occurs on the travel track, and determine the road surface abnormal area on the target road according to the road position where each vehicle belongs when all vehicles with the abnormal shaking event occur on the travel track.
10. An electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of the preceding claims 1-5 when executing the program.
11. A computer-readable storage medium having stored therein instructions which, when executed on a computer, cause the computer to perform the method of any one of claims 1-5.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113850237A (en) * | 2021-11-29 | 2021-12-28 | 华砺智行(武汉)科技有限公司 | Internet vehicle target detection and evaluation method and system based on video and track data |
| CN114581856A (en) * | 2022-05-05 | 2022-06-03 | 广东邦盛北斗科技股份公司 | Agricultural unit motion state identification method and system based on Beidou system and cloud platform |
| CN115058947A (en) * | 2022-05-12 | 2022-09-16 | 安徽中青检验检测有限公司 | Roadbed pavement flatness detection device and method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107316006A (en) * | 2017-06-07 | 2017-11-03 | 北京京东尚科信息技术有限公司 | A kind of method and system of road barricade analyte detection |
| CN108221603A (en) * | 2018-01-08 | 2018-06-29 | 重庆大学 | Road surface three-dimensional information detection device, the method and system of a kind of road |
| CN108242145A (en) * | 2016-12-26 | 2018-07-03 | 高德软件有限公司 | Abnormal track point detecting method and device |
| CN109615862A (en) * | 2018-12-29 | 2019-04-12 | 南京市城市与交通规划设计研究院股份有限公司 | Road vehicle movement of traffic state parameter dynamic acquisition method and device |
-
2020
- 2020-01-19 CN CN202010069930.8A patent/CN113139410B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108242145A (en) * | 2016-12-26 | 2018-07-03 | 高德软件有限公司 | Abnormal track point detecting method and device |
| CN107316006A (en) * | 2017-06-07 | 2017-11-03 | 北京京东尚科信息技术有限公司 | A kind of method and system of road barricade analyte detection |
| CN108221603A (en) * | 2018-01-08 | 2018-06-29 | 重庆大学 | Road surface three-dimensional information detection device, the method and system of a kind of road |
| CN109615862A (en) * | 2018-12-29 | 2019-04-12 | 南京市城市与交通规划设计研究院股份有限公司 | Road vehicle movement of traffic state parameter dynamic acquisition method and device |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113850237A (en) * | 2021-11-29 | 2021-12-28 | 华砺智行(武汉)科技有限公司 | Internet vehicle target detection and evaluation method and system based on video and track data |
| CN114581856A (en) * | 2022-05-05 | 2022-06-03 | 广东邦盛北斗科技股份公司 | Agricultural unit motion state identification method and system based on Beidou system and cloud platform |
| CN115058947A (en) * | 2022-05-12 | 2022-09-16 | 安徽中青检验检测有限公司 | Roadbed pavement flatness detection device and method |
| CN115058947B (en) * | 2022-05-12 | 2024-02-09 | 安徽中青检验检测有限公司 | Roadbed and pavement flatness detection device and method |
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|---|---|
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