CN112702568B - Abnormality detection method and related device - Google Patents
Abnormality detection method and related device Download PDFInfo
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- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
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Abstract
The embodiment of the application discloses an anomaly detection method and a related device, which are applied to processing equipment and comprise the following steps: receiving first video data sent by the municipal cameras, wherein the first video data comprises video images of a plurality of roads shot by the municipal cameras, and the municipal cameras are distributed on the roads; determining whether abnormal conditions occur on the multiple roads according to the first video data; if yes, determining a target road with an abnormal condition and an abnormal reason of the abnormal condition; and carrying out video AI analysis on the video image of the target road and the abnormal reason, and pushing the video image of the target road and the abnormal reason to a municipal administration management department through a system. The embodiment of the application is beneficial to improving the early warning instantaneity of the road surface abnormality detection, and simultaneously improving the safety and the traffic efficiency of road traffic.
Description
Technical Field
The application relates to the field of municipal administration, in particular to an abnormality detection method and a related device.
Background
In the prior art, when the road surface is damaged, deformed and other abnormal problems occur, the fault is reported after manual detection or manual discovery under most conditions, and then workers are dispatched by municipal administration management departments to repair and maintain the road surface, so that the whole treatment process is long in time and low in treatment efficiency, the passing efficiency of the road is seriously influenced, even the road is blocked, and safety accidents occur. How to realize the intelligent detection of various abnormal problems occurring on the road, and report the detected abnormal problems to the municipal administration in time, belongs to a part of municipal administration, and is also an important problem to be solved currently.
Disclosure of Invention
The embodiment of the application provides an abnormality detection method and a related device, which are beneficial to improving the early warning instantaneity of road surface abnormality detection and simultaneously improving the safety and the traffic efficiency of road traffic.
In a first aspect, an embodiment of the present application provides an anomaly detection method, which is characterized in that the anomaly detection method is applied to a processor of a pavement anomaly detection system, where the pavement anomaly detection system includes the processor and a plurality of municipal cameras, and communication connection is established between the processor and the plurality of municipal cameras; the method comprises the following steps:
receiving first video data sent by the municipal cameras, wherein the first video data comprises video images of a plurality of roads shot by the municipal cameras, and the municipal cameras are distributed on the roads;
determining whether abnormal conditions occur on the multiple roads according to the first video data;
if yes, determining a target road with an abnormal condition and an abnormal reason of the abnormal condition;
and carrying out video AI analysis on the video image of the target road and the abnormal reason, and pushing the video image of the target road and the abnormal reason to a municipal administration management department through an intelligent municipal system.
In a second aspect, an embodiment of the present application provides an abnormality detection apparatus, which is applied to a processor of a pavement abnormality detection system, where the pavement abnormality detection system includes the processor and a plurality of municipal cameras, and communication connections are established between the processor and the plurality of municipal cameras; the data transmission device comprises a processing unit and a communication unit, wherein,
the processing unit is used for receiving first video data sent by the municipal cameras, the first video data comprise video images of a plurality of roads shot by the municipal cameras, and the municipal cameras are distributed on the roads; and determining whether an abnormal condition occurs on the plurality of roads according to the first video data; and if so, determining a target road with an abnormal condition and an abnormal reason of the abnormal condition; and the video AI analysis module is used for carrying out video AI analysis on the video image of the target road and the abnormal reason and pushing the video image of the target road and the abnormal reason to a municipal administration through an intelligent municipal system.
In a third aspect, an embodiment of the present application provides a processing device, including a controller, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the controller, the programs including instructions for performing steps in any of the methods of the first aspect of the embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform part or all of the steps as described in any of the methods of the first aspect of the embodiments of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in any of the methods of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
It can be seen that in the embodiment of the present application, the processing device first receives first video data sent by the plurality of municipal cameras, where the first video data includes video images of a plurality of roads captured by the plurality of municipal cameras, the plurality of municipal cameras are distributed on the plurality of roads, and then determines whether an abnormal condition occurs on the plurality of roads according to the first video data, then, if so, determines a target road on which the abnormal condition occurs and an abnormal cause of the abnormal condition, and finally, performs video AI analysis on the video images of the target road and the abnormal cause, and pushes the video images and the abnormal cause to a municipal administration department through an intelligent municipal system. The processing equipment of the pavement abnormality detection system can intelligently identify the target road with abnormal conditions and the abnormal reasons according to the first video data reported by the municipal cameras preset on a plurality of roads, so that video images of the target road and the abnormal reasons are reported to the municipal administration departments, the pavement abnormality early warning real-time performance is improved, the manual investigation work is reduced, and the safety rate and the traffic efficiency of road traffic are improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an abnormality detection system according to an embodiment of the present application;
fig. 2 is a reference schematic diagram of video data reporting according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of an anomaly detection method according to an embodiment of the present application;
FIG. 4 is a flowchart of another abnormality detection method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a processing apparatus according to an embodiment of the present application;
fig. 6 is a block diagram of functional units of an abnormality detection apparatus according to an embodiment of the present 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 application without making any inventive effort, are intended to be within the scope of the application.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The processing device may include a terminal or a server, and embodiments of the present application are not limited. Terminals include the precision of various handheld devices, vehicle mounted devices, wearable devices (e.g., smart watches, smart bracelets, pedometers, etc.), computing devices, or other processes connected to a wireless modem with wireless communication capabilities. User Equipment (UE), mobile Station (MS), terminal Equipment (terminal device), and the like. For convenience of description, the above-mentioned apparatuses are collectively referred to as a processing apparatus.
Embodiments of the present application are described in detail below.
The application provides a pavement abnormality detection system, as shown in fig. 1, the pavement abnormality detection system 10 comprises a processing device 101, a plurality of municipal cameras 102 and a plurality of video patrol cars 103, wherein the processing device 101, the plurality of municipal cameras 102 and the plurality of video patrol cars 103 are in communication connection. The processing device 101 may receive video data of the road reported by the municipal cameras 102, or may receive video data of the road reported by the video patrol cars 103, so as to determine whether an abnormal condition occurs on the road according to the video data. The municipal cameras 102 are distributed on a plurality of roads needing anomaly detection or real-time supervision, can shoot video information of the plurality of roads to obtain first video data, and report the video data to the processing equipment, so that the processing equipment 101 can judge whether the roads have anomalies, the plurality of video patrol cars 102 can detect the roads in real time according to a planned route to obtain the video data of the plurality of roads, or can go to a target road after receiving an instruction of the processing equipment 101 to obtain the video data of the target road, and report the video data to the processing equipment 101, so that the processing equipment 101 can further judge whether the roads have anomalies.
The processing device not only can judge whether the road surface is abnormal according to the video data provided by the municipal camera and the video patrol car, but also can determine whether the road surface is abnormal according to the reminding information comprising the video data and other modes reported by the user through the user equipment, as shown in fig. 2, the processing device can receive the video data 1 reported by the municipal camera, the video data 2 reported by the video patrol car and the video data 3 reported by the user equipment, and determine the road with the abnormal condition. The road with the municipal cameras is mainly characterized in that the abnormal situation is identified through video data 1 reported by the municipal cameras, the abnormal situation is identified through video data 1 reported by a video patrol car which can move at any time on the road without the eczema cameras, and the abnormal situation can be identified through any combination of the video data 1, the video data 2 and the video data 3, so that the reason that the abnormal situation occurs is accurately identified.
The abnormal conditions of the road surface comprise well cover displacement, well cover missing, road surface ponding, road surface crack, road surface damage, road surface empty pit and the like, and if the abnormal conditions cannot be detected in time, safety accidents are likely to occur, so that the abnormal detection of the road surface is extremely important.
Referring to fig. 3, fig. 3 is a flow chart of an abnormality detection method provided in an embodiment of the present application, which is applied to a processor of a pavement abnormality detection system, where the pavement abnormality detection system includes the processor and a plurality of municipal cameras, and the processor and the plurality of municipal cameras are connected in a communication manner. As shown in the figure, the abnormality detection method includes:
s301, the processing device receives first video data sent by the municipal cameras, where the first video data includes video images of a plurality of roads captured by the municipal cameras, and the municipal cameras are distributed on the plurality of roads.
Wherein the municipal cameras are distributed on a plurality of roads for acquiring monitoring videos of the roads in real time, not only the road surface condition of the roads but also the vehicle condition of the roads can be detected, the cameras can be uniformly distributed on each road, more municipal cameras can be arranged on the more congested roads according to the congestion condition of the travelling crane of each road, and fewer or one municipal camera can be arranged on the non-congested roads.
After the video images of the multiple roads are acquired in real time by the municipal cameras, first video data are generated and sent to the processing equipment, and the processing equipment can monitor the vehicle conditions of the multiple roads in real time and detect whether the multiple roads have abnormal conditions or not according to the video pictures of the multiple roads included in the first video data.
S302, the processing equipment determines whether abnormal conditions occur on the multiple roads according to the first video data.
According to video images associated with each road in a plurality of roads, determining whether an abnormal condition exists on each road, wherein the abnormal condition mainly comprises the conditions of well lid shift, well lid deletion, water accumulation, crack, damage, empty pit, unknown object, obstacle and the like of the road surface, and the conditions are mainly caused by objective factors such as abnormal occurrence of the road surface, so that the shape state of a road passing vehicle can be influenced, road congestion is caused, and even accidents such as car accidents can be caused.
S303, if yes, the processing equipment determines a target road with an abnormal condition and an abnormal reason of the abnormal condition.
When detecting that an abnormal condition exists on the road, the processing equipment firstly determines a target road with the abnormal condition and secondly determines an abnormal reason of the abnormal condition, namely a specific factor causing the abnormal condition to occur. The pavement abnormality detection system not only comprises municipal cameras arranged at the fixed positions of a plurality of roads, but also comprises a video patrol car capable of freely moving, and at the moment, the municipal cameras and the video patrol car can be obtained, so that the abnormality cause of the abnormal situation can be further determined.
And S304, the processing equipment performs video AI analysis on the video image of the target road and the abnormal reason, and pushes the video image of the target road and the abnormal reason to a municipal administration department through an intelligent municipal system.
After determining a target road with an abnormal condition and an abnormal reason for the abnormal condition of the target road, carrying out video AI analysis on a video image and the abnormal reason of the target road, detecting whether the abnormal condition of the target road occurs and whether the abnormal condition is matched with the abnormal reason or not, pushing the video image and the abnormal reason to a municipal administration management department through an intelligent municipal administration system, and determining whether the target road needs to be addressed to solve the abnormal condition and when the target road is addressed according to the video image and the abnormal reason by the municipal administration management department so as to issue maintenance tasks to a maintenance personnel terminal.
It can be seen that in the embodiment of the present application, the processing device first receives first video data sent by the plurality of municipal cameras, where the first video data includes video images of a plurality of roads captured by the plurality of municipal cameras, the plurality of municipal cameras are distributed on the plurality of roads, and then determines whether an abnormal condition occurs on the plurality of roads according to the first video data, then, if so, determines a target road on which the abnormal condition occurs and an abnormal cause of the abnormal condition, and finally, performs video AI analysis on the video images of the target road and the abnormal cause, and pushes the video images and the abnormal cause to a municipal administration department through an intelligent municipal system. The processing equipment of the pavement abnormality detection system can intelligently identify the target road with abnormal conditions and the abnormal reasons according to the first video data reported by the municipal cameras preset on a plurality of roads, so that video images of the target road and the abnormal reasons are reported to the municipal administration departments, the pavement abnormality early warning real-time performance is improved, the manual investigation work is reduced, and the safety rate and the traffic efficiency of road traffic are improved.
In one possible example, before the determining whether an abnormal situation occurs in the plurality of roads according to the first video data, the method further includes: detecting whether the capacity of the first video data is larger than a first preset threshold value; if yes, preprocessing a plurality of video images included in the first video data to obtain processed first video data, wherein the capacity of the processed first video data is smaller than the first preset threshold value, and the preprocessing comprises reducing the resolution of the video images and/or reducing the frame rate of the video images.
When determining whether an abnormal condition occurs in the plurality of roads according to the first video data, detecting whether the capacity of the first video data is larger than a first preset threshold, if so, preprocessing a plurality of images included in the first video data to obtain processed first video data, wherein the capacity of the processed first video data is larger than the first preset threshold, the first preset threshold can be preset by processing equipment, for example, the first preset threshold can be 20G, and when the capacity of the first video data is larger than 20G, the first video data is a huge data volume, and in order to reduce the processing pressure of the processing equipment, the first video data can be preprocessed.
The preprocessing mode comprises the steps of reducing the resolution of the video image and reducing the frame rate of the video image, wherein only the resolution of the video image can be reduced, or only the frame rate of the video image can be reduced, or the resolution and the frame rate of the video image can be reduced at the same time. The video image of which resolution and/or frame rate need to be reduced may be each video image included in the first video data, or a part of the video images in the first video data, for example, a plurality of video images are divided by taking a road as a unit, if the video image of a certain road is less than 5, the resolution and/or frame rate of the video image corresponding to the road is not reduced, and if the video image of the certain road is greater than 5, the resolution and/or frame rate of the video corresponding to the road is reduced.
In this example, when it is determined whether an abnormal situation occurs in the plurality of roads according to the first video data, whether the capacity of the first video data is greater than a first preset threshold is detected first, so that when it is detected that the capacity of the first video data is greater than the first preset threshold, the first video data is preprocessed, and the processed first video data is obtained, so that it is beneficial to reducing the processing pressure of the processing device on the video data.
In one possible example, the determining whether an abnormal situation occurs in the plurality of roads according to the first video data includes: detecting whether an abnormal element appears on the road surface of each road in the plurality of roads according to the video images of the plurality of roads; if yes, determining that an abnormal condition occurs, and acquiring a target position where the abnormal element occurs, wherein the abnormal element comprises at least one of the following components: ponding, cracks, breakage, empty pits, missing well covers, well cover shifting and barriers.
The municipal camera can be used for shooting the real situation of the road surface completely, at this moment, whether the abnormal element appears on the road surface is detected only according to the video image of the road, when the abnormal element is detected, the abnormal situation can be determined, and at this moment, the target position where the abnormal element appears can be further acquired, so that the target position can be more targeted for investigation.
The abnormal elements comprise elements such as ponding, cracks, breakage, empty pits, well lid deletion, well lid displacement, barriers and the like on the road surface, when the elements appear on the road surface, the elements can cause extremely large images for the running of vehicles, and even cause traffic accidents when serious, so that the elements need to be removed or repaired in time.
In this example, when judging whether the abnormal condition occurs on the road, the specific implementation manner is to judge whether the abnormal element occurs on the road surface of the road, so that whether the abnormal condition occurs on the road surface of the road can be accurately judged.
In one possible example, the pavement anomaly detection system further includes a plurality of video patrol cars, the processor and the plurality of video patrol cars establishing a communication connection; the determining the target road with the abnormal condition and the abnormal reason of the abnormal condition comprises the following steps: a first instruction is sent to a target video patrol car in the plurality of video patrol cars, wherein the first instruction carries a target position of the target road, and the first instruction is used for indicating the target video patrol car to go to the target road to confirm the abnormality of the target position; receiving a first video image of the target road, which is sent after the target video patrol car confirms the abnormality of the target position of the target road; and determining the abnormality reason of the target road according to the second video image of the target road in the first video data and the first video image.
When an abnormal condition occurs on a road surface, a target road with the abnormal condition and an abnormal reason of the abnormal condition need to be further determined, at this time, because the road surface abnormal detection system not only comprises a plurality of municipal cameras, but also comprises a plurality of video patrol cars, the abnormal reason of the target road can be determined more accurately in a mode that the municipal cameras are combined with the video patrol cars, in a specific implementation mode, the processing equipment sends a first instruction to the target patrol cars in the plurality of video patrol cars, the first instruction carries position information of the target road, the target video patrol cars send the first instruction to the position of the target road for abnormal confirmation, then the first video image of the target road is sent to the processing equipment, and the first video image can help the processing equipment to confirm whether the abnormal condition occurs or not and the reason more intuitively and accurately.
The processing device can select the video patrol car closest to the target road according to the distance between the current video patrol car and the target road or select the video patrol car in an idle state according to the working state of each video patrol car.
In this example, the processing device may determine, according to the second video image of the target road in the first video data reported by the municipal camera, the first video image further reported by the video patrol car, so that the target road is an abnormal cause of the abnormal condition in an omnibearing, more accurate and reliable manner.
In one possible example, the determining the cause of the abnormality of the target road according to the second video image of the target road in the first video data and the first video image includes: acquiring a plurality of images of the target road according to the first video image and the second video image, wherein the images are images of different angles of the target road; determining abnormal elements actually appearing in the target position according to the plurality of images; and acquiring target images comprising the truly-occurring abnormal elements in the plurality of images, and generating abnormal reasons of the target road, wherein the abnormal reasons comprise the truly-occurring abnormal elements and the target images.
According to the first video image and the second video image of the target road, multiple images of the target road can be obtained, the multiple images are images aiming at different angles of the target road, and abnormal elements which can truly appear at the target position can be determined by the multiple images, so that the target images which comprise the truly appearing abnormal elements in the multiple images are obtained, abnormal reasons are generated, reported to a municipal administration management department, and the abnormal reasons comprise the truly appearing abnormal elements and the target images, namely, text description and image description of the truly appearing abnormal elements.
In this example, in order to more accurately determine the abnormal reason of the abnormal condition of the target road, the first video image captured by the video patrol car and the second video image captured by the municipal camera may be combined to obtain a plurality of images of different angles of the target road, and the plurality of images may be analyzed to more accurately determine the abnormal reason of the abnormal condition of the target road.
Referring to fig. 4, fig. 4 is a schematic flow chart of an anomaly detection method according to an embodiment of the present application, which is consistent with the embodiment shown in fig. 3, and is applied to a processing device. As shown in the figure, the abnormality detection method includes:
s401, the processing device receives first video data sent by the municipal cameras, where the first video data includes video images of a plurality of roads captured by the municipal cameras, and the municipal cameras are distributed on the plurality of roads.
S402, the processing device detects whether the capacity of the first video data is greater than a first preset threshold.
S403, if yes, the processing device performs preprocessing on a plurality of video images included in the first video data to obtain processed first video data, wherein the capacity of the processed first video data is smaller than the first preset threshold value, and the preprocessing comprises reducing the resolution of the video images and/or reducing the frame rate of the video images.
S404, the processing equipment determines whether abnormal conditions occur on the plurality of roads according to the processed first video data.
And S405, if so, the processing equipment determines a target road with an abnormal condition and an abnormal reason of the abnormal condition.
And S406, the processing equipment performs video AI analysis on the video image of the target road and the abnormal reason, and pushes the video image of the target road and the abnormal reason to a municipal administration department through an intelligent municipal system.
It can be seen that in the embodiment of the present application, the processing device first receives first video data sent by the plurality of municipal cameras, where the first video data includes video images of a plurality of roads captured by the plurality of municipal cameras, the plurality of municipal cameras are distributed on the plurality of roads, and then determines whether an abnormal condition occurs on the plurality of roads according to the first video data, then, if so, determines a target road on which the abnormal condition occurs and an abnormal cause of the abnormal condition, and finally, performs video AI analysis on the video images of the target road and the abnormal cause, and pushes the video images and the abnormal cause to a municipal administration department through an intelligent municipal system. The processing equipment of the pavement abnormality detection system can intelligently identify the target road with abnormal conditions and the abnormal reasons according to the first video data reported by the municipal cameras preset on a plurality of roads, so that video images of the target road and the abnormal reasons are reported to the municipal administration departments, the pavement abnormality early warning real-time performance is improved, the manual investigation work is reduced, and the safety rate and the traffic efficiency of road traffic are improved.
In addition, when determining whether an abnormal condition occurs in the plurality of roads according to the first video data, detecting whether the capacity of the first video data is larger than a first preset threshold value, so that when detecting that the capacity of the first video data is larger than the first preset threshold value, preprocessing the first video data to obtain the processed first video data, and reducing the processing pressure of processing equipment on the video data is facilitated.
Referring to fig. 5, in accordance with the embodiments shown in fig. 3 and 4, fig. 5 is a schematic structural diagram of a processing device 500 according to an embodiment of the present application, where the processing device 500 runs one or more application programs and an operating system, and as shown, the processing device 500 includes a processor 510, a memory 520, a communication interface 530, and one or more programs 521, where the one or more programs 521 are stored in the memory 520 and are configured to be executed by the processor 510, and the one or more programs 521 include instructions for performing the following steps;
receiving first video data sent by the municipal cameras, wherein the first video data comprises video images of a plurality of roads shot by the municipal cameras, and the municipal cameras are distributed on the roads;
Determining whether abnormal conditions occur on the multiple roads according to the first video data;
if yes, determining a target road with an abnormal condition and an abnormal reason of the abnormal condition;
and carrying out video AI analysis on the video image of the target road and the abnormal reason, and pushing the video image of the target road and the abnormal reason to a municipal administration management department through an intelligent municipal system.
It can be seen that in the embodiment of the present application, the processing device first receives first video data sent by the plurality of municipal cameras, where the first video data includes video images of a plurality of roads captured by the plurality of municipal cameras, the plurality of municipal cameras are distributed on the plurality of roads, and then determines whether an abnormal condition occurs on the plurality of roads according to the first video data, then, if so, determines a target road on which the abnormal condition occurs and an abnormal cause of the abnormal condition, and finally, performs video AI analysis on the video images of the target road and the abnormal cause, and pushes the video images and the abnormal cause to a municipal administration department through an intelligent municipal system. The processing equipment of the pavement abnormality detection system can intelligently identify the target road with abnormal conditions and the abnormal reasons according to the first video data reported by the municipal cameras preset on a plurality of roads, so that video images of the target road and the abnormal reasons are reported to the municipal administration departments, the pavement abnormality early warning real-time performance is improved, the manual investigation work is reduced, and the safety rate and the traffic efficiency of road traffic are improved.
In one possible example, before determining whether an abnormal situation occurs in the plurality of roads according to the first video data, the instructions in the program are specifically configured to: detecting whether the capacity of the first video data is larger than a first preset threshold value; if yes, preprocessing a plurality of video images included in the first video data to obtain processed first video data, wherein the capacity of the processed first video data is smaller than the first preset threshold value, and the preprocessing comprises reducing the resolution of the video images and/or reducing the frame rate of the video images.
In one possible example, in said determining whether an abnormal situation occurs in said plurality of roads from said first video data, the instructions in said program are specifically for: detecting whether an abnormal element appears on the road surface of each road in the plurality of roads according to the video images of the plurality of roads; if yes, determining that an abnormal condition occurs, and acquiring a target position where the abnormal element occurs, wherein the abnormal element comprises at least one of the following components: ponding, cracks, breakage, empty pits, missing well covers, well cover shifting and barriers.
In one possible example, the pavement anomaly detection system further includes a plurality of video patrol cars, the processor and the plurality of video patrol cars establishing a communication connection; in terms of the determination of the target road on which the abnormal situation occurs and the cause of the abnormality, the instructions in the program are specifically for performing the following operations: a first instruction is sent to a target video patrol car in the plurality of video patrol cars, wherein the first instruction carries a target position of the target road, and the first instruction is used for indicating the target video patrol car to go to the target road to confirm the abnormality of the target position; receiving a first video image of the target road, which is sent after the target video patrol car confirms the abnormality of the target position of the target road; and determining the abnormality reason of the target road according to the second video image of the target road in the first video data and the first video image.
In one possible example, in the determining of the cause of the abnormality of the target road from the second video image of the target road in the first video data and the first video image, the instructions in the program are specifically for: acquiring a plurality of images of the target road according to the first video image and the second video image, wherein the images are images of different angles of the target road; determining abnormal elements actually appearing in the target position according to the plurality of images; and acquiring target images comprising the truly-occurring abnormal elements in the plurality of images, and generating abnormal reasons of the target road, wherein the abnormal reasons comprise the truly-occurring abnormal elements and the target images.
The foregoing description of the embodiments of the present application has been presented primarily in terms of a method-side implementation. It will be appreciated that the processing device, in order to achieve the above-described functions, includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the application can divide the processing device into the functional units according to the method example, for example, each functional unit can be divided corresponding to each function, or two or more functions can be integrated into one control unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.
Fig. 6 is a block diagram showing the functional units of an abnormality detection apparatus 600 according to an embodiment of the present application. This anomaly detection device 600 is applied to the processor of the road surface anomaly detection system, the road surface anomaly detection system includes processor and a plurality of municipal cameras, the processor with a plurality of municipal cameras establish communication connection, anomaly detection device 600 includes processing unit 601 and communication unit 602, wherein:
the processing unit 601 is configured to receive first video data sent by the plurality of municipal cameras, where the first video data includes video images of a plurality of roads captured by the plurality of municipal cameras, and the plurality of municipal cameras are distributed on the plurality of roads; and determining whether an abnormal condition occurs on the plurality of roads according to the first video data; and if so, determining a target road with an abnormal condition and an abnormal reason of the abnormal condition; and the video AI analysis module is used for carrying out video AI analysis on the video image of the target road and the abnormal reason and pushing the video image of the target road and the abnormal reason to a municipal administration through an intelligent municipal system.
It can be seen that in the embodiment of the present application, the processing device first receives first video data sent by the plurality of municipal cameras, where the first video data includes video images of a plurality of roads captured by the plurality of municipal cameras, the plurality of municipal cameras are distributed on the plurality of roads, and then determines whether an abnormal condition occurs on the plurality of roads according to the first video data, then, if so, determines a target road on which the abnormal condition occurs and an abnormal cause of the abnormal condition, and finally, performs video AI analysis on the video images of the target road and the abnormal cause, and pushes the video images and the abnormal cause to a municipal administration department through an intelligent municipal system. The processing equipment of the pavement abnormality detection system can intelligently identify the target road with abnormal conditions and the abnormal reasons according to the first video data reported by the municipal cameras preset on a plurality of roads, so that video images of the target road and the abnormal reasons are reported to the municipal administration departments, the pavement abnormality early warning real-time performance is improved, the manual investigation work is reduced, and the safety rate and the traffic efficiency of road traffic are improved.
In one possible example, before the determining whether an abnormal situation occurs in the plurality of roads according to the first video data, the processing unit 601 is specifically configured to: detecting whether the capacity of the first video data is larger than a first preset threshold value; if yes, preprocessing a plurality of video images included in the first video data to obtain processed first video data, wherein the capacity of the processed first video data is smaller than the first preset threshold value, and the preprocessing comprises reducing the resolution of the video images and/or reducing the frame rate of the video images.
In one possible example, in the determining whether an abnormal situation occurs in the plurality of roads according to the first video data, the processing unit 601 is specifically configured to: detecting whether an abnormal element appears on the road surface of each road in the plurality of roads according to the video images of the plurality of roads; if yes, determining that an abnormal condition occurs, and acquiring a target position where the abnormal element occurs, wherein the abnormal element comprises at least one of the following components: ponding, cracks, breakage, empty pits, missing well covers, well cover shifting and barriers.
In one possible example, the pavement anomaly detection system further includes a plurality of video patrol cars, the processor and the plurality of video patrol cars establishing a communication connection; in terms of the determination of the target road on which the abnormal situation occurs and the cause of the abnormality, the processing unit 601 is specifically configured to: a first instruction is sent to a target video patrol car in the plurality of video patrol cars, wherein the first instruction carries a target position of the target road, and the first instruction is used for indicating the target video patrol car to go to the target road to confirm the abnormality of the target position; receiving a first video image of the target road, which is sent after the target video patrol car confirms the abnormality of the target position of the target road; and determining the abnormality reason of the target road according to the second video image of the target road in the first video data and the first video image.
In one possible example, in the aspect of determining the cause of abnormality of the target road based on the second video image of the target road in the first video data and the first video image, the processing unit 601 is specifically configured to: acquiring a plurality of images of the target road according to the first video image and the second video image, wherein the images are images of different angles of the target road; determining abnormal elements actually appearing in the target position according to the plurality of images; and acquiring target images comprising the truly-occurring abnormal elements in the plurality of images, and generating abnormal reasons of the target road, wherein the abnormal reasons comprise the truly-occurring abnormal elements and the target images.
Wherein the processing device may further comprise a storage unit 603, the processing unit 601 and the communication unit 602 may be a controller or a processor, and the storage unit 603 may be a memory.
The embodiment of the application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program makes a computer execute part or all of the steps of any one of the method embodiments, and the computer includes a mobile terminal.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the methods described in the method embodiments above. The computer program product may be a software installation package, said computer comprising a mobile terminal.
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.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or 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 an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over 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 of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one control unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. 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 memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (8)
1. The abnormality detection method is characterized by being applied to processing equipment of a pavement abnormality detection system, wherein the pavement abnormality detection system comprises a processor and a plurality of municipal cameras, and the processor and the municipal cameras are in communication connection; the method comprises the following steps:
Receiving first video data sent by the municipal cameras, wherein the first video data comprises video images of a plurality of roads shot by the municipal cameras, and the municipal cameras are distributed on the roads;
determining whether abnormal conditions occur on the multiple roads according to the first video data;
if yes, determining a target road with an abnormal condition and an abnormal reason of the abnormal condition;
carrying out video AI analysis on the video image of the target road and the abnormal reason, and pushing the video image of the target road and the abnormal reason to a municipal administration management department through an intelligent municipal system;
wherein before determining whether an abnormal situation occurs in the plurality of roads according to the first video data, the method further includes: detecting whether the capacity of the first video data is larger than a first preset threshold value; if yes, preprocessing a plurality of video images included in the first video data to obtain processed first video data, wherein the capacity of the processed first video data is smaller than the first preset threshold value, and the preprocessing comprises reducing the resolution of the video images and/or reducing the frame rate of the video images; wherein the video image subjected to the preprocessing is a part of video images in the plurality of video images, and the first video data after the processing includes: the other video images except the partial video image in the plurality of video images and the processed partial video image obtained by the preprocessing of the partial video image; the partial video image is determined by: dividing the plurality of videos by taking the road as a unit, and determining the video image corresponding to the target road in the plurality of videos as the partial video image if the number of the video images corresponding to the target road is higher than a preset number.
2. The method of claim 1, wherein determining whether an anomaly has occurred in the plurality of roads based on the first video data comprises:
detecting whether an abnormal element appears on the road surface of each road in the plurality of roads according to the video images of the plurality of roads;
if yes, determining that an abnormal condition occurs, and acquiring a target position where the abnormal element occurs, wherein the abnormal element comprises at least one of the following components: ponding, cracks, breakage, empty pits, missing well covers, well cover shifting and barriers.
3. The method of claim 1, wherein the pavement anomaly detection system further comprises a plurality of video patrol cars, the processor and the plurality of video patrol cars establishing a communication connection; the determining the target road with the abnormal condition and the abnormal reason of the abnormal condition comprises the following steps:
a first instruction is sent to a target video patrol car in the plurality of video patrol cars, wherein the first instruction carries a target position of the target road, and the first instruction is used for indicating the target video patrol car to go to the target road to confirm the abnormality of the target position;
Receiving a first video image of the target road, which is sent after the target video patrol car confirms the abnormality of the target position of the target road;
and determining the abnormality reason of the target road according to the second video image of the target road in the first video data and the first video image.
4. A method according to claim 3, wherein said determining the cause of the abnormality of the target link from the second video image of the target link in the first video data and the first video image comprises:
acquiring a plurality of images of the target road according to the first video image and the second video image, wherein the images are images of different angles of the target road;
determining abnormal elements actually appearing in the target position according to the plurality of images;
and acquiring target images comprising the truly-occurring abnormal elements in the plurality of images, and generating abnormal reasons of the target road, wherein the abnormal reasons comprise the truly-occurring abnormal elements and the target images.
5. The data transmission device is characterized by being applied to a processor of a pavement abnormality detection system, wherein the pavement abnormality detection system comprises the processor and a plurality of municipal cameras, and the processor and the municipal cameras are in communication connection; the data transmission device comprises a processing unit and a communication unit, wherein,
The processing unit is used for receiving first video data sent by the municipal cameras, the first video data comprise video images of a plurality of roads shot by the municipal cameras, and the municipal cameras are distributed on the roads; and determining whether an abnormal condition occurs on the plurality of roads according to the first video data; and if so, determining a target road with an abnormal condition and an abnormal reason of the abnormal condition; the video image of the target road and the abnormal reason are subjected to video AI analysis and pushed to a municipal administration department through an intelligent municipal system;
before determining whether an abnormal situation occurs in the plurality of roads according to the first video data, the processing unit is specifically configured to: detecting whether the capacity of the first video data is larger than a first preset threshold value; if yes, preprocessing a plurality of video images included in the first video data to obtain processed first video data, wherein the capacity of the processed first video data is smaller than the first preset threshold value, and the preprocessing comprises reducing the resolution of the video images and/or reducing the frame rate of the video images; wherein the video image subjected to the preprocessing is a part of video images in the plurality of video images, and the first video data after the processing includes: the other video images except the partial video image in the plurality of video images and the processed partial video image obtained by the preprocessing of the partial video image; the partial video image is determined by: dividing the plurality of videos by taking the road as a unit, and determining the video image corresponding to the target road in the plurality of videos as the partial video image if the number of the video images corresponding to the target road is higher than a preset number.
6. The data transmission device according to claim 5, wherein the processing unit is specifically configured to, in determining whether an abnormal situation occurs in the plurality of roads based on the first video data: detecting whether an abnormal element appears on the road surface of each road in the plurality of roads according to the video images of the plurality of roads; if yes, determining that an abnormal condition occurs, and acquiring a target position where the abnormal element occurs, wherein the abnormal element comprises at least one of the following components: ponding, cracks, breakage, empty pits, missing well covers, well cover shifting and barriers.
7. A processing device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-4.
8. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any of claims 1-4.
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