CN113255494A - Method and system for monitoring illegally-invaded vehicle in real time - Google Patents
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Abstract
The embodiment of the invention relates to a method and a system for monitoring illegally-invaded vehicles in real time. The method for monitoring the illegally-invaded vehicle in real time firstly uses a video acquisition module to acquire an image of an interested area in real time; then aiming at the collected images, using a trained deep learning detection model to identify whether a vehicle type corresponding to an illegally-invaded vehicle exists in the images, if not, continuing to collect and identify the images, if so, storing the images of the illegally-invaded vehicle, the illegally-invaded position and the illegally-invaded time, and sending a warning trigger signal; and finally, carrying out intrusion warning on a monitor when the warning trigger signal is received, and displaying the illegal intrusion vehicle image, the illegal intrusion position and the illegal intrusion time to the monitor. The invention can monitor the illegally-invaded vehicle simply, accurately and efficiently with low cost, and can avoid the loss caused by the illegally-invaded vehicle.
Description
Technical Field
The embodiment of the invention relates to the field of security monitoring, in particular to a method and a system for monitoring illegally-invaded vehicles in real time.
Background
In the daily cultivation of a traditional farm and pasture, such as a pig farm, feed transportation vehicles such as trolleys, tricycles and feed trolleys are generally used for carrying feed, but otherwise illegal vehicle intrusion and illegal behaviors (such as stealing, toxicating and the like) after the intrusion cause great influence and property loss. At present, pig farms ensure the order of pig farms and the safety of pigs mainly through manual patrol and other modes, but the manual mode is time-consuming and labor-consuming, and cannot be monitored comprehensively under the condition that the pig farms are large, so that potential safety hazards exist.
Even if there is the pig farm owner and installs the camera in the pig farm, also just discover to invade the vehicle through the image stream that the monitoring camera was shot is watched to the manual work, this can increase monitor person's control burden, also easily overlook and miss the vehicle of invading in addition.
Therefore, a method and a system for real-time monitoring of illegally-intruding vehicles are needed to simply, accurately and efficiently monitor illegally-intruding vehicles at low cost, and thus a technical problem to be solved in the industry is urgently needed.
Disclosure of Invention
To solve the problems of the prior art, at least one embodiment of the present invention provides a method and system for monitoring an illegally-invaded vehicle in real time.
In a first aspect, an embodiment of the present invention provides a method for monitoring an illegally-invaded vehicle in real time, including:
the method comprises the following steps of firstly, acquiring an image of an interested area in real time by using a video acquisition module;
step two, aiming at the collected images, using a trained deep learning detection model to identify whether the images have vehicle types corresponding to illegally invaded vehicles, if so, continuing the step three, and if not, returning to the step one;
storing an illegal vehicle image, an illegal invasion position and illegal invasion time, and sending a warning trigger signal; and
and fourthly, carrying out intrusion warning on a monitor when the warning trigger signal is received, and displaying the illegal intrusion vehicle image, the illegal intrusion position and the illegal intrusion time to the monitor.
In some embodiments, when the trained deep learning detection model is used to identify a corresponding vehicle type of an illegally-invaded vehicle in the image in the second step, an invaded vehicle frame is generated for the illegally-invaded vehicle, and the illegally-invaded vehicle image in the third step includes the invaded vehicle frame.
In some embodiments, the deep learning detection model in step two includes a YoloV4 neural network and a YoloV5 neural network.
In some embodiments, the area of interest is a farm and pasture doorway or a pig house entrance, the monitor is a farm and pasture worker or a farm and pasture owner, and the vehicle is a non-forage truck, including a car, an off-road vehicle, a truck, and a van.
In some embodiments, the video capture module includes a plurality of cameras aligned with the region of interest and a setting module, the monitor sets monitoring sub-regions for each camera through the setting module, and each camera captures images of its corresponding monitoring sub-region.
In a second aspect, an embodiment of the present invention further provides a system for monitoring an illegally-invaded vehicle in real time, including:
the video acquisition module is used for acquiring images of the region of interest in real time;
the intrusion detection module is used for identifying whether the vehicle type corresponding to the illegally-intruded vehicle exists in the image or not by using the trained deep learning detection model aiming at the acquired image, if so, sending an intrusion trigger signal, and if not, continuing to acquire and identify the image;
the intrusion processing module is used for storing corresponding illegal intrusion vehicle images, illegal intrusion positions and illegal intrusion time when the intrusion trigger signals are received and sending warning trigger signals; and
and the intrusion warning module is used for carrying out intrusion warning on a monitor when the warning trigger signal is received and displaying the illegal intrusion vehicle image, the illegal intrusion position and the illegal intrusion time to the monitor.
In some embodiments, the intrusion detection module generates an intrusion vehicle frame for the illegally-intruded vehicle when the trained deep learning detection model is used to identify a corresponding vehicle type of the illegally-intruded vehicle in the image, and the intrusion processing module stores the illegally-intruded vehicle image including the intrusion vehicle frame.
In some embodiments, the deep learning detection model includes a YoloV4 neural network and a YoloV5 neural network.
In some embodiments, the intrusion alert module includes an alert unit configured to alert a monitor of intrusion upon receiving the alert trigger signal, and a display unit configured to display the illegally-intruding vehicle image, the illegally-intruding position, and the illegally-intruding time to the monitor.
In some embodiments, the area of interest is a farm and pasture doorway or a pig house entrance, the monitor is a farm and pasture worker or a farm and pasture owner, and the vehicle is a non-forage truck, including a car, an off-road vehicle, a truck, and a van.
In some embodiments, the video capture module includes a plurality of cameras aligned with the region of interest and a setting module, the monitor sets monitoring sub-regions for each camera through the setting module, and each camera captures images of its corresponding monitoring sub-region.
Compared with the prior art that no effective monitoring means exists for vehicles illegally invading a farm or a pigsty, the method for monitoring the vehicles illegally invading in real time firstly uses the video acquisition module to acquire the images of the interested areas in real time; then aiming at the collected images, using a trained deep learning detection model to identify whether a vehicle type corresponding to an illegally-invaded vehicle exists in the images, if not, continuing to collect and identify the images, if so, storing the images of the illegally-invaded vehicle, the illegally-invaded position and the illegally-invaded time, and sending a warning trigger signal; and finally, carrying out intrusion warning on a monitor when the warning trigger signal is received, and displaying the illegal intrusion vehicle image, the illegal intrusion position and the illegal intrusion time to the monitor. The invention can monitor the illegally-invaded vehicle simply, accurately and efficiently with low cost, and can avoid the loss caused by the illegally-invaded vehicle.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a schematic structural diagram illustrating a system for monitoring an illegally-invaded vehicle in real time according to an embodiment of the present invention;
FIG. 2 is an exemplary schematic view of an image 140A of an intruded vehicle from FIG. 1; and
fig. 3 is a schematic flowchart of a method for monitoring an illegally-invaded vehicle in real time according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Referring to fig. 1, there is shown a constituent structure of a system 1 for monitoring an illegally-intruding vehicle in real time in an embodiment of the present invention. The illegally-intruding vehicle is a non-feed transport vehicle, namely, a motor vehicle except a small handcart, a tricycle and a feed trolley, and comprises various four-wheel and above motor vehicles such as a car, an off-road vehicle, a truck and a van. The illegal vehicle can be set and changed by a monitor according to specific conditions. As shown in fig. 1, a system 1 for monitoring illegally-intruding vehicles in real time includes a video capture module 10, an intrusion detection module 12, an intrusion processing module 14, and an intrusion alert module 16. The respective components of the system 1 for monitoring an intruded vehicle in real time will be described in detail below.
The video capture module 10 is used to capture images of the region of interest in real time. The video acquisition module 10 includes a plurality of cameras 100 aligned with an area of interest and a setting module 102, the monitor sets monitoring sub-regions for each camera 100 through the setting module 102, and each camera 100 performs image acquisition for its corresponding monitoring sub-region. The region of interest can be a gate of a farming and pasture or a pigsty entrance (as shown in fig. 2), and the region of interest can be set and changed by a monitor according to actual conditions.
The intrusion detection module 12 is configured to identify, by using the deep learning detection model 120 that completes training for the image acquired by the video acquisition module 10, whether a vehicle type corresponding to an illegally-intruding vehicle exists in the image, if so, send an intrusion trigger signal, and if not, continue to perform image acquisition and identification. When the intrusion detection module 12 identifies an intruding vehicle in an image by using the deep learning detection model 120 after training, the deep learning detection model 120 generates an intruding vehicle frame 120A as shown in fig. 2 for the intruding vehicle, and the deep learning detection model 120 may be a YoloV4 neural network or a YoloV5 neural network.
The YOLOv5 neural network is the most advanced target detection network in 2020, has higher speed and higher precision, and can reduce the calculation amount and improve the detection speed by using the CSPDarknet53 network. The method is characterized in that the Neck part of the YOLOv5 neural network is added with FPN and PAN enhancement feature fusion to enhance the small target detection effect, a new box loss function is used to accelerate model convergence and improve box accuracy, meanwhile, multiple data enhancement technologies are added in the aspect of data processing, the network detection accuracy is improved by the technologies such as Mosaic and Mixup, and models with multiple sizes are set to flexibly select different models according to different computational powers. The image of the region of interest is input to a YOLOv5 neural network, coordinates of the intruding vehicle frame 120A are output if an illegally intruding vehicle appears in the image, and a null is fed back if no illegally intruding vehicle appears, and detection of a situation where a plurality of illegally intruding vehicles simultaneously appear is supported.
The intrusion processing module 14 includes a database 140, and the intrusion processing module 14 stores the illegally-intruding vehicle image 140A, the illegally-intruding location 140B, and the illegally-intruding time 140C into the database 140 upon receiving the intrusion trigger signal, and transmits an alert trigger signal to the intrusion alert module 16. The intrusion position 140B may be a monitoring sub-region corresponding to the camera 100, and may specifically be determined by an identification code of the camera, for example, the image in fig. 2 is an image acquired by the No. 2 camera for the monitoring sub-region corresponding to the camera, and the intrusion position may be determined by determining the monitoring sub-region corresponding to the No. 2 camera.
Fig. 2 is a schematic diagram of processing the stored illegally-invaded vehicle image by the invasion processing module 14 in fig. 1, as shown in fig. 2, an invaded vehicle frame 120A is included in the illegally-invaded vehicle image 140A, and the invaded vehicle frame 120A is obtained by identifying the image of the region of interest captured by the video capture module 10 by the deep learning detection model 120, the deep learning detection model 120 is exemplarily a YoloV5 neural network, and the invaded vehicle frame 120A accurately and completely selects a human body therein.
The intrusion alert module 16 is configured to alert a monitor of intrusion upon receiving the alert trigger signal, and display an illegally-intruding vehicle image 140A, an illegally-intruding location 140B, and an illegally-intruding time 140C to the monitor. The monitor is a farm and pasture worker or a farm and pasture owner.
Fig. 3 is a schematic flowchart of a method for monitoring an illegally-invaded vehicle in real time according to an embodiment of the present invention. Referring to fig. 3, with combined reference to fig. 1-2, the method 30 for real-time monitoring of an illegally-invaded vehicle first proceeds to step S300, and captures an image of a region of interest in real time using the video capturing module 10. In step S300, the region of interest is a gate of a farm and pasture or a pigsty entrance, as shown in fig. 1, the video capture module 10 includes a plurality of cameras 100 aligned with the region of interest, the monitor can set monitoring sub-regions for each camera 100 through the setting module 102 of the video capture module 10, and each camera 100 captures an image of its corresponding monitoring sub-region.
The method 30 for monitoring the illegally-intruding vehicle in real time continues to step S310, and for the acquired image, the trained deep learning detection model 120 is used to identify whether there is a vehicle type corresponding to the illegally-intruding vehicle in the image, if so, the step S320 is continued, otherwise, the step S300 is returned to. When an intruding vehicle is identified in the image by using the trained deep learning detection model 120 in step S310, an intruding vehicle frame 120A shown in fig. 2 is generated for the illegally intruding vehicle, and the deep learning detection model 120 includes a YoloV4 neural network and a YoloV5 neural network. The illegally-intruding vehicle in step S310 is a non-forage transport vehicle, that is, a motor vehicle other than a small cart, a tricycle, and a forage cart, and the illegally-intruding vehicle includes four or more wheels of a car, an off-road vehicle, a truck, a van, and the like.
The method 30 for real-time monitoring of the illegally-invaded vehicle continues to step S320, where the illegally-invaded vehicle image 140A, the illegally-invaded location 140B and the illegally-invaded time 140C are stored in the database 140, and the warning trigger signal is sent. The illegally cut-in vehicle image 140A in step S320 includes the cut-in vehicle frame 120A shown in fig. 2.
The method 30 for monitoring an illegally-invaded vehicle in real time continues to step S330, and performs an invasion warning to the monitor when the warning trigger signal is received, and displays an illegally-invaded vehicle image 140A, an illegally-invaded position 140B, and an illegally-invaded time 140C to the monitor. The monitor in step S330 is a farm and pasture worker or a farm and pasture owner, and the intrusion warning may be performed by a voice manner or a voice and light mixed manner.
The method for monitoring the illegally-invaded vehicle in real time in the embodiment of the invention firstly uses a video acquisition module to acquire the image of the region of interest in real time; then aiming at the collected images, using a trained deep learning detection model to identify whether a vehicle type corresponding to an illegally-invaded vehicle exists in the images, if not, continuing to collect and identify the images, if so, storing the images of the illegally-invaded vehicle, the illegally-invaded position and the illegally-invaded time, and sending a warning trigger signal; and finally, carrying out intrusion warning on a monitor when the warning trigger signal is received, and displaying the illegal intrusion vehicle image, the illegal intrusion position and the illegal intrusion time to the monitor.
The invention can monitor the illegally-invaded vehicle simply, accurately and efficiently with low cost, and can avoid the loss caused by the illegally-invaded vehicle.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the execution sequence of the steps of the method embodiments can be arbitrarily adjusted unless there is an explicit precedence sequence. The disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Those skilled in the art will appreciate that although some embodiments described herein include some features included in other embodiments instead of others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A method for real-time monitoring of illegally intruded vehicles, comprising:
the method comprises the following steps of firstly, acquiring an image of an interested area in real time by using a video acquisition module;
step two, aiming at the collected images, using a trained deep learning detection model to identify whether the images have vehicle types corresponding to illegally invaded vehicles, if so, continuing the step three, and if not, returning to the step one;
storing an illegal vehicle image, an illegal invasion position and illegal invasion time, and sending a warning trigger signal; and
and fourthly, carrying out intrusion warning on a monitor when the warning trigger signal is received, and displaying the illegal intrusion vehicle image, the illegal intrusion position and the illegal intrusion time to the monitor.
2. The method according to claim 1, wherein in the second step, when the trained deep learning detection model is used to identify the corresponding vehicle type of the illegally-invaded vehicle in the image, an invaded vehicle frame is generated for the illegally-invaded vehicle, and the frame is included in the illegally-invaded vehicle image in the third step.
3. The method for monitoring illegally-invasive vehicles in real time according to claim 1 or 2, wherein the deep learning detection model in the second step includes a YoloV4 neural network and a YoloV5 neural network.
4. The method according to claim 1, wherein the area of interest is a farm-pasture doorway or a pig house entrance, the monitor is a farm-pasture worker or a farm-pasture owner, the vehicle is a non-forage carrier vehicle, and the vehicle comprises a car, an off-road vehicle, a truck, and a van.
5. The method for real-time monitoring of illegally-invasive vehicles according to claim 4, wherein the video acquisition module comprises a plurality of cameras aligned with the region of interest and a setting module, the monitor sets a monitoring sub-region for each camera through the setting module, and each camera performs image acquisition for its corresponding monitoring sub-region.
6. A system for real-time monitoring of illegally intruded vehicles, comprising:
the video acquisition module is used for acquiring images of the region of interest in real time;
the intrusion detection module is used for identifying whether the vehicle type corresponding to the illegally-intruded vehicle exists in the image or not by using the trained deep learning detection model aiming at the acquired image, if so, sending an intrusion trigger signal, and if not, continuing to acquire and identify the image;
the intrusion processing module is used for storing corresponding illegal intrusion vehicle images, illegal intrusion positions and illegal intrusion time when the intrusion trigger signals are received and sending warning trigger signals; and
and the intrusion warning module is used for carrying out intrusion warning on a monitor when the warning trigger signal is received and displaying the illegal intrusion vehicle image, the illegal intrusion position and the illegal intrusion time to the monitor.
7. The system for monitoring illegally-intruding vehicles in real time according to claim 6, wherein the intrusion detection module generates an intruding vehicle frame for the illegally-intruding vehicle when a corresponding vehicle type of the illegally-intruding vehicle is identified in the image by using a trained deep learning detection model, and the intrusion processing module stores the images of the illegally-intruding vehicle, wherein the images of the illegally-intruding vehicle comprise the intruding vehicle frame.
8. The system for real-time monitoring of illegally-invasive vehicles according to claim 6, wherein the deep learning detection model includes a YoloV4 neural network and a YoloV5 neural network; the intrusion warning module comprises a warning unit and a display unit, the warning unit is used for receiving intrusion warning to a monitor when the warning trigger signal is received, and the display unit is used for displaying the illegally-intruding vehicle image, the illegally-intruding position and the illegally-intruding time to the monitor.
9. The system according to claim 6, wherein the area of interest is a farm or pasture doorway or a pig house entrance, the monitor is a farm or pasture worker or a farm or pasture owner, the vehicle is a non-forage carrier vehicle, and the vehicle comprises a car, an off-road vehicle, a truck, and a van.
10. The system for real-time monitoring of illegally-invasive vehicles according to claim 6, wherein the video acquisition module comprises a plurality of cameras aligned with the region of interest and a setting module, the monitor sets a monitoring sub-region for each camera through the setting module, and each camera performs image acquisition for its corresponding monitoring sub-region.
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| CN115937784A (en) * | 2022-12-27 | 2023-04-07 | 正大农业科学研究有限公司 | Farm monitoring method and device, electronic equipment and storage medium |
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| KR101923900B1 (en) * | 2018-10-10 | 2018-11-30 | 대신네트웍스 주식회사 | Multipurpose system of monitoring illegal trash dumping |
| CN110022379A (en) * | 2019-04-23 | 2019-07-16 | 翔创科技（北京）有限公司 | A kind of livestock monitoring system and method |
| CN110348303A (en) * | 2019-06-06 | 2019-10-18 | 武汉理工大学 | A kind of auxiliary water surface patrol system being equipped on unmanned boat and water surface monitoring method |
| CN110691224A (en) * | 2019-10-31 | 2020-01-14 | 上海电力大学 | Transformer substation perimeter video intelligent detection system |
| CN111310736A (en) * | 2020-03-26 | 2020-06-19 | 上海同岩土木工程科技股份有限公司 | Rapid identification method for unloading and piling of vehicles in protected area |
| CN111523401A (en) * | 2020-03-31 | 2020-08-11 | 河北工业大学 | Method for recognizing vehicle type |
| CN112804489A (en) * | 2020-12-31 | 2021-05-14 | 重庆文理学院 | Internet + -based intelligent construction site management system and method |
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| CN115937784A (en) * | 2022-12-27 | 2023-04-07 | 正大农业科学研究有限公司 | Farm monitoring method and device, electronic equipment and storage medium |
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