CN111741256B - Moving target detection method for power transmission line external damage prevention visualization device - Google Patents
Moving target detection method for power transmission line external damage prevention visualization device Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- 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
- G08B13/196—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 using television cameras
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B7/00—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00
- G08B7/06—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00 using electric transmission, e.g. involving audible and visible signalling through the use of sound and light sources
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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Abstract
The utility model provides a transmission line prevents outer broken visual device, includes photovoltaic electric energy collection and storage module, main control unit, video image collection module, reputation warning module and communication module. Each module is connected with the main control unit through a corresponding data exchange interface. By acquiring video images, a background updating algorithm based on statistical average is used for target detection, and whether a moving target exists is judged. The device is a load balancing control system based on network card redundancy design, and can realize self-adaptive control of two communication modes of a wireless network bridge and 4G. The device is based on a statistical average background updating algorithm, and only a previous background and a current image need to be weighted and averaged, so that the memory space is saved, and the device is practical in an environment with a stable background.
Description
Technical Field
The invention relates to a visualization device, in particular to a power transmission line external damage prevention visualization device and a moving target detection method.
Background
The power failure caused by external force damage mainly includes the following types: large machinery line collision, foreign body hanging short circuit, illegal construction damage, smoke and fire diffusion and the like. Aiming at the types, the external force damage condition is mainly monitored by adopting an internet of things technology and a novel sensing technology, and the external force damage condition comprises a laser ranging technology, a magnetic field monitoring technology, an infrared monitoring technology, a computer vision monitoring technology and the like.
When the transmission distance is long, the analog signal attenuation is serious, and the video image loss can be caused. Meanwhile, the storage mode of the magnetic video tape has higher cost and limited information quantity of stored video images.
The digital video monitoring technology adopts digital signals with strong anti-interference capability to transmit video images, and ensures that the signals are not distorted in the transmission process. But the network adaptability of the digital video monitoring technology is not strong.
The IP streaming media video monitoring technology gives full play to the advantages of the network, so that the action range of the video monitoring system is increased, and the video monitoring system is not limited by regions. The user can check the video by inputting the corresponding Web browser address, thereby facilitating the operation of the user. However, most video monitoring systems based on IP streaming media are in a data centralized processing mode, resulting in a large amount of invalid information. Under many engineering applications, monitoring content needs to be watched at any time, and the method is not limited by working environments and working places. Meanwhile, the collection of invalid information needs to be reduced, and the safety and the reliability of the video monitoring system are ensured. Therefore, a new visualization device for preventing external damage to the power transmission line based on the network video monitoring technology needs to be designed.
Disclosure of Invention
The invention aims to provide a visualization device for preventing an external damage of a power transmission line and a moving target detection method, which can realize the functions of video acquisition and transmission of the power transmission line, self-adaption of a network bridge and 4G communication, video stream output processing of a network camera and moving target detection preprocessing. The terminal distribution preprocessing mode can reduce the data transmission and processing capacity by more than 80%, and the efficiency ratio of the terminal distribution preprocessing mode is more than 6 times of that of the terminal distribution preprocessing mode.
In order to achieve the purpose, the invention adopts the following technical scheme:
a visualization device for preventing external damage of a power transmission line comprises a main control unit, a video image acquisition module and a communication module; the video image acquisition module and the communication module are connected with the main control unit;
the video image acquisition module is used for acquiring video stream data and sending the acquired video stream data to the main control unit;
the main control unit is used for updating the received video stream data by adopting a background updating algorithm based on statistical average and judging whether a moving target exists or not;
the communication module is used for transmitting the judgment result of the main control unit to the server.
The invention has the further improvement that the communication module consists of a wireless network bridge, a 4G communication module, a VPN channel and a Zigbee module.
The solar energy collecting and storing module comprises a photovoltaic solar panel, a solar energy power manager and a storage battery pack, wherein the photovoltaic solar panel and the storage battery pack are connected with the solar energy power manager, and the solar energy power manager is connected with the main control unit through a serial port.
The invention is further improved in that the video image acquisition module is a network camera.
The invention has the further improvement that the invention also comprises an acousto-optic alarm module for giving an alarm; the sound and light alarm module comprises a loudspeaker and an alarm lamp, and the loudspeaker and the alarm lamp are connected with the main control unit.
A moving target detection method for an external damage prevention visualization device of a power transmission line based on the above comprises the following steps:
the first step is as follows: acquiring video stream data through a video image acquisition module, capturing a current video stream packet, acquiring a current frame image through decoding, accumulating all images as background models, and sending the background models to a main control unit;
secondly, the master control unit updates the received background model to obtain an updated background model;
after the background updating is finished, judging whether the current frame is a static background; if the background is static, carrying out differential calculation on the current frame and the background to obtain the gray difference of each point; if the current frame is not a static background, motion compensation is carried out on the frame, then difference calculation is carried out on the current frame and the background, and the gray difference value of each point is obtained;
the third step: calculating the weight of each pixel point according to the gray difference value;
the fourth step: calculating the weight of each pixel point by utilizing the gray level difference value to update the updated background model, then carrying out binarization processing to obtain a binary image, if all pixels with gray levels larger than or equal to the gray level difference value T in the binary image are judged to belong to a specific object, otherwise, the pixel points are excluded from an object area; and then performing morphological processing, extracting boundary and connected components, performing connected domain analysis, and marking target pixels so as to judge whether a moving target exists.
In a further development of the invention, in a first step, the background model is represented by the following formula:
wherein n represents the number of picture frames, IiCharacteristic value, B, representing pixel point of ith frame imagenRepresenting the background model established when the image of the nth frame is acquired.
In a further development of the invention, in the second step, the updating is carried out using the following formula:
wherein n represents the number of picture frames, In+1Characteristic value, B, representing pixel point of n +1 th frame imagenRepresenting a background model established when the image of the nth frame is acquired, Bn+1And representing a background model established when the (n + 1) th frame image is acquired.
The invention has the further improvement that the concrete process of the third step is as follows:
setting a gray difference value T, and accumulating a counter w when the change of a certain pixel point in the image is greater than the gray difference value T; if the continuous N frames of the accumulation counter w are all larger than the updated background Bn+1And then, carrying out weighting processing on the pixel point:
where δ is a weighting coefficient, n represents the number of image frames, In+1Characteristic value, B, representing pixel point of n +1 th frame imagenRepresenting a background model established when the image of the nth frame is acquired, Bn+1Representing a background model established when the n +1 frame image is acquired;
in the formula:
n represents successive N frame images;
the weighting coefficient δ is obtained as follows: if the change of the pixel P is larger than the gray difference T in the continuous N frames of images, a weighting coefficient delta is multiplied by the current frame when the background is updated, wherein the weighting coefficient delta is max, and otherwise, the weighting coefficient delta is 1.
The invention is further improved in that the method also comprises a fifth step of: continuously capturing the current video stream packet, and repeating the first step to the fourth step to realize the real-time monitoring of the moving target detection.
Compared with the prior art, the invention has the following beneficial effects: the invention is based on the network video monitoring technology, gives full play to the advantages of the network, can watch the monitored content at any working environment and working place at any time in real time, and effectively makes up the limitations that the current terminal has single communication mode, poor compatibility of an interactive interface and can not filter out invalid video information. The onboard network card resources can be effectively integrated, the network card load is subjected to feedback control according to the signal intensity, onboard bandwidth resources are shared, and the self-adaptive control function of wireless network bridge communication and fourth-generation mobile network communication is realized. The background updating algorithm based on statistical average can effectively improve the sensitivity of background modeling and the identification precision of the detection algorithm, and when the weight change threshold is 20, the identification accuracy is improved by more than 10% compared with a classical method. The terminal distribution preprocessing mode can filter hidden danger-free video information, reduce data transmission and processing capacity by more than 80%, and the efficiency ratio of the terminal distribution preprocessing mode to the centralized processing mode is more than 6 times, so that data transmission and computing resources can be effectively saved, and the monitoring efficiency of the external force damage system is improved.
Drawings
Fig. 1 is a general schematic diagram of a visualization device for preventing external damage to a power transmission line;
fig. 2 is a schematic structural connection diagram of the visualization device for preventing the external damage of the power transmission line.
FIG. 3 is a schematic structural diagram of a master control unit according to the present invention;
fig. 4 is a flowchart of the background update moving object detection method based on statistical averaging according to the present invention.
In the figure, 1 is a main control unit, 2 is a photovoltaic electric energy acquisition and storage module, 3 is a video image acquisition module, 4 is an acousto-optic alarm module, and 5 is a communication module.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1 and 2, the visualization device for preventing the external damage of the power transmission line is arranged on an iron tower and positioned on the outer side of the power transmission line. The device comprises a photovoltaic electric energy acquisition and storage module 2, a main control unit 1, a video image acquisition module 3, an acousto-optic alarm module 4 and a communication module 5. The photovoltaic electric energy acquisition and storage module 2, the main control unit 1, the video image acquisition module 3, the sound and light alarm module 4 and the communication module 5 are connected with the main control unit 1 through respective corresponding data exchange interfaces. The method comprises the following specific steps:
the communication module consists of a wireless network bridge, a 4G communication module, a VPN channel and a Zigbee module;
the photovoltaic electric energy collecting and storing module comprises a photovoltaic solar panel, a solar power supply manager and a storage battery pack, wherein the photovoltaic solar panel and the storage battery pack are connected with the solar power supply manager, and the solar power supply manager is connected with the main control unit through a serial port;
the photovoltaic electric energy acquisition and storage module is used for supplying power to the main control unit.
The video image acquisition module is a network camera;
the sound and light alarm module comprises a loudspeaker and an alarm lamp, and the loudspeaker and the alarm lamp are connected with the main control unit; the sound and light alarm module is used for giving an alarm when a moving target exists, and does not give an alarm when the moving target does not exist.
The initialization of the main control unit in the invention comprises the following steps:
1) referring to fig. 3, the power module, the program download USB mode interface, the real-time clock module, the ethernet interface module, the 4G module, and the relay module are all connected to the main control unit, which includes a core board.
2) Setting system configuration files and initializing port data.
3) Opening port mapping, and establishing data connection between a network segment of the network camera and a network segment of the video monitoring master station;
4) a VPN channel is opened, and the device is inserted into a 4G Internet of things card to connect with a 4G network, so that the device establishes data connection with a video monitoring master station through the 4G network;
5) and the network card corresponding to the wireless network bridge and the virtual network card generated by the 4G module are used as network card redundancy design, the two network cards are bound through a bound interface, parameters such as network delay of each network card are collected through the link connection state, and the information transmission load of the double network cards is fed back and controlled.
6) The device starts a video acquisition function, establishes an inter-process communication pipeline, acquires video stream data packets acquired by the network camera interaction thread and judges whether a moving target exists in a monitoring range by using a background updating algorithm based on statistical average.
Referring to fig. 4, the method for detecting a moving object of channel video data based on a background update algorithm of statistical average includes the following steps:
the first step is as follows: establishing an interprocess communication pipeline: acquiring video stream data acquired by a network camera interaction thread through a video image acquisition module (a network camera), capturing a current video stream packet, acquiring current frame images through decoding, accumulating all the images as background models, and sending the background models to a main control unit;
the background model can be expressed by the following formula:
wherein n represents the number of picture frames, IiCharacteristic value, B, representing pixel point of ith frame imagenRepresenting the background model established when the image of the nth frame is acquired.
And secondly, updating the background, and updating the background model by adopting the following formula to obtain an updated background model:
wherein n represents the number of picture frames, In+1Characteristic value, B, representing pixel point of n +1 th frame imagenRepresenting a background model established when the image of the nth frame is acquired, Bn+1And representing a background model established when the (n + 1) th frame image is acquired.
If the background updating is finished, judging whether the current frame is a static background. If the background is static, carrying out differential calculation on the current frame and the background to obtain the gray difference of each point; if the current frame is not a static background, motion compensation is carried out on the current frame, then difference operation is carried out on the current frame and the background, and the gray difference value of each point is obtained.
The third step: calculating the weight of each pixel point according to the gray difference value, and providing weight reference for updating the background next time;
and setting a gray difference value T, and accumulating the counter w when the change of a certain pixel point in the image is greater than the gray difference value T. If the continuous N frames of the accumulation counter w are all larger than the updated background Bn+1And then, carrying out weighting processing on the pixel point:
where δ is a weighting coefficient, n represents the number of image frames, In+1Characteristic value, B, representing pixel point of n +1 th frame imagenRepresenting a background model established when the image of the nth frame is acquired, Bn+1And representing a background model established when the (n + 1) th frame image is acquired.
In the formula:
n denotes consecutive N frame images.
The weighting coefficient δ is obtained as follows: if the change of the pixel P is larger than the gray difference T in the continuous N frames of images, a weighting coefficient delta is multiplied by the current frame when the background is updated, wherein the weighting coefficient delta is max, and otherwise, the weighting coefficient delta is 1. By adjusting the values of N and max, the degree to which the algorithm is sensitive to background changes during the background update can be changed.
The fourth step: calculating the weight of each pixel point by utilizing the gray level difference value to update the updated background model, then carrying out binarization processing to obtain a binary image, if all pixels with gray levels larger than or equal to the gray level difference value T in the binary image are judged to belong to a specific object, otherwise, the pixel points are excluded from an object area; and then performing morphological processing, extracting boundary and connected components, performing connected domain analysis, and marking target pixels so as to judge whether a moving target exists. And if the moving target exists, requesting the background to further diagnose, and if no moving target exists, waiting for normal polling detection of the background.
The fifth step: and continuously grabbing the current video stream packet, and repeating the steps.
And when the current video stream packet is continuously captured, starting a watchdog counter, feeding the watchdog once every 3s, resetting the watchdog timer, and checking the running state of each process. And if the operation is abnormal, restarting the abnormal process.
The general design of the present invention is shown in figure 1. The invention carries out preprocessing on the collected video image, the distributed preprocessing mode reduces the data transmission and processing amount by more than 80 percent, and the efficiency ratio of the distributed preprocessing mode is more than 6 times of that of the centralized processing mode.
The structure of the main control unit of the present invention referring to fig. 2, the connection relationship of each component of the present invention needs to be given. The power supply voltage is 9-18V, the peripheral control module is provided with 4 relays for controlling the network camera, the wireless network bridge, the loudspeaker and the alarm lamp, and the two network ports of the Ethernet module are respectively connected with the wireless network bridge and the network camera.
Compared with a classical method, the recognition accuracy of the background updating algorithm based on statistical average is improved by more than 10%.
The invention uses a background updating algorithm based on statistical average to detect the target by collecting the video image, and further judges whether a moving target exists. The device is a load balancing control system based on network card redundancy design, and can realize self-adaptive control of two communication modes of a wireless network bridge and 4G. The device is based on a statistical average background updating algorithm, and only a previous background and a current image need to be weighted and averaged, so that the memory space is saved, and the device is practical in an environment with a stable background.
Claims (4)
1. A moving target detection method for a power transmission line external damage prevention visualization device is characterized by comprising the following steps:
the first step is as follows: acquiring video stream data through a video image acquisition module, capturing a current video stream packet, acquiring a current frame image through decoding, accumulating all images as background models, and sending the background models to a main control unit;
secondly, the master control unit updates the received background model to obtain an updated background model;
after the background updating is finished, judging whether the current frame is a static background; if the background is static, carrying out differential calculation on the current frame and the background to obtain the gray difference of each point; if the current frame is not a static background, motion compensation is carried out on the frame, then difference calculation is carried out on the current frame and the background, and the gray difference value of each point is obtained;
the third step: calculating the weight of each pixel point according to the gray difference value; the specific process is as follows:
setting a gray difference value T, and accumulating a counter w when the change of a certain pixel point in the image is greater than the gray difference value T; if the continuous N frames of the accumulation counter w are all larger than the updated background Bn+1And then, carrying out weighting processing on the pixel point:
where δ is a weighting coefficient, n represents the number of image frames, In+1Characteristic value, B, representing pixel point of n +1 th frame imagenRepresenting a background model established when the image of the nth frame is acquired, Bn+1Representing a background model established when the n +1 frame image is acquired;
in the formula:
n represents successive N frame images;
the weighting coefficient δ is obtained as follows: if the change of the pixel P is larger than the gray difference T in the continuous N frames of images, multiplying a weighting coefficient delta max to the current frame when updating the background, otherwise, multiplying the weighting coefficient delta max to 1;
the fourth step: calculating the weight of each pixel point by utilizing the gray level difference value to update the updated background model, then carrying out binarization processing to obtain a binary image, if all pixels with gray levels larger than or equal to the gray level difference value T in the binary image are judged to belong to a specific object, otherwise, the pixel points are excluded from an object area; and then performing morphological processing, extracting boundary and connected components, performing connected domain analysis, and marking target pixels so as to judge whether a moving target exists.
2. The moving object detecting method according to claim 1, wherein in the first step, the background model is expressed by the following formula:
wherein n represents the number of picture frames, IiCharacteristic value, B, representing pixel point of ith frame imagenRepresenting the background model established when the image of the nth frame is acquired.
3. The moving object detecting method according to claim 1, wherein in the second step, the updating is performed by using the following formula:
wherein n represents the number of picture frames, In+1Characteristic value, B, representing pixel point of n +1 th frame imagenRepresenting a background model established when the image of the nth frame is acquired, Bn+1And representing a background model established when the (n + 1) th frame image is acquired.
4. The moving object detecting method according to claim 1, characterized by further comprising a fifth step of: continuously capturing the current video stream packet, and repeating the first step to the fourth step to realize the real-time monitoring of the moving target detection.
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