CN113705442A - Outdoor large-board advertising picture monitoring and identifying system and method - Google Patents
Outdoor large-board advertising picture monitoring and identifying system and method Download PDFInfo
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Abstract
The invention discloses a system and a method for monitoring and identifying an advertising picture of an outdoor large billboard, wherein the system comprises a main control development board, a GPS module, a relay module, a camera module, a transceiving module, a server and a client; the camera module is connected with the main control development board through the relay module, and transmits the picture image to the main control development board after collecting the outdoor large billboard advertising picture image; the collected time is controlled by the relay module; the GPS module is connected with the main control development board and sends the corresponding position of the outdoor large-board advertisement to the main control development board; the main control development board is connected with the transceiving module, the outdoor large billboard advertising picture image and the position data positioned by the GPS module are sent to the server through the transceiving module, and the server analyzes, processes and stores the received data; the server is installed in a remote central control room; the client is connected with the server. The invention can monitor the condition of the outdoor large billboard advertising picture in real time and reduce the operation cost.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a system and a method for monitoring and identifying an outdoor large billboard advertising picture.
Background
The outdoor large billboard is commonly used in outdoor scenes such as urban roads, expressways, building floors and the like, and is characterized in that the billboard is of a steel structure, is usually 18 meters or more away from the ground, and once an advertisement picture is abnormal, a professional climber with high-altitude operation needs to repair the advertisement picture or repair an advertisement spotlight;
because the billboard is in an outdoor environment, wind, rain and the billboard is easily affected by extreme weather (such as typhoon, thunderstorm and the like), if the advertising picture is damaged or a mechanical device fails, the advertising effect of an advertiser is often seriously affected, even in appointed advertising picture release time, malicious replacement and bad industry of repeated sale are easily caused, and further the trust between an advertising media company and the advertiser is reduced, and the problem of service doubt is caused; meanwhile, the outdoor advertising board is wide in distribution area, far away from the ground, high in cost of manual inspection and monitoring time and low in accuracy, and advertising pictures are often not notified in time when abnormal.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an outdoor large-billboard advertising picture monitoring and identifying system, so that the condition of the outdoor large-billboard advertising picture can be known without manual inspection, the working efficiency can be improved, and the operation cost can be reduced.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a monitoring and identifying system and method for an outdoor large billboard advertising picture comprises a main control development board, a GPS module, a relay module, a camera module, a transceiver module, a server and a client;
wherein the content of the first and second substances,
the camera module is connected with the main control development board through the relay module, and transmits an outdoor large billboard advertisement picture image to the main control development board after the picture image is collected; the collected time is controlled by the relay module;
the GPS module is connected with the main control development board and sends the position of the outdoor large-board advertisement corresponding to the GPS module to the main control development board;
the main control development board is connected with the transceiving module, the outdoor large billboard advertising picture image and the position data positioned by the GPS module are sent to the server through the transceiving module, and the server analyzes, processes and stores the received data;
the server is installed in a remote central control room;
the client is connected with the server, and the outdoor large billboard advertising picture is remotely monitored through the server.
Further, the camera module adopts 1.6mm super wide angle fisheye camera module.
Further, the client is any one of a smart phone, a tablet computer, a notebook computer and a desktop computer.
Further, the transceiver module adopts any one of 4G and 5G, NB-IoT.
Further, the main control development board processes the chip by adopting RK 3288.
In order to achieve the above object, the present invention further provides a method for monitoring and identifying an outdoor billboard advertisement picture, comprising the following steps:
s1, the main control development board carries out self-checking, the GPS module and the transceiver module are operated, the server receives the state codes of the modules, and online monitoring of the modules is realized;
s2, the client updates the billboard positioning information, and the user sets the snapshot time point, the snapshot interval time and the advertisement release time period of the target advertiser for each billboard picture;
s3, the master control development board acquires the current world time, judges whether the current world time enters a preset image capturing time point, and if the current world time does not enter the preset image capturing time point, the relay module and the camera module continue to enter a normal standby working state; if the preset image snapshot time point is judged to be entered, the main control development board sends an instruction to start the relay module, the camera module is electrified to operate, and snapshot return of image data of the advertising picture of the corresponding outdoor large billboard is carried out;
s4, the snap-shot image is transmitted back to the main control development board, after the image data are completely received, the relay module is closed, and the next snap-shot time point is waited; the image data is sent to a server through a transceiver module;
s5, storing the first uploaded picture as an initial image corresponding to the outdoor large-board advertisement, taking a snapshot image of the outdoor large-board advertisement within a set time, and finally identifying the outdoor large-board advertisement picture based on the neural network model.
Further, the outdoor large-board advertising picture recognition based on the neural network comprises training an outdoor large-board advertising picture recognition model and recognizing the outdoor large-board advertising picture through the trained outdoor large-board advertising picture recognition model.
Further, the specific process of training the outdoor billboard advertisement picture recognition model is as follows:
s01, converting the initial image into gray scale, marking pixels, wherein 1 represents white, 1 represents black, and three positions are extracted from the initial image to be used as the characteristics of the outdoor billboard advertising range and become convolution kernels with the size of 3x 3;
s02, graying the snap-shot image, marking the pixel characteristics by 1-1, taking any element value in the convolution kernel, multiplying the element value by any pixel value of the snap-shot image, obtaining a new 3x3 mark value when the result is the convolution value marked by the current pixel, and averaging 9 values to obtain a new pixel value, also called a window;
s03, after obtaining a window value, sliding the window to the right with the step length of 2, continuing to calculate a new characteristic pixel value, and after applying convolution kernel calculation to the snapshot image, obtaining a brand new characteristic image; then, adopting an average pooling method to sequentially slide the characteristic values of the characteristic graph to the right and taking an average value;
s04, obtaining a 2x2 characteristic diagram after multiple times of pooling; then, applying a full-connection network to the feature map, adopting a Softmax classification function, and outputting a probability value of each corresponding category, wherein the closer the probability value is to 1, the closer the pixel region is to the initial image, and otherwise, the closer the probability value is to 0, the closer the pixel region is to the initial image;
s05, performing multiple operations through a Softmax classification function, and adding probability values of all pixel regions to obtain an average, wherein the average is a similarity value of the snapshot image and the initial image;
s06, and circulating steps S01-S05 for multiple times, wherein image data captured each time are used as a training set for training an outdoor large-billboard advertisement picture recognition model through convolution, average pooling, full-connection network and full-picture probability average until the outdoor large-billboard advertisement picture recognition model converges.
Furthermore, the identification of the outdoor billboard advertising picture through the trained outdoor billboard advertising picture identification model comprises picture change identification and picture breakage identification;
the screen change recognition process includes:
1) uploading an advertisement design picture of a target outdoor billboard by a client, and filling an advertisement picture publishing time period; meanwhile, the camera module corresponding to the target outdoor billboard carries out snapshot on the advertisement picture image to obtain a target advertisement picture image;
2) uploading the obtained target advertisement picture image to a server, judging the probability value average number of the advertisement picture image through a trained outdoor billboard advertisement picture identification model, if the average value is less than 0.95, judging that the target advertisement picture image is a non-outdoor billboard feature picture, sending the judgment result to a client side, and giving an alarm; if the average value is judged to be greater than or equal to 0.95, a cosine similarity function is executed, if the cosine value is greater than or equal to 0.93, the advertisement design picture is consistent with the snap-shot target advertisement picture image, and the picture replacement risk does not exist; otherwise, the advertisement design picture is considered to be not in accordance with the captured target advertisement picture image, and the picture replacement risk exists;
3) the client receives the final picture comparison result and judges whether the current advertisement picture is changed in the advertisement publishing period according to the appointed advertisement picture publishing time;
the process of identifying the picture breakage comprises the following steps:
and (3) under the condition that the cosine value obtained in the step 2) is more than or equal to 0.93, executing a machine vision convolution neural network algorithm on the advertisement design picture and the snap-shot target advertisement picture image, calculating the average of the characteristic values of all the areas, comparing and subtracting the characteristic data of the two pictures one by one, and drawing a red line mark in the area with the comparison value less than 0.50, wherein the marked area is a picture damaged area.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
1. the scheme is based on the mode of the Internet of things and is used for remotely monitoring the picture change or damage condition of the outdoor large-board advertisement, so that the picture condition of the advertisement can be known without manual inspection, the working efficiency is improved, and the operation cost is reduced.
2. Specifically, a relay module is connected between a main control development board and an image acquisition module, and the image data of the advertisement pictures are acquired at regular time and uploaded to a server through a receiving and sending module to analyze, calculate and compare the images, so that whether the advertisement pictures are damaged or replaced is automatically judged, and the monitoring accuracy and the advertisement release trust problem between the advertisement pictures and an advertiser are greatly improved.
3. Specifically, a GPS module is installed at each outdoor large billboard advertisement position, so that a central control room receives the position of each outdoor large billboard advertisement, and if the advertisement picture of a certain billboard is monitored to be abnormal, a client can receive the advertisement picture in real time and inform the nearest staff of overhauling in the past.
4. The outdoor large billboard advertising picture is identified based on the neural network model, and the identification accuracy is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for the embodiments or the technical solutions in the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an outdoor billboard advertising picture monitoring and identifying system according to the present invention;
FIG. 2 is a schematic flow chart of a method for monitoring and identifying an outdoor billboard advertising picture according to the invention;
FIG. 3 is a schematic flow chart of training an outdoor billboard advertisement picture recognition model in the outdoor billboard advertisement picture monitoring and recognition method according to the invention;
FIG. 4 is a schematic flow chart of the method for monitoring and identifying the outdoor billboard advertising picture according to the invention, in which the outdoor billboard advertising picture is identified by the trained outdoor billboard advertising picture identification model.
Reference numerals:
1-master control development board; 2-a GPS module; 3-a relay module; 4-a camera module; 5-a transceiver module; 6-a server; 7-client side.
Detailed Description
The invention will be further illustrated with reference to specific examples:
as shown in fig. 1, an outdoor large billboard advertising picture monitoring and identifying system comprises a main control development board 1, a GPS module 2, a relay module 3, a camera module 4, a transceiver module 5, a server 6 and a client 7.
Wherein the content of the first and second substances,
the camera module 4 is connected with the main control development board 1 through the relay module 3, and transmits the picture image to the main control development board 1 after collecting the outdoor large billboard advertising picture image; the collected time is controlled by the relay module 3;
the GPS module 2 is connected with the main control development board 1 and sends the corresponding position of the outdoor large-board advertisement to the main control development board 1;
the main control development board 1 is connected with a transceiver module 5, the outdoor large billboard advertising picture image and the position data positioned by the GPS module 2 are sent to a server 6 through the transceiver module 5, and the server 6 analyzes, processes and stores the received data;
the server 6 is installed in a remote central control room;
the client 7 is connected with the server 6, and the outdoor large billboard advertising picture is remotely monitored through the server 6.
Specifically, in the present embodiment, the camera module 4 employs a 1.6mm ultra-wide angle fisheye camera module. The client 7 is a smart phone. The transceiver module 5 adopts a 5G network. The main control development board 1 processes the chip by adopting RK 3288.
As shown in fig. 2, the working principle of the present embodiment is as follows:
s1, the main control development board 1 carries out self-checking, the GPS module 2 and the transceiver module 5 are operated, the server 6 receives the state codes of the modules, and online monitoring of the modules is realized;
s2, the client 7 updates the billboard positioning information, and the user sets the snapshot time point, the snapshot interval time and the advertisement release time period of the target advertiser for each billboard picture;
s3, the master control development board 1 acquires the current world time, judges whether to enter a preset image capturing time point, and if not, the relay module 3 and the camera module 4 continue to enter a normal standby working state; if the preset image snapshot time point is judged to be entered, the main control development board 1 sends an instruction to start the relay module 3, the camera module 4 is electrified to operate, and snapshot return of image data of the advertising picture of the corresponding outdoor large board is carried out;
s4, the captured image is transmitted back to the main control development board 1, after the image data are completely received, the relay module 3 is closed, and the next capturing time point is waited; the image data is sent to the server 6 through the transceiver module 5;
s5, storing the first uploaded picture as an initial image corresponding to the outdoor large-board advertisement, taking a snapshot image of the outdoor large-board advertisement within a set time, and finally identifying the outdoor large-board advertisement picture based on the neural network model.
In the above, the identification of the outdoor large-billboard advertising picture based on the neural network is performed, which includes training an outdoor large-billboard advertising picture identification model and identifying the outdoor large-billboard advertising picture through the trained outdoor large-billboard advertising picture identification model.
As shown in fig. 3, the specific process of training the outdoor billboard advertisement frame recognition model is as follows:
s01, converting the initial image into gray scale, marking pixels, wherein 1 represents white, 1 represents black, and three positions are extracted from the initial image to be used as the characteristics of the outdoor billboard advertising range and become convolution kernels with the size of 3x 3;
s02, graying the snap-shot image, marking the pixel characteristics by 1-1, taking any element value in the convolution kernel, multiplying the element value by any pixel value of the snap-shot image, obtaining a new 3x3 mark value when the result is the convolution value marked by the current pixel, and averaging 9 values to obtain a new pixel value, also called a window;
s03, after obtaining a window value, sliding the window to the right with the step length of 2, continuing to calculate a new characteristic pixel value, and after applying convolution kernel calculation to the snapshot image, obtaining a brand new characteristic image; then, adopting an average pooling method to sequentially slide the characteristic values of the characteristic graph to the right and taking an average value;
s04, obtaining a 2x2 characteristic diagram after multiple times of pooling; then, applying a full-connection network to the feature map, adopting a Softmax classification function, and outputting a probability value of each corresponding category, wherein the closer the probability value is to 1, the closer the pixel region is to the initial image, and otherwise, the closer the probability value is to 0, the closer the pixel region is to the initial image;
s05, performing multiple operations through a Softmax classification function, and adding probability values of all pixel regions to obtain an average, wherein the average is a similarity value of the snapshot image and the initial image;
s06, and circulating steps S01-S05 for multiple times, wherein image data captured each time are used as a training set for training an outdoor large-billboard advertisement picture recognition model through convolution, average pooling, full-connection network and full-picture probability average until the outdoor large-billboard advertisement picture recognition model converges.
As shown in fig. 4, the recognition of the outdoor billboard advertising picture by the trained outdoor billboard advertising picture recognition model includes picture change recognition and picture breakage recognition;
the screen change recognition process includes:
1) the client 7 uploads an advertisement design picture of a target outdoor billboard, and fills in an advertisement picture publishing time period; meanwhile, the camera module 4 corresponding to the target outdoor billboard carries out snapshot on the advertisement picture image to obtain a target advertisement picture image;
2) uploading the obtained target advertisement picture image to a server 6, judging the probability value average of the advertisement picture image through a trained outdoor billboard advertisement picture identification model, if the average value is less than 0.95, judging that the target advertisement picture image is a non-outdoor billboard feature picture, sending the judgment result to a client 7, and giving an alarm; if the average value is judged to be greater than or equal to 0.95, a cosine similarity function is executed, if the cosine value is greater than or equal to 0.93, the advertisement design picture is consistent with the snap-shot target advertisement picture image, and the picture replacement risk does not exist; otherwise, the advertisement design picture is considered to be not in accordance with the captured target advertisement picture image, and the picture replacement risk exists;
3) the client 7 receives the final picture comparison result, and judges whether the current advertisement picture is changed in the advertisement publishing period according to the appointed advertisement picture publishing time;
the process of identifying the picture breakage comprises the following steps:
and (3) under the condition that the cosine value obtained in the step 2) is more than or equal to 0.93, executing a machine vision convolution neural network algorithm on the advertisement design picture and the snap-shot target advertisement picture image, calculating the average of the characteristic values of all the areas, comparing and subtracting the characteristic data of the two pictures one by one, and drawing a red line mark in the area with the comparison value less than 0.50, wherein the marked area is a picture damaged area.
The picture change or the damaged condition of outdoor big tablet advertisement are monitored to the mode of this embodiment based on thing networking for need not artifical patrol and also can know the picture condition of advertisement, not only improved work efficiency, still reduced the operation cost. Then, a relay module 3 is connected between the main control development board 1 and the image acquisition module, and the advertisement picture image data is acquired at regular time and uploaded to a server 6 through a transceiver module 5 to be analyzed, calculated and compared, so that whether the advertisement picture is damaged or replaced is automatically judged, and the monitoring accuracy and the advertisement release trust problem between the advertisement picture and an advertiser are greatly improved. And in addition, the GPS module 2 is arranged at each outdoor large-board advertisement position, so that the central control room receives the position of each outdoor large-board advertisement position, and if the advertisement picture of a certain advertisement large board is monitored to be abnormal, the client 7 can receive the advertisement picture in real time and inform the nearest staff of overhauling in the past. And finally, the outdoor large billboard advertising picture is identified based on the neural network model, so that the identification accuracy is ensured.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.
Claims (9)
1. An outdoor large-board advertising picture monitoring and identifying system is characterized by comprising a main control development board (1), a GPS module (2), a relay module (3), a camera module (4), a transceiving module (5), a server (6) and a client (7);
wherein the content of the first and second substances,
the camera module (4) is connected with the main control development board (1) through the relay module (3), and transmits the picture image to the main control development board (1) after collecting the outdoor large billboard advertising picture image; the collected time is controlled by the relay module (3);
the GPS module (2) is connected with the main control development board (1) and sends the position of the outdoor large board advertisement corresponding to the GPS module to the main control development board (1);
the main control development board (1) is connected with the transceiver module (5), the outdoor large billboard advertising picture image and the position data positioned by the GPS module (2) are sent to the server (6) through the transceiver module (5), and the server (6) analyzes, processes and stores the received data;
the server (6) is installed in a remote central control room;
the client (7) is connected with the server (6), and the outdoor large billboard advertising picture is remotely monitored through the server (6).
2. An outdoor large billboard advertising picture monitoring and identifying system according to claim 1, characterized in that the camera module (4) adopts a 1.6mm ultra-wide angle fisheye camera module.
3. The system for monitoring and identifying the outdoor large billboard advertising picture according to claim 1, wherein the client (7) is any one of a smart phone, a tablet computer, a notebook computer and a desktop computer.
4. An outdoor large billboard advertising picture monitoring and identification system according to claim 1, characterised in that the transceiver module (5) employs any of 4G, 5G, NB-IoT.
5. The system for monitoring and identifying the outdoor large billboard advertising picture according to claim 1, wherein the main control development board (1) adopts a RK3288 processing chip.
6. An outdoor large billboard advertising picture monitoring and identifying method is characterized by comprising the following steps:
s1, the main control development board carries out self-checking, the GPS module and the transceiver module are operated, the server receives the state codes of the modules, and online monitoring of the modules is realized;
s2, the client updates the billboard positioning information, and the user sets the snapshot time point, the snapshot interval time and the advertisement release time period of the target advertiser for each billboard picture;
s3, the master control development board acquires the current world time, judges whether the current world time enters a preset image capturing time point, and if the current world time does not enter the preset image capturing time point, the relay module and the camera module continue to enter a normal standby working state; if the preset image snapshot time point is judged to be entered, the main control development board sends an instruction to start the relay module, the camera module is electrified to operate, and snapshot return of image data of the advertising picture of the corresponding outdoor large billboard is carried out;
s4, the snap-shot image is transmitted back to the main control development board, after the image data are completely received, the relay module is closed, and the next snap-shot time point is waited; the image data is sent to a server through a transceiver module;
s5, storing the first uploaded picture as an initial image corresponding to the outdoor large-board advertisement, taking a snapshot image of the outdoor large-board advertisement within a set time, and finally identifying the outdoor large-board advertisement picture based on the neural network model.
7. The method as claimed in claim 6, wherein the performing of the neural network-based outdoor billboard advertisement picture recognition comprises training an outdoor billboard advertisement picture recognition model and recognizing the outdoor billboard advertisement picture through the trained outdoor billboard advertisement picture recognition model.
8. The method for monitoring and recognizing the outdoor large billboard advertising picture according to claim 7, wherein the specific process for training the outdoor large billboard advertising picture recognition model is as follows:
s01, converting the initial image into gray scale, marking pixels, wherein 1 represents white, 1 represents black, and three positions are extracted from the initial image to be used as the characteristics of the outdoor billboard advertising range and become convolution kernels with the size of 3x 3;
s02, graying the snap-shot image, marking the pixel characteristics by 1-1, taking any element value in the convolution kernel, multiplying the element value by any pixel value of the snap-shot image, obtaining a new 3x3 mark value when the result is the convolution value marked by the current pixel, and averaging 9 values to obtain a new pixel value, also called a window;
s03, after obtaining a window value, sliding the window to the right with the step length of 2, continuing to calculate a new characteristic pixel value, and after applying convolution kernel calculation to the snapshot image, obtaining a brand new characteristic image; then, adopting an average pooling method to sequentially slide the characteristic values of the characteristic graph to the right and taking an average value;
s04, obtaining a 2x2 characteristic diagram after multiple times of pooling; then, applying a full-connection network to the feature map, adopting a Softmax classification function, and outputting a probability value of each corresponding category, wherein the closer the probability value is to 1, the closer the pixel region is to the initial image, and otherwise, the closer the probability value is to 0, the closer the pixel region is to the initial image;
s05, performing multiple operations through a Softmax classification function, and adding probability values of all pixel regions to obtain an average, wherein the average is a similarity value of the snapshot image and the initial image;
s06, and circulating steps S01-S05 for multiple times, wherein image data captured each time are used as a training set for training an outdoor large-billboard advertisement picture recognition model through convolution, average pooling, full-connection network and full-picture probability average until the outdoor large-billboard advertisement picture recognition model converges.
9. The method as claimed in claim 7, wherein the recognition of the outdoor billboard advertising picture by the trained outdoor billboard advertising picture recognition model comprises picture change recognition and picture breakage recognition;
the screen change recognition process includes:
1) uploading an advertisement design picture of a target outdoor billboard by a client, and filling an advertisement picture publishing time period; meanwhile, the camera module corresponding to the target outdoor billboard carries out snapshot on the advertisement picture image to obtain a target advertisement picture image;
2) uploading the obtained target advertisement picture image to a server, judging the probability value average number of the advertisement picture image through a trained outdoor billboard advertisement picture identification model, if the average value is less than 0.95, judging that the target advertisement picture image is a non-outdoor billboard feature picture, sending the judgment result to a client side, and giving an alarm; if the average value is judged to be greater than or equal to 0.95, a cosine similarity function is executed, if the cosine value is greater than or equal to 0.93, the advertisement design picture is consistent with the snap-shot target advertisement picture image, and the picture replacement risk does not exist; otherwise, the advertisement design picture is considered to be not in accordance with the captured target advertisement picture image, and the picture replacement risk exists;
3) the client receives the final picture comparison result and judges whether the current advertisement picture is changed in the advertisement publishing period according to the appointed advertisement picture publishing time;
the process of identifying the picture breakage comprises the following steps:
and (3) under the condition that the cosine value obtained in the step 2) is more than or equal to 0.93, executing a machine vision convolution neural network algorithm on the advertisement design picture and the snap-shot target advertisement picture image, calculating the average of the characteristic values of all the areas, comparing and subtracting the characteristic data of the two pictures one by one, and drawing a red line mark in the area with the comparison value less than 0.50, wherein the marked area is a picture damaged area.
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