CN112327893A - AI (Artificial intelligence) technology-based unmanned aerial vehicle flight control front-end people counting system and method - Google Patents
AI (Artificial intelligence) technology-based unmanned aerial vehicle flight control front-end people counting system and method Download PDFInfo
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Abstract
The invention discloses an AI (artificial intelligence) technology-based unmanned aerial vehicle flight control front-end people counting system and method, which comprises a ground station system, an unmanned aerial vehicle-end shooting system and a flight control system, wherein the shooting system shoots and collects images in a shooting range, the flight control system counts and identifies the people in the images collected by the shooting system, and simultaneously receives an instruction sent by the ground station system to control the flight of an unmanned aerial vehicle, the ground station system receives the unmanned aerial vehicle flight data and identification statistical data transmitted by the flight control system, monitors the attitude and position information of the unmanned aerial vehicle, and simultaneously performs parameter configuration, changes the mode of the unmanned aerial vehicle and plans a flight task. The invention carries out statistics and identification on the number of people in the designated area through the unmanned aerial vehicle.
Description
Technical Field
The invention relates to the field of people counting, in particular to an AI technology-based unmanned aerial vehicle flight control front-end people counting system and method.
Background
With the rapid development of social economy, personnel density management work becomes more and more important and becomes an important safety problem which is not negligible for people. At present, due to rapid development of mode recognition and image processing technologies, a method of counting the number of passing persons in an image by arranging fixed image acquisition equipment is widely applied, but the method is basically used for counting the number of passing persons at a crossing or a node, and is practical for scenes such as superstores, stations, museums and the like with fixed entrances and exits. For disaster sites such as squares, playgrounds and the like which are not provided with fixed entrances and exits or which can not be reached by workers such as fire and the like, the number of people can not be counted quickly.
Disclosure of Invention
The invention aims to provide an AI technology-based unmanned aerial vehicle flight control front-end people counting system and method, which aim to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
the utility model provides an unmanned aerial vehicle flies accuse front end people statistical system based on AI technique, includes:
the unmanned aerial vehicle flight control system comprises a ground station system and an unmanned aerial vehicle-end shooting system and a flight control system, wherein the shooting system shoots and collects images in a shooting range, the flight control system counts and identifies the number of people in the images collected by the shooting system, and simultaneously receives an instruction sent by the ground station system to control the flight of the unmanned aerial vehicle, the ground station system receives the flight data and the identification statistical data of the unmanned aerial vehicle transmitted by the flight control system, monitors the posture and the position information of the unmanned aerial vehicle, configures parameters, changes the mode of the unmanned aerial vehicle and plans a flight task.
Furthermore, the shooting system comprises a camera and a data transmission module, the camera is carried on the unmanned aerial vehicle through a holder, the camera collects images within a shooting range, and the collected images are transmitted to the flight control system through the data transmission module.
Furthermore, the shooting system also comprises an infrared lens, and the thermal imaging is utilized to assist the camera in shooting.
Furthermore, the ground station system comprises a ground communication module, and a ground login verification module, a parameter configuration module and a data processing module which are electrically connected with the ground communication module, wherein the ground login verification module analyzes and identifies identity information input by an operator, grants control authority after successful authentication, then the operator inputs control parameters into the parameter configuration module, the parameter configuration module converts the parameter information into an electric signal and sends the electric signal to the flight control system through the ground communication module, meanwhile, the ground communication module receives data acquired by the flight control system and transmits the data to the data processing module, and the data processing module analyzes the data information and displays the data on a user interface.
Further, flight control system is including flying to control communication module, instruction analysis module, flight control module and degree of depth study people number statistics module, it carries out data interchange with ground station system and shooting system to fly to control communication module, realize with ground station system and shooting system's communication between, the instruction is analyzed the module and is connected the instruction that sends to ground station system with flying to control communication module electricity and is analyzed and convey in the flight control module rather than being connected, the realization is controlled unmanned aerial vehicle's flight, degree of depth study people number statistics module is connected the image of receiving shooting system conveying with instruction analysis module electricity and is discerned, count the number of people wherein to with data transmission to ground station system through flying to control communication module.
Furthermore, flight control system still includes radar module and keeps away the barrier module, keep away the barrier module according to the planning route that ground station system sent and the flight situation that radar module sensed, properly adjust unmanned aerial vehicle's flight route, avoid unmanned aerial vehicle and other object collisions.
Furthermore, the people counting module comprises an instruction safety inspection module, a deep learning classification module, a category quantity counting module and a data distribution and subscription module, the instruction safety inspection module inspects data and parameter loading conditions sent from the ground station, the deep learning classification module performs operation classification on the data in the image shot by the camera, selects people and object information, sends the information to the category position extraction module electrically connected with the deep learning classification module to judge and identify the image, and sends the information to the ground station system through the data distribution and subscription module electrically connected with the deep learning classification module.
The method for counting the number of people by the system comprises the following steps:
s1: inputting control parameters in a parameter configuration module of a ground station system, and planning a flight route of the unmanned aerial vehicle;
s2: the flight control system receives an instruction of the ground station and then triggers a corresponding function of the unmanned aerial vehicle, and the unmanned aerial vehicle flies according to a designated route;
s3: in the flying process of the unmanned aerial vehicle, the radar module scans and detects the flying environment of the unmanned aerial vehicle, and the obstacle avoidance system adjusts the planned route timely according to the environment to avoid collision with other objects;
s4: in the flight process of the unmanned aerial vehicle, the camera continuously collects images in a shooting range, and the collected images are transmitted to a flight control system;
s5: the deep learning people counting module in the flight control system receives the image data and then processes the image data as follows:
the first step is as follows: the command and data safety inspection module inspects data sent from the ground station and module parameter loading conditions, and if the data are normal, the command is sent to the deep learning classification module;
the second step is that: the deep learning classification module extracts a key frame from each frame of data in a video shot by a camera, performs frame skipping setting according to the performance of a hardware system, performs next processing on a selected frame as a processed picture, performs operation classification on the selected frame according to a trained model, identifies possible objects in the image, and sends coordinates and sizes of the objects in the image to the category position extraction module;
the third step: the category number counting module counts the number of the identified portraits by judging and identifying the objects counted in the module and sends the counted portraits to the data distribution and subscription module;
the fourth step: the data distribution and subscription module sends the received data to the ground station and the flight control module;
s6: and after receiving the statistical data of the flight control system, the data processing module of the ground station system analyzes the statistical data and displays the statistical data on a user interface.
The second and third steps above use the open source YOLO V3 algorithm.
Compared with the prior art, the invention has the beneficial effects that: the unmanned aerial vehicle is controlled by the flight control system to fly so that the camera on the unmanned aerial vehicle collects images in the designated area, the collected data are transmitted to the deep learning people counting module to be analyzed and processed to count the number of people in the designated area, the counting result is fed back to the ground station system to be displayed, and an operator sends an instruction to the flight control system by the ground station according to the feedback result to control the unmanned aerial vehicle, so that the people counting and identifying in the designated area are realized.
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FIG. 1 is a schematic view of the system of the present invention,
FIG. 2 is a schematic diagram of the people counting module according to the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 and 2, the present invention provides a technical solution: the utility model provides an unmanned aerial vehicle flies accuse front end people statistical system based on AI technique, includes:
the system comprises a ground station system, a shooting system and a flight control system at an unmanned aerial vehicle end;
the shooting system shoots and collects images in a shooting range, the flight control system counts and identifies the number of people in the images collected by the shooting system, and simultaneously receives an instruction sent by the ground station system to control the flight of the unmanned aerial vehicle, the ground station system receives the flight data and the identification statistical data of the unmanned aerial vehicle transmitted by the flight control system, monitors the attitude and the position information of the unmanned aerial vehicle, and simultaneously configures parameters, changes the mode of the unmanned aerial vehicle and plans a flight task.
The shooting system comprises an unmanned aerial vehicle, a camera and a data transmission module, the camera is carried on the unmanned aerial vehicle through a holder, the camera collects images within a shooting range, and the collected images are transmitted to the flight control system through the data transmission module.
The shooting system further comprises an infrared lens, when the shooting sight line is poor, and the camera cannot clearly shoot images of the target area, the infrared lens is started to carry out thermal imaging, and the camera is assisted to complete shooting.
The ground station system comprises a ground communication module, a ground login verification module, a parameter configuration module and a data processing module, wherein the ground login verification module is electrically connected with the ground communication module, the ground login verification module analyzes and identifies identity information input by an operator, control authority is granted after successful authentication, then the operator inputs control parameters into the parameter configuration module, the parameter configuration module converts the parameter information into an electric signal and sends the electric signal to the flight control system through the ground communication module, meanwhile, the ground communication module receives data collected by the flight control system and transmits the data to the data processing module, and the data processing module analyzes the data information and displays the data on a user interface.
Flight control system is including flying to control communication module, instruction analysis module, flight control module and degree of depth study people number statistics module, it carries out data exchange with ground station system and shooting system to fly to control communication module, realize with ground station system and shooting system's communication between the system, instruction analysis module is connected with flying to control communication module electricity and is analyzed and convey the flight control module rather than being connected the instruction that ground station system sent and come, the realization is controlled unmanned aerial vehicle's flight, degree of depth study people number statistics module is connected with the instruction analysis module electricity and is received the image that shooting system conveyed and come and discern, statistics number wherein to transmit data to ground station system through flying to control communication module.
Flight control system still includes radar module and keeps away the barrier module, keep away the barrier module and send the planning route of coming and the flight situation that radar module sensed according to ground station system, carry out appropriate adjustment to unmanned aerial vehicle's flight route, avoid unmanned aerial vehicle and other object collisions, radar module includes six evenly distributed radars on the unmanned aerial vehicle outer wall.
The people counting module comprises an instruction safety inspection module, a deep learning classification module, a category quantity counting module and a data distribution and subscription module, the instruction safety inspection module inspects data and parameter loading conditions sent from the ground station, the deep learning classification module performs operation classification on the data in the image shot by the camera, selects people and object information, sends the information to the category position extraction module electrically connected with the deep learning classification module to judge and identify the image, and then sends the information to the ground station system through the data distribution and subscription module electrically connected with the deep learning classification module.
The method for counting the number of people by the system comprises the following steps:
s1: inputting control parameters in a parameter configuration module of a ground station system, and planning a flight route of the unmanned aerial vehicle;
s2: the flight control system receives an instruction of the ground station and then triggers a corresponding function of the unmanned aerial vehicle, and the unmanned aerial vehicle flies according to a designated route;
s3: in the flying process of the unmanned aerial vehicle, the radar module scans and detects the flying environment of the unmanned aerial vehicle, and the obstacle avoidance system adjusts the planned route timely according to the environment to avoid collision with other objects;
s4: in the flight process of the unmanned aerial vehicle, the camera continuously collects images in a shooting range, and the collected images are transmitted to a flight control system;
s5: the deep learning people counting module in the flight control system receives the image data and then processes the image data as follows:
the first step is as follows: the command and data safety inspection module inspects data sent from the ground station and module parameter loading conditions, and if the data are normal, the command is sent to the deep learning classification module;
the second step is that: the deep learning classification module extracts a key frame from each frame of data in a video shot by a camera, performs frame skipping setting according to the performance of a hardware system, performs next processing on a selected frame as a processed picture, performs operation classification on the selected frame according to a trained model, identifies possible objects in the image, and sends coordinates and sizes of the objects in the image to the category position extraction module;
the third step: the category number counting module counts the number of the identified portraits by judging and identifying the objects counted in the module and sends the counted portraits to the data distribution and subscription module;
the fourth step: the data distribution and subscription module sends the received data to the ground station and the flight control module;
s6: and after receiving the statistical data of the flight control system, the data processing module of the ground station system analyzes the statistical data and displays the statistical data on a user interface.
The second and third steps above use the open source YOLO V3 algorithm.
The above step S3 includes the following steps:
step 2, analyzing a topographic map which embodies the environmental characteristics near the unmanned aerial vehicle by the obstacle avoidance module according to the radar signal and the image signal;
step 3, the obstacle avoidance module obtains the shortest route which returns to the planned route after avoiding all obstacles by combining the planned route given by the ground station system according to the environment;
step 4, the obstacle avoidance module feeds back the reestablished route to the ground station system and the flight control module;
and 5, receiving the sent new route by the ground station system, and controlling the unmanned aerial vehicle to fly according to the new route by the flight control module if the operator has no objection.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The utility model provides an unmanned aerial vehicle flies accuse front end people statistical system based on AI technique, includes the shooting system and the flight control system of ground station system and unmanned aerial vehicle end, its characterized in that: the shooting system shoots and collects images in a shooting range, the flight control system counts and identifies the number of people in the images collected by the shooting system, and simultaneously receives an instruction sent by the ground station system to control the flight of the unmanned aerial vehicle, the ground station system receives the flight data and the identification statistical data of the unmanned aerial vehicle transmitted by the flight control system, monitors the attitude and the position information of the unmanned aerial vehicle, and simultaneously configures parameters, changes the mode of the unmanned aerial vehicle and plans a flight task.
2. The AI-technology-based unmanned aerial vehicle flight control front-end people counting system of claim 1, wherein the camera system comprises a camera and a data transmission module, the camera is mounted on the unmanned aerial vehicle through a cradle head, the camera collects images within a shooting range, and the collected images are transmitted to the flight control system through the data transmission module.
3. The AI-technology-based unmanned aerial vehicle flight control front-end people counting system of claim 1, wherein the camera system further comprises an infrared lens, and the camera is assisted in taking pictures by thermal imaging.
4. The AI-technology-based unmanned aerial vehicle flight control front-end people counting system of claim 1, wherein the ground station system comprises a ground communication module, and a ground login verification module, a parameter configuration module and a data processing module electrically connected thereto, wherein the ground login verification module analyzes and identifies identity information input by an operator, grants control authority after successful authentication, the operator inputs control parameters into the parameter configuration module, the parameter configuration module converts the parameter information into an electrical signal and sends the electrical signal to the flight control system through the ground communication module, the ground communication module receives data collected by the flight control system and transmits the data to the data processing module, and the data processing module analyzes the data information and presents the data on a user interface.
5. The AI-technology-based unmanned aerial vehicle flight control front-end people counting system of claim 1, wherein the flight control system comprises a flight control communication module, an instruction analysis module, a flight control module and a deep learning people counting module, the flight control communication module exchanges data with the ground station system and the shooting system to realize communication with the ground station system and the shooting system, the instruction analysis module is electrically connected with the flight control communication module to analyze an instruction sent by the ground station system and transmit the instruction to the flight control module connected with the instruction analysis module to realize control of the flight of the unmanned aerial vehicle, the deep learning people counting module is electrically connected with the instruction analysis module to receive an image transmitted by the shooting system for recognition, count the number of people therein, and transmit the data to the ground station system through the flight control communication module.
6. The AI-technology-based unmanned aerial vehicle flight control front-end people counting system of claim 5, wherein the flight control system further comprises a radar module and an obstacle avoidance module, the radar module comprises a plurality of radars which are uniformly distributed on the surface of the unmanned aerial vehicle for scanning, ranging and detecting around the unmanned aerial vehicle, the obstacle avoidance module is electrically connected with the radar module, and the obstacle avoidance module makes adjustments according to the surrounding environment where the unmanned aerial vehicle is located to bypass obstacles with the shortest route.
7. The AI-technology-based unmanned aerial vehicle flight control front-end people counting system of claim 5, wherein the people counting module comprises a command security check module, a deep learning classification module, a category quantity counting module and a data distribution and subscription module, the command security check module checks the loading condition of data and parameters sent from a ground station, the deep learning classification module performs operation classification on the data in the image shot by the camera, selects people and object information, sends the information to the category position extraction module electrically connected with the deep learning classification module to judge and identify the image, and sends the information to the ground station system through the data distribution and subscription module electrically connected with the deep learning classification module.
8. An AI technology-based unmanned aerial vehicle flight control front-end people counting method is characterized by comprising the following steps:
s1: inputting control parameters in a parameter configuration module of a ground station system, and planning a flight route of the unmanned aerial vehicle;
s2: the flight control system receives an instruction of the ground station and then triggers a corresponding function of the unmanned aerial vehicle, and the unmanned aerial vehicle flies according to a designated route;
s3: in the flying process of the unmanned aerial vehicle, the radar module scans and detects the flying environment of the unmanned aerial vehicle, and the obstacle avoidance system adjusts the planned route timely according to the environment to avoid collision with other objects;
s4: in the flight process of the unmanned aerial vehicle, the camera continuously collects images in a shooting range, and the collected images are transmitted to a flight control system;
s5: after receiving the image data, a deep learning people counting module in the flight control system identifies the figures in the image data, counts the number of the figures, and feeds the result back to a ground station system;
s6: and after receiving the statistical data of the flight control system, the data processing module of the ground station system analyzes the statistical data and displays the statistical data on a user interface.
9. The AI technology-based unmanned aerial vehicle flight control front-end people counting method of claim 8, wherein the processing of the image data by the deep learning people counting module in step S5 comprises the following steps:
the first step is as follows: the command and data safety inspection module inspects data sent from the ground station and module parameter loading conditions, and if the data are normal, the command is sent to the deep learning classification module;
the second step is that: the deep learning classification module extracts a key frame from each frame of data in a video shot by a camera, performs frame skipping setting according to the performance of a hardware system, performs next processing on a selected frame as a processed picture, performs operation classification on the selected frame according to a trained model, identifies possible objects in the image, and sends coordinates and sizes of the objects in the image to the category position extraction module;
the third step: the category number counting module counts the number of the identified portraits by judging and identifying the objects counted in the module and sends the counted portraits to the data distribution and subscription module;
the fourth step: and the data distribution and subscription module sends the received data to the ground station and the flight control module.
10. The AI-technology-based unmanned aerial vehicle flight control front-end people counting method of claim 9, wherein the second and third steps employ open-source YOLO V3 algorithm.
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