CN113807279A - System and method for detecting on-duty state of controller based on machine vision - Google Patents
System and method for detecting on-duty state of controller based on machine vision Download PDFInfo
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Abstract
The invention discloses a system and a method for detecting the on-duty state of a controller based on machine vision, wherein the system comprises the following steps: the data acquisition device is used for receiving the monitoring video data of the control seats in real time; the position analysis module is used for carrying out area division on the control seat monitoring video data according to the spatial position of the control seat by a spatial positioning method to obtain the position coordinates of the control seat in the monitoring video data; the target detection module is used for detecting the human body target of the control seat monitoring video data through a convolutional neural network target detection algorithm to obtain the central point coordinate of the human body target; and the off-duty analysis module is used for calculating the distance between the coordinate of the central point of the human body target and the position coordinate of the control seat in the monitoring video, and judging that the controller is off duty if the distance is greater than or equal to a preset value, so that the safety of aviation control can be improved.
Description
Technical Field
The invention relates to the field of aviation control, in particular to a system and a method for detecting the on-duty state of a controller based on machine vision.
Background
With the continuous and rapid increase of airspace flight flow, under the impact of two waves of safety and development, the air traffic control safety service work faces a great challenge. The air traffic control personnel have great responsibility and are responsible for commanding and monitoring the takeoff, the flight and the landing of the aircrafts in the air and on the ground and directly closing the safety of passengers on the aircrafts. 7/8 th of 2014, the eastern B737-800/B-5731 aircraft executes MU2528 flight tasks, and when contacting the tower in the approach stage of Wuhan, because the controller sleeps at the post, calls for many times without response, fails to establish contact with the tower, and then flies back under the command of the approach controller. An unsafe event is formed together AS a cause of human responsibility according to article 1.19 of sample other unsafe events for civil aviation (AC-396-AS-2010-05). In order to prevent unsafe events caused by off duty and sleeping duty of a controller in the duty process, the conventional common method is to adopt a leader duty mode to carry out irregular supervision on duty site. The mode not only wastes manpower, but also has low efficiency, and can not completely avoid the occurrence of accidents. The prior art aims at the off-duty and off-duty detection method of the controller, whether the controller is in the off-duty state or the off-duty state is mostly judged from a single aspect, the error judgment rate of the off-duty or off-duty condition of the controller is extremely high, and the off-duty behavior and off-duty behavior detection of the controller on the control duty site cannot be met.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a system and a method for detecting the on-duty state of a controller based on machine vision, which improve the safety of aviation control.
First aspect
The invention provides a controller on-duty state detection system based on machine vision, which comprises:
the data acquisition device is used for receiving the monitoring video data of the control seats in real time;
the position analysis module is used for carrying out area division on the control seat monitoring video data according to the spatial position of the control seat by a spatial positioning method to obtain the position coordinates of the control seat in the monitoring video data;
the target detection module is used for carrying out human body target detection on the control seat monitoring video data through a convolutional neural network target detection algorithm to obtain a central point coordinate of a human body target;
and the off-duty analysis module is used for calculating the distance between the center point coordinate of the human body target and the position coordinate of the control seat in the monitoring video, and judging that the controller is off duty if the distance is greater than or equal to a preset value.
Preferably, the system further comprises a first sleep post analysis module, which is used for detecting human body posture data from the control seat monitoring video data through a human body posture estimation algorithm; and if the posture keeping time of the controller lying on the seat is detected to be greater than or equal to the first preset time, judging that the controller sleeps.
Preferably, the system further comprises a second sleep post analysis module, which is used for detecting the eye closure state of the controller from the monitoring video data of the controlled seat through an eye closure state detection algorithm; and if the eye closing state keeping time of the controller is detected to be greater than or equal to the second preset time, judging that the controller sleeps.
Preferably, the data acquisition device is further used for receiving flight plan data and attendance data in real time;
the system also comprises an on-duty information analysis module which is used for carrying out detection calculation analysis on the control seat monitoring video data, the flight plan data and the on-duty attendance data to obtain the detection data of the on-duty condition of the controller.
Preferably, the off-Shift monitoring system further comprises an alarm module, and when the off-Shift analysis module judges that the controller is off-Shift, the alarm module gives an alarm to remind.
Preferably, the system further comprises a data analysis and recording module for analyzing and recording the alarm reminding information.
Second aspect of the invention
The invention provides a controller on-duty state detection method based on machine vision, which comprises the following steps:
receiving monitoring video data of a control seat in real time;
the method comprises the steps that area division is carried out on monitoring video data of a controlled seat according to the spatial position of the controlled seat through a spatial positioning method, and the position coordinates of the controlled seat in the monitoring video data are obtained;
detecting a human body target on the monitoring video data of the controlled seat through a convolutional neural network target detection algorithm to obtain a central point coordinate of the human body target;
and calculating the distance between the coordinate of the central point of the human body target and the position coordinate of the control seat in the monitoring video, and judging that the controller is off duty if the distance is greater than or equal to a preset value.
Preferably, the method further comprises the following steps:
detecting human body posture data of the controlled seat monitoring video data through a human body posture estimation algorithm; and if the posture keeping time of the controller lying on the seat is detected to be greater than or equal to the first preset time, judging that the controller sleeps.
Preferably, the method further comprises the following steps:
detecting the eye closure state of the controller on the monitoring video data of the controlled seat through an eye closure state detection algorithm; and if the eye closing state keeping time of the controller is detected to be greater than or equal to the second preset time, judging that the controller sleeps.
Preferably, the method further comprises the following steps:
and when the controller is judged to be off duty, alarming and reminding are carried out.
The invention has the beneficial effects that:
the method comprises the steps that firstly, a computer vision technology is adopted, monitoring video data, fan opening and closing data and duty attendance data are fused, and the method is suitable for detecting the off-duty behavior and the off-duty behavior of a controller in different application scenes of control region adjustment, approach and tower;
secondly, adopting improved YOLO v5 to realize human body target identification, and judging whether the controller is off duty or not in the space position in the monitoring video by combining with the control seat; adopting an OpenPose posture estimation algorithm to realize human body target behavior analysis, detecting and calculating the state of the controller with the behavior of lying prone and keeping the state for a preset number of minutes, and judging the controller sleeping on duty; and detecting the eye closing state of the controller by adopting a PERCLOS algorithm of OpenCv, keeping the eye closing state to reach a preset value, and judging that the controller sleeps. Different algorithms are adopted for different detection purposes, and the off-Shift and sleep-Shift misjudgment rates are reduced;
and thirdly, unsafe events caused by the off-duty and off-duty behaviors of the controller in the control field can be prevented, an innovative and modern safety management means can be provided for the basic level management of the air traffic control system, and the method has important practical significance for guaranteeing flight safety and improving the quality of control service.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic structural diagram according to a first embodiment of the present invention;
fig. 2 is a schematic flow chart of a second embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
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, not all, embodiments of the present invention. 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.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Example one
As shown in fig. 1, an embodiment of the present invention provides a system for detecting a controller on-duty status based on machine vision, including:
the data acquisition device adopts a camera with a fixed angle to acquire the control seats in a fixed area and receives the monitoring video data of the control seats in real time; the system is also used for receiving flight plan data, attendance data and GPS clock data in real time;
the on-duty information analysis module is used for carrying out detection calculation analysis on the control seat monitoring video data, the flight plan data and the on-duty attendance data to obtain the detection data of the on-duty condition of the controller; specifically, flight plan data are analyzed to obtain control sector opening and closing state data; and analyzing the attendance data to obtain the information of the operators on duty and the time on duty of the control seat. Moreover, the system time is synchronized into the same time reference of the monitoring video data and the attendance data through the GPS clock data, so that the accuracy of the calculated result time is ensured;
the position analysis module is used for carrying out area division on the control seat monitoring video data according to the spatial position of the control seat by a spatial positioning method to obtain the position coordinates of the control seat in the monitoring video data;
the target detection module is used for carrying out noise reduction and filtering processing on the control seat monitoring video data and extracting a characteristic value; after the characteristics are extracted, human body target detection is carried out on the characteristic values through a YOLO v5 convolutional neural network target detection algorithm to obtain the central point coordinates of the human body target;
and the off-post analysis module is used for calculating the distance between the center point coordinate of the human body target and the position coordinate of the control seat in the monitoring video, specifically, calculating the distance between the center point coordinate of the human body target and the center point of the position coordinate of the control seat in the monitoring video. If the distance is larger than or equal to a preset value, judging that the controller is off duty; otherwise, the controller is judged to be on duty.
The embodiment of the invention also comprises a first sleep post analysis module, which is used for detecting the human body posture data of the monitoring video data of the control seat through an OpenPose human body posture estimation algorithm under the condition that the controller is on post; if the situation that the posture keeping time of the controller lying on the seat is detected to be greater than or equal to the first preset time, judging that the controller sleeps; otherwise, judging that the controller does not sleep on duty;
the second sleep post analysis module is used for detecting the eye closure state of the controller on the monitoring video data of the controlled seat through an eye closure state detection algorithm; if the fact that the eye closing state keeping time of the controller is larger than or equal to second preset time is detected, judging that the controller sleeps; otherwise, judging that the controller does not sleep.
The embodiment of the invention also comprises an alarm module which automatically alarms and reminds when the controller is off duty or sleeps. The alarm prompt can be in the form of an acousto-optic signal, and can also be sent to a preset terminal in the form of a short message. In the embodiment of the invention, automatic alarm is divided into three conditions, firstly, whether a post attendant is on duty is judged according to the monitoring video for detecting a human body target, calculating the opening and closing state of a sector and attendance checking time on duty, and if the fact that a controller is not on duty is detected, the system automatically carries out off-duty alarm reminding; secondly, when detecting that the person on duty at the controlled seat lies prone and sleeps for a preset number of minutes, the system automatically gives an alarm for sleeping on duty; and thirdly, when the eyes of the person on duty at the control seat are detected to be closed and kept for a preset number of minutes, the system automatically carries out sleep post warning prompt.
The system also comprises a data analysis and recording module which is used for detecting data analysis and recording alarm reminding information based on the on-duty condition of the controller, wherein the alarm reminding information comprises controller information, off-duty or off-duty time information and the like.
The embodiment of the invention also comprises a configuration module which is used for configuring system parameters, including defining the seat space position aiming at the monitoring video with a fixed angle and setting the time interval of the same behavior posture of the controller on the post.
The embodiment of the invention leads in the monitoring video data of the control seat, the flight plan data, the attendance data on duty and the parameter configuration data to be fused, detected and calculated, and judges whether the personnel on duty of the control seat have the behaviors of going off duty and sleeping on duty. The system comprises a data acquisition device for receiving seat monitoring video data, flight plan data, attendance data on duty and GPS clock data, a system calculation server for carrying out detection, calculation and analysis by fusing the received data and configured parameters to obtain the on-duty condition of the seat personnel under control, and if the system judges that the seat personnel under control are off duty and sleeping on duty, the system automatically gives an alarm and analyzes and records the alarm information.
Example two
The embodiment of the invention provides a method for detecting the on-duty state of a controller based on machine vision, which comprises the following steps as shown in figure 2:
the method comprises the following steps: respectively receiving control seat monitoring video data, flight plan data, attendance data and GPS clock data from a seat video data interface, a flight plan data interface, an attendance data interface and a GPS data interface in real time;
step two: analyzing, storing and preprocessing the monitoring video data, the flight plan data, the attendance data and the GPS clock data of the controlled seat;
step three: analyzing the flight plan data to obtain control sector opening and closing state data; and analyzing the attendance data to obtain the information of the operators on duty and the time on duty of the control seat. Moreover, through GPS clock data, the time of the system data calculation server is synchronized into the same time reference of the monitoring video data and the attendance data, so that the accuracy of the calculation result time is ensured;
step four: when a controller is on duty, the monitoring video data of the control seats are subjected to area division according to the spatial positions of the control seats by a spatial positioning method to obtain the position coordinates of the control seats in the monitoring video data; carrying out noise reduction and filtering processing on the control seat monitoring video data, and extracting a characteristic value; after the characteristics are extracted, human body target detection is carried out on the characteristic values through a YOLO v5 convolutional neural network target detection algorithm to obtain the central point coordinates of the human body target; and calculating the distance between the center point coordinate of the human body target and the position coordinate of the control seat in the monitoring video, specifically, calculating the distance between the center point coordinate of the human body target and the center point of the position coordinate of the control seat in the monitoring video. If the distance is larger than or equal to a preset value, judging that the controller is off duty; warning and reminding off Shift; otherwise, judging that the controller is on duty; the off-duty alarm reminding can be in the form of an acousto-optic signal, and can also be sent to a preset terminal in the form of a short message; and recording alarm reminding information, wherein the alarm reminding information comprises controller information, off-Shift or sleep-Shift time information and the like.
Step five: under the condition that a controller is on duty, detecting human body posture data of the monitoring video data of the control seat through an OpenPose human body posture estimation algorithm; if the situation that the posture of the controller is prone on the seat is detected to be kept for more than or equal to first preset time, judging that the controller sleeps, and carrying out post sleeping warning reminding; otherwise, judging that the controller does not sleep on duty; the sleep post warning prompt can be in the form of an acousto-optic signal, and can also be sent to a preset terminal in the form of a short message; and recording alarm reminding information, wherein the alarm reminding information comprises controller information, off-Shift or sleep-Shift time information and the like.
Step six: under the condition that the controller does not sleep in the fifth step, detecting the eye closing state of the controller on the monitoring video data of the controlled seat through an eye closing state detection algorithm; if the fact that the keeping time of the closed state of the eyes of the controller is larger than or equal to the second preset time is detected, judging that the controller sleeps, and carrying out sleep alarm reminding; otherwise, judging that the controller does not sleep. The sleep post warning prompt can be in the form of an acousto-optic signal, and can also be sent to a preset terminal in the form of a short message; and recording alarm reminding information, wherein the alarm reminding information comprises controller information, off-Shift or sleep-Shift time information and the like.
And circularly detecting the on-duty state of the controller according to the steps.
The system and the method for detecting the on-duty state of the controller based on the machine vision adopt an improved YOLO v5 target detection algorithm to realize human body target detection, compare the position information of the human body target and the control seat in a monitoring video, and if the distance between the center point of the human body target and the center point of the control seat in the monitoring video at the spatial position is calculated to reach a preset value, the off-duty state of the controller of the control seat can be judged; the method comprises the steps of adopting an OpenPose posture estimation algorithm to realize human behavior analysis, detecting and calculating the state of the controller with the behavior of lying prone, keeping the state for a preset number of minutes, and judging the controller sleeping on duty; and detecting that the eyes of the controller are in a closed state by adopting a PERCLOS algorithm of OpenCv and keeping the eyes to a preset value, and judging that the controller sleeps. The embodiment of the invention integrates the opening and closing state of the control sector, the attendance checking time of the controller on duty and the parameter configuration component, judges whether the detection controller has the off-duty behavior and the off-duty behavior during the on-duty period, and automatically sends out the alarm prompt by the system if the off-duty behavior and the off-duty behavior exist, thereby improving the precautionary consciousness of the on-duty personnel and the manager and preventing accidents from happening. And the embodiment of the invention adopts different algorithms aiming at different detection purposes, thereby reducing the misjudgment rate of off Shift and sleeping Shift. The embodiment of the invention can prevent unsafe events caused by off-duty and off-duty behaviors of controllers on a control site, can provide an innovative and modern safety management means for basic management of the air traffic control system, and has important practical significance for guaranteeing flight safety and improving control service quality.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A machine vision based system for detecting the on-duty status of a controller, comprising:
the data acquisition device is used for receiving the monitoring video data of the control seats in real time;
the position analysis module is used for carrying out area division on the control seat monitoring video data according to the spatial position of the control seat by a spatial positioning method to obtain the position coordinates of the control seat in the monitoring video data;
the target detection module is used for carrying out human body target detection on the control seat monitoring video data through a convolutional neural network target detection algorithm to obtain a central point coordinate of a human body target;
and the off-duty analysis module is used for calculating the distance between the center point coordinate of the human body target and the position coordinate of the control seat in the monitoring video, and judging that the controller is off duty if the distance is greater than or equal to a preset value.
2. The system for detecting the on-duty state of the controller based on the machine vision as claimed in claim 1, further comprising a first sleep analysis module for detecting human body posture data of the monitoring video data of the control seat through a human body posture estimation algorithm; and if the posture keeping time of the controller lying on the seat is detected to be greater than or equal to the first preset time, judging that the controller sleeps.
3. The system of claim 1, further comprising a second sleep analysis module for detecting eye closure status of the controller from the control seat monitoring video data by eye closure status detection algorithm; and if the eye closing state keeping time of the controller is detected to be greater than or equal to the second preset time, judging that the controller sleeps.
4. The machine vision-based controller on-duty state detection system as claimed in claim 1, wherein said data acquisition device is further configured to receive flight plan data and attendance data in real time;
the system also comprises an on-duty information analysis module which is used for carrying out detection calculation analysis on the control seat monitoring video data, the flight plan data and the on-duty attendance data to obtain the detection data of the on-duty condition of the controller.
5. The system of claim 1, further comprising an alarm module for alarming when the off-duty analysis module determines that the controller is off duty.
6. The machine vision-based system for detecting the on-duty state of the controller, as claimed in claim 5, further comprising a data analyzing and recording module for analyzing and recording the warning reminding information.
7. A controller on-duty state detection method based on machine vision is characterized by comprising the following steps:
receiving monitoring video data of a control seat in real time;
the method comprises the steps that area division is carried out on monitoring video data of a controlled seat according to the spatial position of the controlled seat through a spatial positioning method, and the position coordinates of the controlled seat in the monitoring video data are obtained;
detecting a human body target on the monitoring video data of the controlled seat through a convolutional neural network target detection algorithm to obtain a central point coordinate of the human body target;
and calculating the distance between the coordinate of the central point of the human body target and the position coordinate of the control seat in the monitoring video, and judging that the controller is off duty if the distance is greater than or equal to a preset value.
8. The machine vision-based controller on-duty state detection method of claim 7, further comprising the steps of:
detecting human body posture data of the controlled seat monitoring video data through a human body posture estimation algorithm; and if the posture keeping time of the controller lying on the seat is detected to be greater than or equal to the first preset time, judging that the controller sleeps.
9. The machine vision-based controller on-duty state detection method of claim 7, further comprising the steps of:
detecting the eye closure state of the controller on the monitoring video data of the controlled seat through an eye closure state detection algorithm; and if the eye closing state keeping time of the controller is detected to be greater than or equal to the second preset time, judging that the controller sleeps.
10. The machine vision-based controller on-duty state detection method of claim 7, further comprising the steps of:
and when the controller is judged to be off duty, alarming and reminding are carried out.
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Cited By (1)
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