KR20230017444A - Monitoring method of compliance with quarantine rules using drones - Google Patents

Monitoring method of compliance with quarantine rules using drones Download PDF

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KR20230017444A
KR20230017444A KR1020210098915A KR20210098915A KR20230017444A KR 20230017444 A KR20230017444 A KR 20230017444A KR 1020210098915 A KR1020210098915 A KR 1020210098915A KR 20210098915 A KR20210098915 A KR 20210098915A KR 20230017444 A KR20230017444 A KR 20230017444A
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신대균
연제민
노상우
박기정
김준성
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연제민
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박기정
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Abstract

The present invention relates to a method for monitoring compliance with quarantine rules using drones, comprising the steps of: photographing a corresponding region in real time using drones; live streaming a photographed video using an RTMP function mounted on a drone and using a streaming address as an input value in a premade code; determining population density, distances between people and whether masks are worn using the code in an object detection model; determining a high risk state when the code detects 60 or more people in the photographed video, four or more people within 1 m from one another, and one or more people not wearing a mask or wearing a mask poorly, and determining a safe state in other cases; and an Arduino module attached to the drone generating warning sound and propagating the same to people in the corresponding region in the case of a high risk. Therefore, provided is a method for monitoring compliance with quarantine rules using drones, wherein compliance with quarantine rules can be promoted.

Description

드론을 이용한 방역수칙준수 감시방법{Monitoring method of compliance with quarantine rules using drones}{Monitoring method of compliance with quarantine rules using drones}

본 발명은 드론을 이용한 방역수칙준수 감시방법에 관한 것으로, 더욱 상세하게는 드론을 이용하여 방역지침을 준수하도록 감시 및 경고하는 방법에 관한 것이다. The present invention relates to a method for monitoring compliance with quarantine rules using drones, and more particularly, to a method for monitoring and warning to comply with quarantine guidelines using drones.

일반적으로 야외 지역에서 방역수칙이 잘 지켜지고 있는지 확인하기 위해 안전요원이 순찰을 돌거나 CCTV를 사용하고 있으며, 사람들에게 경고를 해야 할 때에는 해당 지역에 이미 설치된 스피커로 안내 음성을 내 보내고 있다.In general, security guards patrol or use CCTVs to check whether quarantine rules are being followed in outdoor areas, and when it is necessary to warn people, voice guidance is sent out through speakers already installed in the area.

이와 같이, CCTV와 스피커를 야외 공원이나 행사장에 설치하는 것은 공간적 제약, 비용적 문제를 야기하고, 안전요원을 배치하려면 교육과 인건비를 제공해야 하며, 신뢰도 검증도 필요하므로 비효율적이다.In this way, installing CCTVs and speakers in outdoor parks or event venues is inefficient because it causes space limitations and cost problems, and training and labor costs must be provided to deploy safety personnel, and reliability verification is also required.

상기와 같은 문제점을 해결하기 위한 본 발명의 목적은 드론을 이용하여 사람 밀집을 막고, 사람 간 안전거리 확보와 마스크 미착용자 및 불량 착용자를 식별하여 방역수칙준수를 실천하도록 경고하는 드론을 이용한 방역수칙준수 감시방법을 제공하는 데 있다.The purpose of the present invention to solve the above problems is to use drones to prevent crowding of people, to secure a safe distance between people, to identify people who do not wear masks and bad wearers, and to use drones to warn them to comply with quarantine rules. It is to provide a method for monitoring compliance.

상기와 같은 목적을 달성하기 위한 본 발명에 따른 드론을 이용한 방역수칙준수 감시방법은 해당 지역을 드론으로 실시간 영상을 촬영하는 단계; 촬영 영상을 드론에 탑재된 RTMP 기능으로 라이브 스트리밍하고, 스트리밍 주소를 미리 만들어둔 코드에 입력값으로 사용하는 단계; 상기 코드를 객체탐지모델을 활용하여 인구밀집도, 사람 간 거리, 마스크착용 여부를 판별하는 단계; 상기 코드에서 영상에 촬영된 사람 수 60명 이상, 서로 간의 거리가 1m 이내인 사람 수 4명 이상, 마스크 미착용자 및 불량 착용자 1명 이상을 포착할 경우High Risk 상태로 판단하고, 그 외의 경우는 Safe 상태로 판단하는 단계; 및 상기 High Risk 상태일 때 드론에 부착된 아두이노 모듈에서 경보음을 발생시켜 해당 지역 사람들에게 전파하는 단계;를 포함하는 것을 특징으로 한다. A method for monitoring compliance with quarantine rules using a drone according to the present invention for achieving the above object includes the steps of photographing a real-time image of a corresponding area with a drone; Live-streaming the captured video using the RTMP function built into the drone and using the streaming address as an input value to a pre-made code; determining population density, distance between people, and whether a mask is worn by using the code as an object detection model; In the code above, if more than 60 people are captured in the video, more than 4 people with a distance of less than 1m from each other, and more than 1 person not wearing a mask or wearing a bad mask, it is judged as High Risk. In other cases, Determining a safe state; and generating an alarm sound from an Arduino module attached to the drone when in the high risk state and disseminating it to people in the corresponding area.

상기 인구밀집도는 드론이 고도 6m, 카메라 각도 33도에서 촬영한 60제곱미터 범위에서 1제곱미터 당 사람 수가 1명 이상일 때를 혼잡으로 분류하고, 반대의 경우는 정상으로 분류하는 것을 특징으로 한다.The population density is classified as congested when the number of people per 1 square meter is more than one in the range of 60 square meters photographed by a drone at an altitude of 6 m and a camera angle of 33 degrees, and the opposite case is classified as normal.

상기 사람 간 거리는 YOLO 모델에서 인식한 사람의 경계 사각형을 설정하고 중심점을 획득하여 2명의 중심점을 연결하여 1개의 조합을 얻고, 이 조합들을 화면상에 모두 표시하며, 중심점 간 거리를 측정하여 이 거리가 1m 이하인 사람 수를 파악하여 인원이 4명 이상일 경우 거리두기가 이루어지지 않는다고 판단하는 것을 특징으로 한다. The distance between people is determined by setting the bounding rectangle of the person recognized in the YOLO model, obtaining a center point, connecting the center points of two people to obtain one combination, displaying all of these combinations on the screen, and measuring the distance between the center points. It is characterized by determining the number of people who are 1 m or less and determining that distance is not achieved when the number of people is 4 or more.

상기 마스크착용여부는 마스크 착용 및 미착용 모습이 각각 라벨링 된 사진을 2500장씩 구해서 데이터셋을 만들어 YOLO 모델로 트레이닝시켜 얻어낸 Weight 파일을 뽑아 코드에 입력하여 영상 내 마스크 미착용자 및 불량 착용자를 식별하고 1명이라도 발견되는 경우 위험하다고 판단하는 것을 특징으로 한다. For the mask wearing status, 2500 photos each labeled with and without a mask were obtained, a data set was created, and the weight file obtained by training with the YOLO model was extracted and inputted into the code to identify those who did not wear a mask and bad wearers in the image, and identified 1 person. It is characterized in that it is determined that it is dangerous if it is found.

이와 같이, 본 발명에 따르면 통제가 어렵고 CCTV와 같은 기초 인프라 활용이 불가능한 야외 행사장에서 사람이 밀집되는 것을 막고, 사람 간 안전거리를 확보할 수 있으며, 마스크 미착용자 및 불량 착용자를 식별하여 효과적으로 방역수칙준수를 실천하도록 감시 및 경고할 수 있다.As such, according to the present invention, it is possible to prevent crowding of people at an outdoor event venue where it is difficult to control and impossible to utilize basic infrastructure such as CCTV, to secure a safe distance between people, and to identify those who do not wear masks and those who wear bad masks to effectively implement quarantine rules. You can monitor and warn to practice compliance.

도 1은 본 발명에 따른 드론을 이용한 방역수칙준수 감시방법을 나타낸 흐름도이다.1 is a flowchart showing a method for monitoring compliance with quarantine rules using a drone according to the present invention.

아래에서는 첨부한 도면을 참고로 하여 본 발명의 실시예에 대하여 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자가 용이하게 실시할 수 있도록 상세히 설명한다. 그러나 본 발명은 여러 가지 상이한 형태로 구현될 수 있으며 여기에서 설명하는 실시예에 한정되지 않는다.Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily carry out the present invention. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein.

그러면 본 발명에 따른 드론을 이용한 방역수칙준수 감시방법의 바람직한 실시예를 자세히 설명하기로 한다.Then, a preferred embodiment of the quarantine rule compliance monitoring method using a drone according to the present invention will be described in detail.

도 1은 본 발명에 따른 드론을 이용한 방역수칙준수 감시방법을 나타낸 흐름도이다.1 is a flowchart showing a method for monitoring compliance with quarantine rules using a drone according to the present invention.

도 1을 참조하면, 도 1은 본 발명에 따른 드론을 이용한 방역수칙준수 감시방법은 먼저, 드론의 임무 기능으로 해당 지역을 비행하며 실시간 영상을 촬영한다(S100). Referring to FIG. 1, in the method for monitoring compliance with quarantine rules using a drone according to the present invention, first, a real-time video is taken while flying in the area with the mission function of the drone (S100).

이어서, 촬영한 영상을 드론에 탑재된 RTMP 기능으로 라이브 스트리밍하고, 스트리밍 주소를 미리 만들어둔 코드에 입력값으로 사용한다(S200).Subsequently, the captured video is live-streamed using the RTMP function built into the drone, and the streaming address is used as an input value to the pre-made code (S200).

이어서, 상기 코드를 객체탐지모델을 활용하여 인구밀집도, 사람 간 거리, 마스크 착용 여부를 판별한다(S300).Subsequently, the above code is used to determine population density, distance between people, and whether or not a mask is worn by using an object detection model (S300).

여기서, 상기 인구밀집도는 드론이 고도 6m, 카메라 각도 33도에서 촬영한 60제곱미터 범위에서 1제곱미터 당 사람 수가 1명 이상일 때를 혼잡으로 분류하고, 반대의 경우는 정상으로 분류한다. 이때, 사람 수는 YOLO 모델로 파악할 수 있다. Here, the population density is classified as congested when the number of people per square meter is 1 or more in the range of 60 square meters photographed by a drone at an altitude of 6 m and a camera angle of 33 degrees, and the opposite case is classified as normal. At this time, the number of people can be grasped by the YOLO model.

상기 사람 간 거리는 YOLO 모델에서 인식한 사람의 경계 사각형을 설정하고 중심점을 획득한다. 이후 2명의 중심점을 연결하여 1개의 조합을 얻고, 이 조합들을 화면상에 모두 표시한다. 중심점 간 거리를 측정하여 이 거리가 1m 이하인 사람 수를 파악한다. 여기서 파악된 인원이 4명 이상일 경우 거리두기가 이루어지지 않는다고 판단한다.For the distance between people, a bounding rectangle of the person recognized in the YOLO model is set and a center point is obtained. Then, by connecting the center points of the two people, one combination is obtained, and all of these combinations are displayed on the screen. The distance between the center points is measured and the number of people whose distance is less than 1 m is determined. If the number of people identified here is 4 or more, it is determined that distancing is not achieved.

상기 마스크착용여부는 마스크 착용 및 미착용 모습이 각각 라벨링된 사진을 2500장씩 구해서 데이터셋을 만든다. 이를 YOLO 모델로 트레이닝시켜 얻어낸 Weight 파일을 뽑아낸다. 이 파일을 코드에 입력하여 영상 내 마스크 미착용자 및 불량 착용자를 식별하고 1명이라도 발견되는 경우 위험하다고 판단한다.As for whether or not the mask is worn, a dataset is created by obtaining 2,500 photos each labeled with and without a mask. Extract the weight file obtained by training it with the YOLO model. By inputting this file into the code, the person not wearing a mask or wearing a bad mask in the video is identified, and if even one person is found, it is determined to be dangerous.

이어서, 상기 코드에서 영상에 촬영된 사람 수 60명 이상, 서로 간의 거리가 1m 이내인 사람 수 4명 이상, 마스크 미착용자 및 불량 착용자 1명 이상을 포착할 경우“High Risk”상태로 판단하고, 그 외의 경우는“Safe”상태로 판단한다(S400). Next, if the above code captures 60 or more people captured in the video, 4 or more people with a distance of less than 1m from each other, and 1 or more people not wearing a mask or wearing a bad mask, it is judged as “High Risk” status, In other cases, it is determined as “Safe” (S400).

다음으로,“High Risk”상태일 때는 드론에 부착된 아두이노 모듈에서 경보음을 발생시켜 해당 지역 사람들에게 전파한다(S500).Next, in the “High Risk” state, the Arduino module attached to the drone generates an alarm sound and spreads it to people in the area (S500).

이상에서 본 발명의 실시예에 대하여 상세하게 설명하였지만 본 발명의 권리범위는 이에 한정되는 것은 아니고 다음의 청구범위에서 정의하고 있는 본 발명의 기본 개념을 이용한 당업자의 여러 변형 및 개량 형태 또한 본 발명의 권리범위에 속하는 것이다.Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements made by those skilled in the art using the basic concept of the present invention defined in the following claims are also included in the scope of the present invention. that fall within the scope of the right.

Claims (4)

해당 지역을 드론으로 실시간 영상을 촬영하는 단계;
촬영 영상을 드론에 탑재된 RTMP 기능으로 라이브 스트리밍하고, 스트리밍 주소를 미리 만들어둔 코드에 입력값으로 사용하는 단계;
상기 코드를 객체탐지모델을 활용하여 인구밀집도, 사람 간 거리, 마스크착용 여부를 판별하는 단계;
상기 코드에서 영상에 촬영된 사람 수 60명 이상, 서로 간의 거리가 1m 이내인 사람 수 4명 이상, 마스크 미착용자 및 불량 착용자 1명 이상을 포착할 경우High Risk 상태로 판단하고, 그 외의 경우는 Safe 상태로 판단하는 단계; 및
상기 High Risk 상태일 때 드론에 부착된 아두이노 모듈에서 경보음을 발생시켜 해당 지역 사람들에게 전파하는 단계;를 포함하는 것을 특징으로 하는 드론을 이용한 방역수칙준수 감시방법. Taking a real-time image of the area with a drone;
Live-streaming the captured video using the RTMP function built into the drone and using the streaming address as an input value to a pre-made code;
determining population density, distance between people, and whether a mask is worn by using the code as an object detection model;
In the code above, if more than 60 people are captured in the video, more than 4 people with a distance of less than 1m from each other, and more than 1 person not wearing a mask or wearing a bad mask, it is judged as High Risk. In other cases, Determining a safe state; and
In the high risk state, the Arduino module attached to the drone generates an alarm sound and propagates it to people in the area; quarantine rule compliance monitoring method using a drone, characterized in that it comprises. 제1항에 있어서,
상기 인구밀집도는 드론이 고도 6m, 카메라 각도 33도에서 촬영한 60제곱미터 범위에서 1제곱미터 당 사람 수가 1명 이상일 때를 혼잡으로 분류하고, 반대의 경우는 정상으로 분류하는 것을 특징으로 하는 드론을 이용한 방역수칙준수 감시방법. According to claim 1,
The population density is classified as congested when the number of people per square meter is 1 or more in the range of 60 square meters photographed by the drone at an altitude of 6 m and a camera angle of 33 degrees, and the opposite case is classified as normal. How to monitor compliance with quarantine rules. 제1항에 있어서,
상기 사람 간 거리는 YOLO 모델에서 인식한 사람의 경계 사각형을 설정하고 중심점을 획득하여 2명의 중심점을 연결하여 1개의 조합을 얻고, 이 조합들을 화면상에 모두 표시하며, 중심점 간 거리를 측정하여 이 거리가 1m 이하인 사람 수를 파악하여 인원이 4명 이상일 경우 거리두기가 이루어지지 않는다고 판단하는 것을 특징으로 하는 드론을 이용한 방역수칙준수 감시방법. According to claim 1,
The distance between people is determined by setting the bounding rectangle of the person recognized in the YOLO model, obtaining a center point, connecting the center points of two people to obtain one combination, displaying all of these combinations on the screen, and measuring the distance between the center points. A method for monitoring compliance with quarantine rules using drones, characterized in that by determining the number of people less than 1 m and determining that distance is not achieved if the number of people is 4 or more. 제1항에 있어서,
상기 마스크착용여부는 마스크 착용 및 미착용 모습이 각각 라벨링 된 사진을 2500장씩 구해서 데이터셋을 만들어 YOLO 모델로 트레이닝시켜 얻어낸 Weight 파일을 뽑아 코드에 입력하여 영상 내 마스크 미착용자 및 불량 착용자를 식별하고 1명이라도 발견되는 경우 위험하다고 판단하는 것을 특징으로 하는 드론을 이용한 방역수칙준수 감시방법. According to claim 1,
For the mask wearing status, 2500 photos each labeled with and without a mask were obtained, a data set was created, and the weight file obtained by training with the YOLO model was extracted and inputted into the code to identify those who did not wear a mask and bad wearers in the image, and identified 1 person. A method for monitoring compliance with quarantine rules using drones, characterized in that it is judged to be dangerous if even is found.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102619493B1 (en) * 2023-06-27 2024-01-03 주식회사 명빈글로벌 Method And System for Detecting Mass Casualty Incident

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102619493B1 (en) * 2023-06-27 2024-01-03 주식회사 명빈글로벌 Method And System for Detecting Mass Casualty Incident

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