Open AccessDesign of distance sensing and distance using quadcopter drone based on face recognition method
Author(s) -
R. Agustiady,
Nur Rachman Supadmana Muda,
Desyderius Minggu,
C. Herkariawan,
M. L. N. Suryana
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1098/3/032012
Subject(s) - quadcopter , drone , adversary , computer science , task (project management) , robot , face (sociological concept) , artificial intelligence , computer security , automation , visualization , field (mathematics) , real time computing , inertial measurement unit , human–computer interaction , aeronautics , simulation , engineering , systems engineering , aerospace engineering , mechanical engineering , social science , genetics , mathematics , sociology , pure mathematics , biology
With the development of communication and automation technology at this time, the Robot is widely used as a tool to assist the task of long-distance operations in the army combat unit. Especially for tasks that require high mobilization and tasks that have a relatively high level of danger, therefore the use of Unmanned Aerial Vehicles for remote sensing is developing very rapidly both in the field of civilian and military defense equipment. So in this study the researchers will develop the use of UAVs, especially in the type of quadcopter drones for remote sensing enemy areas. In this case, enemy surveillance still uses a lot of personnel, so monitoring the area requires a large amount of personnel. Conditions like this that can require high concentration and will drain personnel and logistics. The use of this remote sensing robot can minimize the use of TNI AD personnel directly, because in the sensing it is replaced by the role of visualization on the drone and has the ability to determine recognize the enemy by adding a face recognition system, so that later the tool is able to recognize enemies by detecting captured faces by the drone’s camera.