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심층학습 기반의 자동 객체 추적 및 핸디 모션 제어 드론 시스템 구현 및 검증
김영수,이준범,이찬영,전혜리,김승필,Kim, Youngsoo,Lee, Junbeom,Lee, Chanyoung,Jeon, Hyeri,Kim, Seungpil 대한임베디드공학회 2021 대한임베디드공학회논문지 Vol.16 No.5
In this paper, we implemented a deep learning-based automatic object tracking and handy motion control drone system and analyzed the performance of the proposed system. The drone system automatically detects and tracks targets by analyzing images obtained from the drone's camera using deep learning algorithms, consisting of the YOLO, the MobileNet, and the deepSORT. Such deep learning-based detection and tracking algorithms have both higher target detection accuracy and processing speed than the conventional color-based algorithm, the CAMShift. In addition, in order to facilitate the drone control by hand from the ground control station, we classified handy motions and generated flight control commands through motion recognition using the YOLO algorithm. It was confirmed that such a deep learning-based target tracking and drone handy motion control system stably track the target and can easily control the drone.