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논문 기본 정보

자료유형
학술저널
저자정보
Ji-Su Ryu (Incheon National University) Ji-Won Jung (Incheon National University) Chan-Ho Jeong (Incheon National University) Byoung-Jo Choi (Incheon National University) Myeong-lyeol Lee (Incheon National University) Hyung Wook Kwon (Incheon National University)
저널정보
한국양봉학회 Journal of Apiculture Journal of Apiculture Vol.36 No.4
발행연도
2021.11
수록면
273 - 280 (8page)
DOI
10.17519/apiculture.2021.11.36.4.273

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초록· 키워드

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A new honeybee in-out monitoring system is proposed using real-time deep-learning based image recognition and tracking. The specific design of beehive gate is turned out to be an important factor for accurate bee movement monitoring. We check a series of beehive gate designs for the monitoring system. A novel gate design employing heart valve structure is proposed for ensuring one-way traffic for the bees as well as one-at-a-time gate passing, resulting in an improved bee detection accuracy. As for the deep-learning based image recognition framework, YOLOv4 is used in the proposed system for a better honeybee-detection accuracy as well as a faster detection in comparison to YOLOv3 which was employed for our previous study. In addition, DeepSORT algorithm is employed for a reliable tracking of the detected honeybees. In our experiments the proposed honeybee monitoring system exhibited 99.5% detection accuracy, while our previous system resulted in 97.5% in the same settings.

목차

Abstract
INTRODUCTION
MATERIALS AND METHODS
RESULTS AND DISCUSSION
LITERATURE CITED

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