무인기를 이용한 심층 신경망 기반 해파리 분포 인식 시스템 Deep Neural Network-based Jellyfish Distribution Recognition System Using a UAV

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In this paper, we propose a jellyfish distribution recognition and monitoring system using a UAV (unmanned aerial vehicle). The UAV was designed to satisfy the requirements for flight in ocean environment. The target jellyfish, Aurelia aurita, is recognized through convolutional neural network and its distribution is calculated. The modified deep neural network architecture has been developed to have reliable recognition accuracy and fast operation speed. Recognition speed is about 400 times faster than GoogLeNet by using a lightweight network architecture. We also introduce the method for selecting candidates to be used as inputs to the proposed network. The recognition accuracy of the jellyfish is improved by removing the probability value of the meaningless class among the probability vectors of the evaluated input image and re-evaluating it by normalization. The jellyfish distribution is calculated based on the unit jellyfish image recognized. The distribution level is defined by using the novelty concept of the distribution map buffer.
Publisher
한국로봇학회
Issue Date
2017-12
Language
Korean
Citation

로봇학회 논문지, v.12, no.4, pp.432 - 440

ISSN
1975-6291
DOI
jkros.2017.12.4.432
URI
http://hdl.handle.net/10203/237814
Appears in Collection
EE-Journal Papers(저널논문)
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