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.

1. 서 론
 2. 로봇 시스템
  2.1 하드웨어 구성
  2.2 기능
 3. 심층 신경망
  3.1 학습 데이터
  3.2 신경망 구조
  3.3 신경망 학습
 4. 해파리 분포 인식 알고리즘
  4.1 해파리 후보군 선택
  4.2 해파리 분포도
 5. 실 험
 6. 결 론
 References

• 구정모(KAIST, Daejeon, Korea) | Jungmo Koo
• 명현(Civil & Environmental Engineering, KAIST, Daejeon, Korea) | Hyun Myung Corresponding author