Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Implementation of Fish Detection Based on Convolutional Neural Networks

CNN 기반의 물고기 탐지 알고리즘 구현

  • Lee, Yong-Hwan (Dept. of Digital Contents, Wonkwang University) ;
  • Kim, Heung-Jun (Dept. of Computer Science and Engineering, Gyeongnam National University of Science and Technology)
  • 이용환 (원광대학교 디지털콘텐츠공학과) ;
  • 김흥준 (경남과학기술대학교 컴퓨터공학과)
  • Received : 2020.09.21
  • Accepted : 2020.09.23
  • Published : 2020.09.30
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Abstract

Autonomous underwater vehicle makes attracts to many researchers. This paper proposes a convolutional neural network (CNN) based fish detection method. Since there are not enough data sets in the process of training, overfitting problem can be occurred in deep learning. To solve the problem, we apply the dropout algorithm to simplify the model. Experimental result showed that the implemented method is promising, and the effectiveness of identification by dropout approach is highly enhanced.

Keywords

References

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