한국환경복원기술학회지 (Journal of the Korean Society of Environmental Restoration Technology)

  • 제24권6호
  • /
  • Pages.89-96
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  • 2021
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  • 1229-3032(pISSN)
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  • 2733-5011(eISSN)
한국환경복원기술학회 (The Korea Society of Environmental Restoration Technology)
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갯벌 생태계 모니터링을 위한 딥러닝 기반의 영상 분석 기술 연구 - 신두리 갯벌 달랑게 모니터링을 중심으로 -

Image analysis technology with deep learning for monitoring the tidal flat ecosystem -Focused on monitoring the Ocypode stimpsoni Ortmann, 1897 in the Sindu-ri tidal flat -

  • 투고 : 2021.11.19
  • 심사 : 2021.11.30
  • 발행 : 2021.12.31
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초록

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

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과제정보

본 논문은 환경부의 환경기술개발사업(과제번호: RE202101196)의 지원을 받아 한국환경연구원이 수행한 "ICT 기반 생태계 모니터링 기술 및 동식물 탐지 AI 알고리즘 개발(2021-057R)" 사업의 연구결과로 작성되었습니다.

참고문헌

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