[논문]갯벌 생태계 모니터링을 위한 딥러닝 기반의 영상 분석 기술 연구 - 신두리 갯벌 달랑게 모니터링을 중심으로 -

김동우; 이상혁; 유재진; 손승우

doi:10.13087/kosert.2021.24.6.89

[국내논문] 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 - 원문보기

環境復元綠化 = Journal of the Korean Society of Environmental Restoration Technology, v.24 no.6, 2021년, pp.89 - 96

김동우 (한국환경연구원) , 이상혁 (한국환경연구원) , 유재진 (한국환경연구원) , 손승우 (한국환경연구원)

Abstract ▼ AI-Helper

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.

Keyword

표/그림 (6)

그림 Figure 1. Ocypode stimpsoni monitoring area in Sindu-ri tidal flat
표 Table 1. Information of UAV and Sensor(RGB)
그림 Figure 2. Concept of IOU
그림 Figure 3. (a) RGB image of UAV, (b) Area expansion. (c),(d) Ocypode stimpsoni and burrow labelling
그림 Figure 4. Automatic classification result of Ocypode stimpsoni and Burrow using U-net(Red : Ocypode stimpsoni, Blue : Burrow)
표 Table 2. IOU result of Model

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