Convolutional LSTM을 이용한 유의 파고 및 파향의 실시간 추정 기법 연구

초록

Real-time estimation of wave condition is essential to improve sailing efficiency. However, existing methodologies are uneconomical due to the expensive radar and high computational complexity. To this end, we propose a neural network model capable of real-time estimation of significant wave height and direction by using raw ocean images collected from operating vessels. In the proposed method, multiple consecutive ocean images are concatenated as a single clip. Then, Convolutional Long Short-Term Memory (ConvLSTM), which combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), was trained on the clips. The final estimation is performed through regression or classification using the extracted spatiotemporal feature map. Based on the datasets collected from two different ships, our proposed method achieved the absolute error of 8cm and a relative error of 5% for significant wave height estimation. Besides, the proposed method yielded an absolute error of 6° for wave direction.

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