| Summary: | Ocean state estimation is a basic problem in the field of ocean engineering. Under the trend of data-driven, the development of intelligent ship decision-making, ocean energy system design and other aspects, are inseparable from the estimation of wave parameters in the ocean area. In recent years, researchers have developed remote sensing technology to monitor ocean waves. However, sensor-based methods all have a key limitation, which is high cost and fault worry. More importantly, one major limitation exists in current research: due to lack of change information and relying on a single feature of spatial data, the final predictive results are inaccurate. Adopting a 3D Convolutional Neural Network is a possible solution to improve the detection accuracy. Unfortunately, it cannot be deployed in the ocean environment due to lack of physical network connections. To resolve these issues, we develop a light-weight version of 3D Convolutional Neural Network, namely a low-cost, high-accuracy detection scheme to foresee ocean wave parameters using a 3D Mobile Convolutional Neural Network technique called iOceanSee in the marine environment. iOceanSee employs a mobile terminal composed of low-cost measuring equipment and non-interference (except light) device-an RGB camera to collect video data in real time. It extracts both space and time features through three-dimensional depthwise separable convolutions. More specifically, iOceanSee is able to capture the encoding motion information from multiple adjacent frames of the video, according to which period and height of the waves being evaluated. Our experimental results conclude that iOceanSee obtains comparable performance to 3D Convolutional Neural Network and outperforms other models in terms of measurement accuracy in the marine environments.
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