Nearshore wave grade video monitoring based on deep learning

ZHENG Zong-sheng; HAO Jian-bo; HUANG Dong-mei; ZOU Guo-liang

doi:10.13634/j.cnki.mes20170622

Volume 36 Issue 6

Dec. 2017

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Chinese Journal of MARINE ENVIRONMENTAL SCIENCE > 2017 > 36(6): 934-940. > DOI: 10.13634/j.cnki.mes20170622

ZHENG Zong-sheng, HAO Jian-bo, HUANG Dong-mei, ZOU Guo-liang. Nearshore wave grade video monitoring based on deep learning[J]. Chinese Journal of MARINE ENVIRONMENTAL SCIENCE, 2017, 36(6): 934-940. DOI: 10.13634/j.cnki.mes20170622

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Nearshore wave grade video monitoring based on deep learning

College of Information Technology, Shanghai Ocean University, Shanghai 201306, China

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Received Date: October 11, 2016
Revised Date: November 17, 2016

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Abstract

Deep learning is an important research field of machine learning. As an effective tool for Big Data processing and analysis, it has been paid more attention. Based on multi-source long time sequence nearshore wave video data labeled by wave synchronous measure in situ, the ocean-specific wave grade deep learning model architecture (Wave-CNNs) was proposed. We constructed the wave train and test datasets which were suitable for deep learning in marine environment. The video monitoring images were preprocessed by using the data augmentation technology to improve the generalization ability of model. According to the correlation of video, error function was introduced to optimize model sensitivity. Finally, the proposed improved deep learning model was applied to 3000-sample wave image training set, and the results were verified by 300-sample wave image test set. The results showed that Wave-CNNs achieved 66.6% recognition accuracy, which was superior to traditional Bayes and SVM methods.
- deep learning,
- Big Data,
- wave grade,
- convolutional neural networks

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