基于深度学习的近岸海浪等级视频监测

深度学习是机器学习的重要研究领域，同时作为大数据的有效处理和分析工具越来越受到关注。以多源长时间序列近岸海浪视频环境数据为样本，波浪仪同步测量海浪等级数据为图像标签，构建了面向海洋环境适用于深度学习的海浪训练集、测试集。通过数据扩增技术对视频监测数据进行预处理，提高模型泛化能力，依据视频的相关性，引入误差函数，优化模型灵敏度，提出了适用于海洋领域海浪等级深度学习模型架构（Wave-CNNs），最后将提出的改进深度学习模型应用于3000样本海浪图像训练集，并通过300样本海浪图像测试集对结果进行验证，实验结果表明，算法对3个等级海浪识别精度达到了66.6%，优于传统Bayes及SVM方法。

Nearshore wave grade video monitoring based on deep learning

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.