一个视频数据集用于近岸破浪类型分类
Hang Yin1,2,3, Feng Cai4,5, Hongshuai Qi2
1College of Ocean and Earth Sciences, Xiamen University, Xiamen, 361104, China.
Scientific data
|October 31, 2025
概括
本研究引入了第一个视频数据集,用于分类波浪断裂类型,这对于理解沿海水力动力学至关重要. 该数据集包含9000个剪辑,可以通过深度学习对波浪能量消散进行高级分析.
科学领域:
- 沿海的地形形态.
- 海洋学 海洋学 海洋学
- 遥感是一种远程传感.
背景情况:
- 波浪破裂类型是近岸水力动力学和能量消耗的关键.
- 当前的遥感方法经常使用静态图像,缺少动态断裂事件演变.
- 视频分析提供了一种更全面的方法来研究波折.
研究的目的:
- 引入第一个公开可用的视频数据集,用于破浪类型的分类.
- 为训练和评估用于波折分析的机器学习模型提供一个强大的数据集.
- 通过动态视频数据,推进近岸水力动力学过程的理解.
主要方法:
- 收集了9000个标记的视频片段,从15个摄像头收集到各种沿海地点的波浪破裂.
- 实施了严格的数据策划工作流程:视频细分,裁剪,标签和提取.
- 使用深度学习架构,将卷积神经网络 (CNN) 和循环神经网络 (RNN) 结合起来进行分类.
主要成果:
- 开发的数据集包括三种主要波浪断裂类型:溢出,入和冲浪.
- 分类实验使用CNN-RNN深度学习模型实现了最先进的性能.
- 数据集的质量通过一个细致的策划过程来确保.
结论:
- 新的视频数据集显著提高了对波折类型的遥感能力.
- 这种资源有助于更准确地建模波浪能量消散和近岸过程.
- 这些发现强调了深度学习在分析动态沿海现象方面的有效性.
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