评估深度学习计算机视觉用于河流水位测量
Wen-Cheng Liu1, Wei-Che Huang1
1Department of Civil and Disaster Prevention Engineering, National United University, Miaoli, 360302, Taiwan.
Heliyon
|February 23, 2024
概括
这项研究引入了SegNet神经网络,用于精确测量河流水位,其性能优于传统方法. 改进的培训数据增强了SegNet的功能.
科学领域:
- 环境监测环境监测环境监测
- 计算机视觉 计算机视觉 计算机视觉
- 深度学习是一种深度学习.
背景情况:
- 传统的基于图像的测量站因季节性变化而难以进行可靠的河流水位测量.
- 现有的摄像机测量方法在动态的河流环境中缺乏精度.
- 需要先进的技术来提高水文监测的准确性和可靠性.
研究的目的:
- 开发和评估一种基于深度学习的方法,用于准确测量河流水位.
- 将SegNet神经网络的性能与传统的连续图像减法 (CIS) 方法进行比较.
- 评估训练数据集特征对神经网络有效性的影响.
主要方法:
- 利用现场历史图像来训练神经网络,包括SegNet,U-Net和FCN.
- 采用SegNet,一个深度学习计算机视觉模型,用于水位分析.
- 将SegNet的性能与传统的连续图像减法 (CIS) 方法进行比较.
主要成果:
- 与CIS方法相比,SegNet神经网络在测量河流水位方面表现出更高的准确性.
- 实现了0.013m和0.066m之间的低根平均平方误差 (RMSE),具有0.998.99的高相关系数.
- 用于培训的更大,更多样化和更高分辨率的图像数据集提高了SegNet性能.
结论:
- 深度学习计算机视觉,特别是SegNet神经网络,为提高河流水位监测提供了巨大的潜力.
- 培训数据的质量和多样性对于优化SegNet在水文应用中的性能至关重要.
- 这种先进的技术有望改善河流系统的水位监测和管理.
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