An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity

doi:10.12454/j.jsuese.202400869

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An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity

更新时间：2025-05-20

- An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity
- Advanced Engineering Sciences Pages: 1-12(2025)
- 作者机构：
  
  1.龙源（北京）新能源工程技术有限公司，北京 100034
  2.上海华讯网络系统有限公司服务运作部，成都 6100741
- 作者简介：
- 基金信息：
- DOI：10.12454/j.jsuese.202400869
  CLC： TP183
- Received：28 October 2024，
  
  Revised：18 April 2025，
  
  Published Online：19 May 2025，
- 稿件说明：
移动端阅览
.An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity[J].Advanced Engineering Sciences,XXXX,XX(XX):1‒12.周继威,安国成,王根一.基于改进Unet网络的河流表面流速识别方法[J].工程科学与技术,

An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity[J/OL]. Advanced engineering sciences, 2025, 1-12.
.An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity[J].Advanced Engineering Sciences,XXXX,XX(XX):1‒12.周继威,安国成,王根一.基于改进Unet网络的河流表面流速识别方法[J].工程科学与技术, DOI：10.12454/j.jsuese.202400869.

An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity[J/OL]. Advanced engineering sciences, 2025, 1-12. DOI： 10.12454/j.jsuese.202400869.

摘要

河流表面流速的精准识别是水力发电效能优化的关键基础，针对复杂河流环境的数据采集差异性及少样本训练导致该领域深度学习模型泛化性不足的问题，本研究提出一种基于改进Unet网络的河流表面流速识别方法，旨在通过优化标签构建、训练策略及网络架构提升流速识别的精度与鲁棒性。本研究聚焦3个核心问题：1）基于钟型与阶梯式分布特性生成高精度灰度标签，将河道中心至边缘的流速梯度映射为灰度值，结合多摄像头多分辨率（2 560×1 440、1 920×1 080、1 280×720）数据采集构建跨场景数据集（河海实验室、Belgium）；2）设计双输出分支Unet网络，通过编码器

-解码器对称结构提取多尺度特征，在解码阶段引入融合层与解构层实现时间维度信息融合与通道匹配，同时输出2维流速分布图和1维像素位移值，结合双重监督损失函数优化模型；3）采用时空训练法，以连续32帧视频序列为输入，结合多尺度随机裁剪（160~640像素）及沿河道中心纵向掩码增强（掩码B，

=0.1）提升动态特征捕捉能力。实验表明，本方法在河海数据集低、中、高流速段（0~1、1~2、2~3 m/s）的RMSE分别为0.069、0.103、0.238，较Fast光流法（0.794）、CBEGAN（0.463）及Two-stage-method（0.297）降低50%~80%；Belgium数据集

MSE

为0.059（

=0.047），流速分布匹配精度（

AVD

）与准确率（

）达0.892和0.942；单帧推理时间22 ms（较CBEGAN减少67%），为水力发电效能优化、洪水预警等场景提供了可靠数据支持。

Abstract

Objective

Accurate estimation of river surface flow velocity is critical for optimizing hydropower generation efficiency and enhancing flood warning systems. Existing deep learning models face challenges in generalization due to limited training samples and data heterogeneity across complex river environments. This study aimed to address these limitations by developing a robust framework that integrates improved data labeling strategies

optimized training methods

and a novel neural network architecture to achieve high-precision velocity estimation.

Methods

Methods A dual-output branch structure was designed within an improved U-Net architecture to simultaneously supervise velocity distribution and pixel displacement values

enhancing feature representation and model robustness. The framework incorporated a spatiotemporal training strategy

where continuous video sequences were utilized to capture temporal dynamics. Data preprocessing included multi-resolution image acquisition (2560×1 440

1 920×1 080

1 280×720) from simulated river channels and natural environments. Two label generation methods—bell-shaped and stepped distributions—were proposed to encode velocity information into grayscale maps

balancing precision and computational efficiency. Multi-scale random cropping (160~640 pixels) and a customized masking strategy were applied to augment training data

focusing on critical river channel regions. Model performance was evaluated using RMSE

MAVD (matching accuracy of velocity distribution)

and FRA (Flow Rate Accuracy) metrics across the Hehai

Belgium

and real-world CDJJB datasets.

Results and Discussions

The proposed framework demonstrated superior performance across diverse datasets

achieving an average

MSE

of 0.137 on the Hehai dataset (subranges: H(0-1)=0.069

H(1-2)=0.103

H(2-3)=0.238) and M

=0.109

outperforming Fast optical flow (RMSE=0.272)

CBEGAN ( RMSE=0.217)

and the Two-stage method ( RMSE=0.183) by 50%~80%. On the Belgium dataset

RMSE and MAE were reduced to 0.059 and 0.047

respectively

while cross-dataset validation on real-world CDJJB data confirmed robustness (RMSE=0.086

MAE=0.073) under natural turbulence and lighting variations. These results validated the framework’s ability to address data heterogeneity and limited training samples through integrated spatiotemporal training and adaptive augmentation.Multi-scale random cropping (160~640 pixels) significantly enhanced velocity recognition accuracy. Larger crops (640 pixels) preserved spatial context

achieving MAVD=0.862 and FRA=0.927

whereas smaller crops (160 pixels) limited velocity detection to 1.5 m/s

inadequate for high-flow scenarios (

3 m/s). Intermediate scales (320 pixels) balanced computational efficiency and accuracy (MAVD=0.816)

with training loss curves revealing accelerated convergence for 640-pixel inputs

reducing loss saturation by 40% compared to 160-pixel crops. Customized masking strategies further improved precision by focusing on critical river regions. Mask B

aligned with high-velocity zones

achieved MAVD=0.892 and FRA=0.942

surpassing random masking (MAVD=0.881) by 4% and reducing prediction uncertainty by 23% in flows

2 m/s. In contrast

random masking (k=3

s=0.3) degraded accuracy (MAVD=0.722) due to excessive occlusion of hydrodynamic features

underscoring the nec

essity of domain-specific augmentation.Temporal sequence optimization revealed N=32 as the optimal video length

balancing spatiotemporal feature extraction (MAVD=0.816

FRA=0.894). Longer sequences (N=48/64) introduced redundancy (R=0.35~0.42)

degrading MAVD by 8%–12% and increasing inference time by 72% (N=64: 38 ms vs. N=32: 22 ms). Information entropy analysis confirmed redundant features (R

0.3) in extended sequences increased computational complexity without improving accuracy. Comparative analysis with state-of-the-art methods highlighted the framework’s advantages. For high-velocity flows (

2 m/s)

the dual-output architecture reduced RMSE to 0.238

a 70% improvement over traditional optical flow (OTV: 0.794). Real-time inference at 22 ms/frame surpassed CBEGAN (67 ms) and the Two-stage method (41 ms). On the Belgium dataset

MAE=0.047 in low-flow conditions (

0.8 m/s) outperformed OTV (0.074) and CBEGAN (0.095). Cross-dataset validation under natural turbulence (CDJJB) maintained MAE=0.073

demonstrating adaptability to environmental heterogeneity.The integration of adaptive label generation (bell-shaped/stepped distributions) and spatiotemporal training addressed data scarcity by leveraging sequential video dynamics. Mask B’s longitudinal alignment with high-velocity zones ensured focused learning on hydrodynamic features

while multi-scale cropping enhanced generalization through spatial context retention.. These results validate the framework’s potential for optimizing energy output and flood management

while emphasizing the necessity of expanded datasets to address environmental variability.

Conclusions

The improved U-Net framework effectively addresses challenges in river surface velocity estimation by integrating spatiotemporal training

adaptive labeling

and data augmentation. The dual-output structure ensures accurate velocity mapping and displacement prediction

while multi-scale cropping and targeted masking enhance generalization. Experimental results validate the method’s efficiency and accuracy across diverse datasets

with significant implications for hydropower optimization and flood management. Limitations in handling low-light conditions highlight the need for nighttime dataset expansion in future work.

关键词

Keywords

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