Xu Zhao-Dong, Zhang Zhi-Wei, Guo Ying-Qing, Zhang Yan, Zhan Yang
China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing 210096, China.
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel). 2024 Aug 14;24(16):5249. doi: 10.3390/s24165249.
In recent years, the increasing frequency of climate change and extreme weather events has significantly elevated the risk of levee breaches, potentially triggering large-scale floods that threaten surrounding environments and public safety. Rapid and accurate measurement of river surface velocities is crucial for developing effective emergency response plans. Video image velocimetry has emerged as a powerful new approach due to its non-invasive nature, ease of operation, and low cost. This paper introduces the Dynamic Feature Point Pyramid Lucas-Kanade (DFP-P-LK) optical flow algorithm, which employs a feature point dynamic update fusion strategy. The algorithm ensures accurate feature point extraction and reliable tracking through feature point fusion detection and dynamic update mechanisms, enhancing the robustness of optical flow estimation. Based on the DFP-P-LK, we propose a river surface velocity measurement model for rapid levee breach emergency response. This model converts acquired optical flow motion to actual flow velocities using an optical flow-velocity conversion model, providing critical data support for levee breach emergency response. Experimental results show that the method achieves an average measurement error below 15% within the velocity range of 0.43 m/s to 2.06 m/s, demonstrating high practical value and reliability.
近年来，气候变化和极端天气事件频发，显著增加了堤坝决口的风险，可能引发大规模洪水，威胁周边环境和公共安全。快速准确地测量河面流速对于制定有效的应急响应计划至关重要。视频图像测速法因其非侵入性、操作简便和成本低等特点，已成为一种强大的新方法。本文介绍了动态特征点金字塔Lucas-Kanade（DFP-P-LK）光流算法，该算法采用了特征点动态更新融合策略。该算法通过特征点融合检测和动态更新机制，确保了特征点的准确提取和可靠跟踪，增强了光流估计的鲁棒性。基于DFP-P-LK，我们提出了一种用于堤坝决口快速应急响应的河面流速测量模型。该模型使用光流-速度转换模型将获取的光流运动转换为实际流速，为堤坝决口应急响应提供关键数据支持。实验结果表明，该方法在0.43 m/s至2.06 m/s的速度范围内，平均测量误差低于15%，具有较高的实用价值和可靠性。
