视频测流技术研究现状与应用

Research Status and Application of Video-based Flow Measurement Technology

摘要:

随着计算机视觉与遥感技术的快速发展，视频图像测流技术逐渐成为水文测量领域的研究热点。文章介绍了大尺度粒子图像测速法(LSPIV)、时空图像测速法(STIV)及基于深度学习的流量估测算流方法的技术原理、研究进展与应用场景，对比分析了各类方法的优势与局限性。研究发现，LSPIV适用于高精度需求场景，STIV在宽河道监测中效率突出，而深度学习技术为复杂环境下的非接触式测流提供了新思路。未来研究需要突破环境干扰与算法效率的瓶颈，推动多源数据融合与自适应算法的发展。

Abstract:

With the rapid development of computer vision and remote sensing technologies, video image flow measurement has become a research hotspot in hydrological measurement. This paper expounds the technical principles, research progress and application scenarios of Large-Scale Particle Image Velocimetry (LSPIV), Space-Time Image Velocimetry (STIV) and deep learning-based flow estimation methods, and compares their advantages and limitations. The results show that LSPIV is applicable to scenarios requiring high precision, STIV boasts outstanding efficiency in wide river channel monitoring, and deep learning provides new ideas for non-contact flow measurement under complex environments. Future studies shall break through the bottlenecks of environmental interference and algorithm efficiency, promote the integration of multi-source data and the development of adaptive algorithms.