基于深度学习的视觉水位识别技术与装备

Visual Water Level Recognition Technology and Equipment Based on Deep Learning

摘要: 随着人工智能科技的快速发展与不断进步，水文监测领域正逐步迈向智能化和信息化，对水位监测的实时性和精确性提出了更高要求。在此背景下，本文开创性地设计并实现了一款深度学习驱动的武大AiFlow视觉水位计，融合了前沿人工智能算法与高清晰度视频捕获技术，旨在为河流、湖泊及水库等多类水体提供一个即时且精准的水位测量解决方案。武大AiFlow视觉水位计已成功部署于多个水位监控点，通过对麦穰水文站与黄壁庄水文站的实证研究，进行了详尽的性能验证与分析。结果表明，武大AiFlow视觉水位计监测方案精度高，能够满足水位监测规范要求，在背景杂乱、光线变化、水体波动等复杂场景下也具有良好的适用性，其可视化功能有利于历史数据复盘和远程监控与管理，满足愈加精细化的市场需求。

Abstract: With the rapid development and continuous progress of Artificial Intelligence (AI) technology, the field of hydrological monitoring is gradually moving towards intelligence and informatization, which puts forward higher requirements for real-time and accuracy of water level monitoring. In this context, this article innovatively designs and implements a deep learning driven Wuhan University AiFlow visual water level meter, integrating cutting-edge AI algorithms and high-definition video capture technology, aiming to provide an instant and accurate water level measurement solution for multiple types of water bodies such as rivers, lakes, and reservoirs. Wuhan University AiFlow visual water level meter has been successfully deployed at numerous water level monitoring points, and the detailed performance verification and analysis has been conducted through empirical research on Mairang Hydrological Station and Huangbizhuang Hydrological Station. The results show that the Wuhan University AiFlow visual water level meter monitoring scheme has high accuracy, meets the water level monitoring standards, and has good applicability in complex scenarios such as cluttered backgrounds, changes in lighting, and fluctuations in water bodies. Its visualization function is conducive to historical data review and remote monitoring and management, meeting the increasingly refined market demand.