融合视觉模型与统计方法，提升水位流速估算精度。
Vision-Based Water Level and Flow Estimation
- 结合SOTA视觉模型与物理先验，优化检测流程。
- 利用鲁棒滤波策略，提升水位与流速估计准确性。
- 适合水利监测、智能巡检等实际场景应用。
随着计算机视觉的快速发展，基于视觉的水位和河流表面流速估算方法已趋于成熟。相较于传统传感技术，该方法具有更高的可解释性、自动化数据存档能力以及更强的系统鲁棒性。然而，环境敏感性、精度有限及复杂现场标定等问题依然存在。本文提出一种集成框架，融合最先进（SOTA）视觉模型与统计建模方法。通过引入物理先验知识和鲁棒滤波策略，显著提升了水位检测与流量估算的准确性。代码将公开于 https://github.com/sunzx97/Vision_Based_Water_Level_and_Flow_Estimation.git。
原文摘要 · Abstract (English)
With the rapid evolution of computer vision, vision-based methodologies for water level and river surface velocity estimation have reached significant maturity. Compared to traditional sensing, these techniques offer superior interpretability, automated data archiving, and enhanced system robustness. However, challenges such as environmental sensitivity, limited precision, and complex site calibration persist. This work proposes an integrated framework that synergizes state-of-the-art (SOTA) vision models with statistical modeling. By leveraging physical priors and robust filtering strategies, we improve the accuracy of water level detection and flow estimation. Code will be available at https://github.com/sunzx97/Vision_Based_Water_Level_and_Flow_Estimation.git
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