Vision-Based Water Level and Flow Estimation
For hydrology and environmental monitoring, this framework enhances vision-based water level and flow estimation, though it is an incremental improvement over existing methods.
This work proposes an integrated framework combining SOTA vision models with statistical modeling to improve water level detection and flow estimation accuracy, addressing challenges like environmental sensitivity and complex calibration.
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