Vision-Based Water Level and Flow Estimation

TL;DR

This paper presents a vision-based framework combining advanced computer vision models with statistical methods to improve water level and flow estimation accuracy, addressing environmental and calibration challenges.

Contribution

It introduces an integrated approach that leverages physical priors and robust filtering to enhance water measurement precision using vision-based techniques.

Findings

Improved accuracy in water level detection.

Enhanced flow estimation robustness.

Effective handling of environmental variability.

Abstract

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