Robust water level measurement method based on computer vision

Abstract

Compared with traditional methods based on non-visual devices, the water level measurement method based on computer vision has the advantages of low equipment installation and maintenance cost, convenient visualization, and so on. However, the complex condition of the monitoring site, such as different weather, lighting, water quality, staff gauge appearance, camera imaging, etc., pose significant challenges to the existing computer vision method, which can cope with specific situations but often cannot be universal. We propose a robust water level measurement method to overcome this problem. Firstly, we introduce a mapping method, which preprocesses the original image's extended region of interest (EROI) to obtain the transformed image. The region of interest (ROI) here refers to the staff gauge region, and the “extended” means taking advantage of the contextual information. Secondly, we construct a high-resolution representation deep learning regression model for water line positioning in the transformed image. Then, we trained the model with a robust data augmentation method consisting of two submethods: appearance-based data augmentation (ADA) and random extension in the direction (RED). Finally, we did a series of experiments and analyses on the widely distributed test dataset we built with two groups of criteria to evaluate the results, which are normalized since the data set contains samples of different scales. The result of this study is significantly improved compared with other computer vision methods. It shows that this study has robustness in various environments and has vast promotion value and application potential.