Water Level Inversion Detection Method for Water Level Images without a Scale in Complex Environments

Abstract

Accurately perceiving changes in water level information is key to achieving the fine control of water and flooding; however, the existing technology cannot achieve water level recognition in complex and harsh environments, such as at night; in haze, rain, or snow; or during obscuration by floating objects or shadows. Therefore, on the basis of a deep analysis of the characteristics of water level images in complex and harsh environments, in this study, we took full advantage of a deep learning network’s ability to characterise semantic features and carried out exploratory research on water level detection in no-water-ruler scenarios based on the two technical means of target detection and semantic segmentation. The related experiments illustrate that all the methods proposed in this study can effectively adapt to complex and harsh environments. The results of this study are valuable for applications in solving the difficulties of accurate water level detection and flood disaster early warnings in poor-visibility scenarios.