Water level is a key element of hydrological measurement. Accurate water level measurement is of great significance for flood disaster prevention and water metering. With the construction of intelligent hydraulic engineering and the large-scale deployment of video equipment, the water level recognition methods based on image processing have been processed rapidly, which is currently cutting-edge research interest in the field of water level measurement. This article proposes a monocular vision-based gaugeless water level measurement method. Firstly, deep learning techniques are used to formulate a water surface segmentation model enabling automated waterline detection from water edge images. Subsequently, utilizing spatial mapping derived from camera calibration and sectional constraints, 3D coordinates corresponding to waterline pixels are computed. Finally, statistical methods are applied to compute the water level. The method is applied to an indoor flume experiment to validate its accuracy. The average number of falsely segmented pixels on the water line is 0. 825, which shows that the water surface segmentation is accurate. The mean absolute error and root mean square error are 1. 5 mm and 1. 9 mm, respectively. The results show that the method can accurately measure the variation process of water level.