An intelligent water level monitoring method based on SSD algorithm

Water levels are essential components for the observation and management of water resources. However, the existing water level monitoring methods either require manpower, which is inefficient, or are restricted to a certain environment. This paper proposes a novel approach that can automatically monitor, recognize and calculate the water level based on deep learning. First, a series of experiments were performed in a physical pool to obtain images from real scenes. Then, the original Single Shot MultiBox Detector (SSD) preprocessing model was trained and optimized. Subsequently, a trained and verified staff gauge detection model was obtained, which was applied to extract the staff gauge information from images. Finally, a 24-hour time series of water level changes was simulated and analyzed using photographic images principle, and the NSE and R2 were 0.98 and 0.99, respectively. These results indicate that the proposed method is more accurate in practice and can provide a relatively accurate and reliable measurement technique.