Research on Water-Level Recognition Method Based on Image Processing and Convolutional Neural Networks

• Published in: Water (Basel) 2022-06, Vol.14 (12), p.1890
• Main Authors: Dou, Gang, Chen, Rensheng, Han, Chuntan, Liu, Zhangwen, Liu, Junfeng
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Aquatic resources; Artificial intelligence; Automation; Cameras; Catchment scale; Catchments; China; Classification; Costs; Deep learning; Digital imaging; Edge detection; Environmental monitoring; Floods; Hydrologic research; Hydrology; Image processing; Information processing; Maintenance; Management; Methods; Mountains; Neural networks; Precipitation; Rivers; Sensors; Surface water; Template matching; Water; Water levels; Water management; Water resources; Water resources management; Watersheds
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w14121890

Water level dynamics in catchment-scale rivers is an important factor for surface water studies. Manual measurement is highly accurate but inefficient. Using automatic water level sensors has disadvantages such as high cost and difficult maintenance. In this study, a water level recognition method based on digital image processing technology and CNN is proposed. For achieving batch segmentation of source images, the coordinates of the water ruler region in the source image and characters’ region and the scale lines’ region on the ruler are obtained by using image processing algorithms such as grayscale processing, edge detection, and the tilt correction method based on Hough-transform and morphological operations. The CNN is then used to identify the value of digital characters. Finally, the water level value is calculated according to the mathematical relationship between the number of scale lines detected by pixel traversal in the binarized image and the value of digital characters. This method is used to identify the water levels of the water ruler images collected in the Hulu watershed of the Qilian Mountains in Northwest China. The results show that the accuracy compared with the actual measured water level reached 94.6% and improved nearly 24% compared to the template matching algorithm. With high accuracy, low cost, and easy deployment and maintenance, this method can be applied to water level monitoring in mountainous rivers, providing an effective tool for watershed hydrology research and water resources management.