Water Level Detection in Adverse Weather Conditions Using Security Cameras

Various techniques in computer vision have been proposed for water level detection. However, existing methods face challenges during adverse conditions including snow, fog, rain, and nighttime. In this paper, we introduce a novel approach that analyzes images for water level detection by incorporating a deblurring process to increase image clarity. By employing real-time object detection technique YOLOv5, we show that the proposed approach can achieve significantly improved precision, during both daytime and nighttime under under challenging weather circumstances.