Development of Real-Time Water Level Detection Technique by CCTV and Deep Learning

With the intensification of climate change, extreme rainfall events have become more frequent, increasing the risks of urban flooding and river overflow. As a result, real-time water level monitoring has become essential for disaster prevention and water resources management. Conventional monitoring methods mainly rely on water gauges and sensors, which are costly to install and maintain and are often constrained by environmental and terrain conditions. Moreover, most image-based approaches require calibrated staff gauges as reference objects, limiting their flexibility in practical applications.

This study proposes a daytime water level monitoring approach that integrates existing CCTV systems with deep learning techniques. Instance segmentation models based on Mask R-CNN and YOLOv11 are employed to automatically extract water regions from images, and their performance is evaluated in terms of mask quality and inference efficiency. Vertical pixel variations at selected locations within the segmented water regions are further analyzed to estimate water level changes. The results indicate that the proposed method can effectively capture daytime water level variation trends, offering advantages such as low cost, non-contact measurement, and high scalability for multi-station real-time monitoring.

 

Keywords: Deep learning, image detection, water level monitoring, CCTV