A Computer Vision System for Staff Gauge in River Flood Monitoring

Abstract

Rivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames.