Employing Robotics and Deep Learning in Underground Leak Detection

Abstract

Leaks in water distribution networks (WDNs) produce significant economic losses. These leaks from underground pipelines affect the surrounding environment in different ways that can be detected using various technologies. This article introduces an unmanned ground vehicle (UGV) equipped with an infrared temperature sensor to remotely detect thermal anomalies on the surface caused by underground leaking pipelines. An MLX90614 low-cost thermopile infrared sensor was proposed to trace the surface temperature above leaking pipelines and record the corresponding position of each reading. Two positioning techniques were tested independently: a satellite navigation system and odometry using a magnetic encoder. Raw data from the temperature sensor along with the corresponding locations were recorded on a MicroSD card using an Arduino Uno Board through analog-to-digital converters (ADCs) and serial peripheral interface (SPI) bus. Experimental work showed the ability of the robot in detecting thermal anomalies caused by leaks accurately with the required trace resolution. Finally, a 2-D convolutional neural network (CNN) solution is introduced for distinguishing true detections from false alarms.