Microwave tomography and artificial intelligence for water leakage detection: preliminary results

The timely detection of leakage in water mains is an issue that is relevant to the sustainable and efficient use of natural resources and the prevention of environmental hazards and risks for citizens. Consequently, the development of nondestructive techniques capable of detecting and localizing water leaks in buried pipelines is of huge interest. In this contribution, we present an artificial intelligence tool to perform automatic leakage detection from ground penetrating radar tomographic images. Ground penetrating radar is a prominent technology for subsoil inspection based on the remote interaction of microwave signals with buried anomalies, but its results require expert-users and are prone to subjective interpretation. This can be counteracted by processing raw-data using microwave tomography algorithms, which are capable of delivering more easily interpretable images. However, tomographic images can be still very difficult to interpret when the assumptions underlying the algorithm fail and therefore do not lead to conclusive results. To overcome this issue, we cast the leakage-detection problem as an image segmentation task, in which the popular convolutional neural network U-NET is trained to turn tomographic images obtained from raw-data processing into binary images clearly depicting the location of the leaks. Preliminary results with full-wave synthetic data confirm the potential of the proposed approach.