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 non-destructive 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.
KEYWORDS: Singular value decomposition, Electromagnetism, Matrices, Magnetism, Education and training, Inverse problems, Neural networks, Data modeling, Spatial resolution, Electric fields
This study addresses a 2D scalar electromagnetic inverse source problem by using a deep neural network-based artificial intelligence technique. Specifically, the Learned Singular Value Decomposition (L-SVD) approach based on hybrid autoencoding is adopted. The main goal is to reproduce the singular value decomposition through neural networks and compare the reconstruction performance of L-SVD and truncated SVD (TSVD) in the case of noiseless data, which represents a reference benchmark. The results demonstrate that L-SVD outperforms TSVD in terms of spatial resolution.
The paper deals with subsurface imaging via radar systems mounted onboard aerial platforms. Specifically, the attention is focused on a radar prototype installed on a small unmanned aerial vehicle (S-UAV), previously proposed by few of the authors. In particular, the challenges in terms of electromagnetic modeling and flight dynamics knowledge and control are here tackled. In this frame, an ad-hoc designed data processing strategy is presented; this strategy involves a preprocessing step and a reconstruction step. The pre-processing is performed in time domain and, beyond filtering procedures commonly exploited in radar imaging, involves a procedure devoted to compensate flight altitude variations and to account for the S-UAV trajectory, which is estimated by processing measurements collected by an onboard GPS receiver. In addition, the reconstruction of the investigated scenario is performed by means of a microwave tomographic approach based on a linear model of the electromagnetic scattering and the concept of equivalent dielectric permittivity for the propagation path. This latter allows us to properly face the imaging of buried objects, while avoiding the mathematical complexity introduced by the presence of the air-medium interface. Accordingly, the imaging is faced as a linear inverse scattering problem formulated in the spatial domain similarly to the case of a homogeneous scenario and, thanks to the concept of equivalent permittivity, depth and horizontal position of buried objects are retrieved properly. This is corroborated by means of a numerical analysis accounting for synthetic data.
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