KEYWORDS: Pipes, Image segmentation, Image processing, Tomography, Antennas, Education and training, Microwave radiation, Electromagnetism, Binary data, General packet radio service
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: Electric fields, Singular value decomposition, Data modeling, Matrices, Magnetism, Inverse problems, Neural networks, Spatial resolution, Education and training, Electromagnetism
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.
Nowadays the importance of Unmanned Aerial Vehicle (UAV) based sensing technologies is globally recognized. Indeed, thanks to the ability of investigating large areas in a very short time and at very reduced cost, the UAV sensing technology has been widely used in multiple application contexts, including security and surveillance inspections, environmental monitoring, geology, agriculture, archeology and cultural heritage. Actually, the widespread remote sensing technologies mounted on-board UAVs are mainly optical, thermal and multi-spectral sensors, which are passive technologies designed to measure the signals emitted into the optical and (near and far) infrared portions of the electromagnetic spectrum thus providing useful 2D and 3D information about the observed scene. Radar systems represent an important complementary solution. Indeed, radar system is an active system which transmits and receives electromagnetic signals at microwave frequencies, thus offering the advantages of performing inspections in free space and through-obstacle scenarios. However, UAV based radar imaging is not yet a well consolidated technology due to the significant challenges related to the acquisition modality and data processing strategies. Since both transmitting and receiving radar units must be installed on-board the UAV, this introduces not trivial issues related to payload and assets constrains. Moreover, in order to obtain reliable and easily interpretable images, a high precision UAV trajectory reconstruction must be deployed. As a contribution to this topic, an UAV imaging system prototype based on a microwave tomographic approach was recently proposed. Experimental tests at the Archaeological Park of Paestum (SA) has been recently carried out. During the survey, the UAV platform was piloted in path-planning mode, i.e. “autonomous flight” on a predefined rectangular grid and a novel imaging strategy which exploits multiple measurement lines has been developed.
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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