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.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.