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Wireless Communications and Mobile Computing
Volume 2017, Article ID 9386928, 11 pages
Research Article

Rock Falls Impacting Railway Tracks: Detection Analysis through an Artificial Intelligence Camera Prototype

1“Sapienza” University of Rome and Research Center for Geological Risk (CERI), P.le Aldo Moro No. 5, 00185 Rome, Italy
2Tecnostudi Ambiente S.r.l., Piazza Manfredo Fanti No. 30, 00185 Rome, Italy

Correspondence should be addressed to Matteo Fiorucci; ti.1amorinu@iccuroif.oettam

Received 26 May 2017; Revised 13 July 2017; Accepted 6 August 2017; Published 12 October 2017

Academic Editor: Cesar Briso

Copyright © 2017 Andrea Fantini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


During the last few years, several approaches have been proposed to improve early warning systems for managing geological risk due to landslides, where important infrastructures (such as railways, highways, pipelines, and aqueducts) are exposed elements. In this regard, an Artificial intelligence Camera Prototype (AiCP) for real-time monitoring has been integrated in a multisensor monitoring system devoted to rock fall detection. An abandoned limestone quarry was chosen at Acuto (central Italy) as test-site for verifying the reliability of the integrated monitoring system. A portion of jointed rock mass, with dimensions suitable for optical monitoring, was instrumented by extensometers. One meter of railway track was used as a target for fallen blocks and a weather station was installed nearby. Main goals of the test were (i) evaluating the reliability of the AiCP and (ii) detecting rock blocks that reach the railway track by the AiCP. At this aim, several experiments were carried out by throwing rock blocks over the railway track. During these experiments, the AiCP detected the blocks and automatically transmitted an alarm signal.