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dc.contributor.author
Varona, Braian Raúl
dc.contributor.author
Monteserin, Ariel José
dc.contributor.author
Teyseyre, Alfredo Raul
dc.date.available
2020-12-21T14:25:00Z
dc.date.issued
2019-05
dc.identifier.citation
Varona, Braian Raúl; Monteserin, Ariel José; Teyseyre, Alfredo Raul; A deep learning approach to automatic road surface monitoring and pothole detection; Springer London Ltd; Personal And Ubiquitous Computing; 24; 4; 5-2019; 519-534
dc.identifier.issn
1617-4909
dc.identifier.uri
http://hdl.handle.net/11336/120932
dc.description.abstract
Anomalies in road surface not only impact road quality but also affect driver safety, mechanic structure of the vehicles, and fuel consumption. Several approaches have been proposed to automatic monitoring of the road surface condition in order to assess road roughness and to detect potholes. Some of these approaches adopt a crowdsensing perspective by using a built-in smartphone accelerometer to sense the road surface. Although the crowdsensing perspective has several advantages as ubiquitousness and low cost, it has certain sensibility to the false positives produced by man-made structures, driver actions, and road surface characteristics that cannot be considered as road anomalies. For this reason, we propose a deep learning approach that allows us (a) to automatically identify the different kinds of road surface, and (b) to automatically distinguish potholes from destabilizations produced by speed bumps or driver actions in the crowdsensing-based application context. In particular, we analyze and apply different deep learning models: convolutional neural networks, LSTM networks, and reservoir computing models. The experiments were carried out with real-world information, and the results showed a promising accuracy in solving both problems.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer London Ltd
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CROWDSENSING
dc.subject
DEEP LEARNING
dc.subject
POTHOLE DETECTION
dc.subject
ROAD SURFACE MONITORING
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
A deep learning approach to automatic road surface monitoring and pothole detection
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-11-18T21:21:07Z
dc.journal.volume
24
dc.journal.number
4
dc.journal.pagination
519-534
dc.journal.pais
Reino Unido
dc.journal.ciudad
London
dc.description.fil
Fil: Varona, Braian Raúl. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
dc.description.fil
Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
dc.description.fil
Fil: Teyseyre, Alfredo Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
dc.journal.title
Personal And Ubiquitous Computing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/10.1007/s00779-019-01234-z
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s00779-019-01234-z
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