Mostrar el registro sencillo del ítem

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