Pavement distress detection with deep learning using the orthoframes acquired by a mobile mapping system
author
Riid, Andri
Lõuk, Roland
Pihlak, Rene
Tepljakov, Aleksei
Vassiljeva, Kristina
statement of authorship
Andri Riid, Roland Lõuk, Rene Pihlak, Aleksei Tepljakov, and Kristina Vassiljeva
source
Applied sciences
publisher
MDPI
journal volume number month
vol. 9, 22
year of publication
2019
pages
art. 4829, 22 p. : ill
url
https://doi.org/10.3390/app9224829
subject term
teekattematerjalid
defektid
pilditöötlus
kujutuvastus
keyword
pavement distress
defect detection
image recognition
image processing
deep neural network
ISSN
2076-3417
notes
Bibliogr.: 94 ref
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
1.1
Scopus
https://www.scopus.com/sourceid/21100829268
https://www.scopus.com/record/display.uri?eid=2-s2.0-85075257880&origin=inward&txGid=a5d19b5bd2746d7487943dea35ff101b
WOS
https://jcr.clarivate.com/jcr-jp/journal-profile?journal=APPL%20SCI-BASEL&year=2022
https://www.webofscience.com/wos/woscc/full-record/WOS:000502570800106
category (general)
Engineering
Tehnika
Physics and astronomy
Füüsika ja astronoomia
Chemical engineering
Keemiatehnoloogia
Computer science
Arvutiteadus
Materials science
Materjaliteadus
category (sub)
Engineering. General engineering
Tehnika. Üldine inseneriteadus
Physics and astronomy. Instrumentation
Füüsika ja astronoomia. Instrumentatsioon
Chemical engineering. Fluid flow and transfer processes
Keemiatehnoloogia. Vedeliku voolu- ja ülekandeprotsessid
Computer science. Computer science applications
Arvutiteadus. Arvutiteaduse rakendused
Materials science. General materials science
Materjaliteadus. Üldine materjaliteadus
Chemical engineering. Process chemistry and technology
Keemiatehnoloogia. Protsessi keemia ja tehnoloogia
quartile
Q2
TalTech department
tarkvarateaduse instituut
arvutisüsteemide instituut
language
inglise
Reserch Group
Laboratory of proactive technologies
Centre for intelligent systems