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Environmentally adaptive fish or no-fish classification for river video fish counters using high-performance desktop and embedded hardware

author
Soom, Jürgen
Pattanaik, Vishwajeet
Leier, Mairo
Tuhtan, Jeffrey Andrew
statement of authorship
Jürgen Soom, Vishwajeet Pattanaik, Mairo Leier, Jeffrey A. Tuhtan
source
Ecological Informatics
publisher
Elsevier
journal volume number month
vol. 72
year of publication
2022
pages
art. 101817, 14 p. : ill
url
https://doi.org/10.1016/j.ecoinf.2022.101817
subject term
kalad (zooloogia)
kaitse
keskkonnakaitse
keskkond
riistvara
keyword
Fish detection
Deep learning
Underwater video
Environmental classification
Embedded hardware
ISSN
1574-9541
notes
Bibliogr. p. 13-14
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
1.1
Scopus
Journal metrics at Scopus
Article at Scopus
WOS
Journal metrics at WOS
Article at WOS
category (general)
Mathematics
Computer science
Agricultural and biological sciences
Environmental science
category (sub)
Mathematics. Applied mathematics
Computer science. Computational theory and mathematics
Mathematics. Modeling and simulation
Agricultural and biological sciences. Ecology, evolution, behavior and systematics
Environmental science. Ecology
Computer science. Computer science applications
Environmental science. Ecological modeling
kvartiil
Q1
TTÜ department
arvutisüsteemide instituut
language
inglise
Uurimisrühm
Centre for biorobotics
Information systems group
Centre for environmental sensing and intelligence
Embedded AI research laboratory
Soom, J., Pattanaik, V., Leier, M., Tuhtan, J.A. Environmentally adaptive fish or no-fish classification for river video fish counters using high-performance desktop and embedded hardware // Ecological Informatics (2022) vol. 72, art. 101817, 14 p. : ill. https://doi.org/10.1016/j.ecoinf.2022.101817