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A convolutional neural network for detecting sea turtles in drone imagery
ArticleAbstract: Marine megafauna are difficult to observe and count because many species travel widely and spend larPalabras claves:convolutional neural networks, deep learning for ecology, marine megafauna, marine population monitoring, object detection, sea turtles, unoccupied aircraft systemsAutores:Bézy V.S., Fleishman A.B., Gray P.C., Johnston D.W., Kenneth J. Lohmann, Klein D.J., McKown M.W.Fuentes:scopusErratum: Author Correction: Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments (Scientific reports (2017) 7 1 (17690))
OtherAbstract: A correction to this article has been published and is linked from the HTML and PDF versions of thisPalabras claves:Autores:Bézy V.S., Johnston D.W., Kenneth J. Lohmann, Newton E., Sykora-Bodie S.T.Fuentes:scopusMass-nesting events in olive ridley sea turtles: environmental pbkp_redictors of timing and size
ArticleAbstract: During the mass nesting of olive ridley sea turtles, Lepidochelys olivacea, thousands of adult femalPalabras claves:arribada, LEPIDOCHELYS OLIVACEA, MIGRATION, model averaging, Ostional, survival analysisAutores:Bézy V.S., Fonseca L.G., Kenneth J. Lohmann, Orrego C.M., Putman N.F., Quirós-Pereira W.M., Umbanhowar J.A., Valverde R.A.Fuentes:scopusQuantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments
ArticleAbstract: Although sea turtles face significant pressure from human activities, some populations are recoverinPalabras claves:Autores:Bézy V.S., Johnston D.W., Kenneth J. Lohmann, Newton E., Sykora-Bodie S.T.Fuentes:scopus