Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/75255

TitleObject detection with RetinaNet on aerial imagery: the Algarve landscape
Author(s)Coelho, C.
Costa, M. Fernanda P.
Ferrás, Luís Jorge Lima
Soares, A. J.
KeywordsComputer vision
Neural networks
Deep learning
Object detection
RetinaNet
Issue date11-Sep-2021
PublisherSpringer
JournalLecture Notes in Computer Science
CitationCoelho C., Costa M.F.P., Ferrás L.L., Soares A.J. (2021) Object Detection with RetinaNet on Aerial Imagery: The Algarve Landscape. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science, vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_35
Abstract(s)This work presents a study of the different existing object detection algorithms and the implementation of a Deep Learning model capable of detecting swimming pools from satellite images. In order to obtain the best results for this particular task, the RetinaNet algorithm was chosen. The model was trained using a customised dataset from Kaggle and tested with a newly developed dataset containing aerial images of the Algarve landscape and a random dataset of images obtained from Google Maps. The performance of the trained model is discussed using several metrics. The model can be used by the authorities to detect illegal swimming pools in any region, especially in the Algarve region due to the high density of pools there.
TypeConference paper
URIhttps://hdl.handle.net/1822/75255
ISBN978-3-030-86959-5
e-ISBN978-3-030-86960-1
DOI10.1007/978-3-030-86960-1_35
ISSN0302-9743
Publisher versionhttps://link.springer.com/chapter/10.1007/978-3-030-86960-1_35
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

Files in This Item:
File Description SizeFormat 
Springer_Lecture_Notes_in_Computer_Science___ICCSA_2021___Cec_lia.pdf5,66 MBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID