Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/75255
Título: | Object detection with RetinaNet on aerial imagery: the Algarve landscape |
Autor(es): | Coelho, C. Costa, M. Fernanda P. Ferrás, Luís Jorge Lima Soares, A. J. |
Palavras-chave: | Computer vision Neural networks Deep learning Object detection RetinaNet |
Data: | 11-Set-2021 |
Editora: | Springer |
Revista: | Lecture Notes in Computer Science |
Citação: | Coelho 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 |
Resumo(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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/75255 |
ISBN: | 978-3-030-86959-5 |
e-ISBN: | 978-3-030-86960-1 |
DOI: | 10.1007/978-3-030-86960-1_35 |
ISSN: | 0302-9743 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-86960-1_35 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
Springer_Lecture_Notes_in_Computer_Science___ICCSA_2021___Cec_lia.pdf | 5,66 MB | Adobe PDF | Ver/Abrir |