Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/jspui/handle/123456789/15311
Title: Combined weightless neural network FPGA architecture for deforestation surveillance and visual navigation of UAVs.
Authors: Torres, Vitor Angelo Maria Ferreira
Jaimes, Brayan Rene Acevedo
Ribeiro, Eduardo da Silva
Braga, Mateus Taulois
Shiguemori, Elcio Hideit
Velho, Haroldo Fraga de Campos
Torres, Luiz Carlos Bambirra
Braga, Antônio de Pádua
Keywords: Classification
Artificial neural networks
Issue Date: 2020
Citation: TORRES, V. A. M. F. et al. Combined weightless neural network FPGA architecture for deforestation surveillance and visual navigation of UAVs. Engineering Applications of Artificial Intelligence, v. 87, artigo 103227, 2020. Disponível em: <https://www.sciencedirect.com/science/article/pii/S095219761930212X>. Acesso em: 29 abr. 2022.
Abstract: This work presents a combined weightless neural network architecture for deforestation surveillance and visual navigation of Unmanned Aerial Vehicles (UAVs). Binary images, which are required for position estimation and UAV navigation, are provided by the deforestation surveillance circuit. Learned models are evaluated in a real UAV flight over a green countryside area, while deforestation surveillance is assessed with an Amazon forest benchmarking image data. Small utilization percentage of Field Programmable Gate Arrays (FPGAs) allows for a higher degree of parallelization and block processing of larger regions of input images.
URI: http://www.repositorio.ufop.br/jspui/handle/123456789/15311
metadata.dc.identifier.uri2: https://www.sciencedirect.com/science/article/pii/S095219761930212X
metadata.dc.identifier.doi: https://doi.org/10.1016/j.engappai.2019.08.021
ISSN: 0952-1976
Appears in Collections:DECSI - Artigos publicados em periódicos

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