Detalhes
Nome
Vitor Manuel FilipeCluster
InformáticaCargo
Investigador CoordenadorDesde
01 outubro 2012
Nacionalidade
PortugalCentro
Centro de Sistemas de Informação e de Computação GráficaContactos
+351222094199
vitor.m.filipe@inesctec.pt
2020
Autores
Fernandes, H; Costa, P; Filipe, V; Paredes, H; Barroso, J;
Publicação
Universal Access in the Information Society
Abstract
The fourth author name was missed in the original publication. The correct list of authors should read as “Hugo Fernandes, Paulo Costa, Vitor Filipe, Hugo Paredes, João Barroso”. It has been corrected in this erratum. The original article has been updated. © 2017 Springer-Verlag GmbH Germany
2020
Autores
Alves, JP; Fonseca Ferreira, NMF; Valente, A; Soares, S; Filipe, V;
Publicação
Robotics in Education - Advances in Intelligent Systems and Computing
Abstract
2020
Autores
Safadinho, D; Ramos, J; Ribeiro, R; Filipe, V; Barroso, J; Pereira, A;
Publicação
SENSORS
Abstract
The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed-without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5-10 m, with recalls from 59%-76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.
2020
Autores
Pinto de Aguiar, ASP; Neves dos Santos, FBN; Feliz dos Santos, LCF; de Jesus Filipe, VMD; Miranda de Sousa, AJM;
Publicação
Computers and Electronics in Agriculture
Abstract
2020
Autores
Crisostomo, L; Fonseca Ferreira, NMF; Filipe, V;
Publicação
International Journal of Advanced Robotic Systems
Abstract
Teses supervisionadas
2019
Autor
Salik Ram Khanal
Instituição
UTAD
2018
Autor
Leonel Agostinho Costa Crisóstomo
Instituição
UTAD
2018
Autor
Jorge Manuel Pereira Duque
Instituição
UTAD
2018
Autor
Salik Ram Khanal
Instituição
UTAD
2017
Autor
Leonel Agostinho Costa Crisóstomo
Instituição
UTAD
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