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Sobre

Sobre

Nasci no Porto em 1979. Completei os meus estudos de base (Licenciatura) em Engenharia Electrotecnica e de Computadores no ISEP em 2004. Em 2014 completei o Doutoramento em Electronica e Computadores no Instituto Superior Técnico. Actualmente exerco as funções de investigador sénior no INESC TEC, onde trabalho na área da robótica móvel em projectos de âmbito nacional e internacional. Sou autor e revisor de várias publicações com arbitragem cientifica no dominio da visão por computador para aplicações de robótica móvel.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Hugo Miguel Silva
  • Cargo

    Investigador Sénior
  • Desde

    03 outubro 2011
018
Publicações

2023

The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios

Autores
Riz L.; Caraffa A.; Bortolon M.; Mekhalfi M.L.; Boscaini D.; Moura A.; Antunes J.; Dias A.; Silva H.; Leonidou A.; Constantinides C.; Keleshis C.; Abate D.; Poiesi F.;

Publicação
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Abstract
We present MONET, a new multimodal dataset captured using a thermal camera mounted on a drone that flew over rural areas, and recorded human and vehicle activities. We captured MONET to study the problem of object localisation and behaviour understanding of targets undergoing large-scale variations and being recorded from different and moving viewpoints. Target activities occur in two different land sites, each with unique scene structures and cluttered backgrounds. MONET consists of approximately 53K images featuring 162K manually annotated bounding boxes. Each image is timestamp-aligned with drone metadata that includes information about attitudes, speed, altitude, and GPS coordinates. MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata. We assessed the difficulty of the dataset in terms of transfer learning between the two sites and evaluated nine object detection algorithms to identify the open challenges associated with this type of data. Project page: https://github.com/fabiopoiesi/monet-dataset.

2022

Hyperspectral Imaging Zero-Shot Learning for Remote Marine Litter Detection and Classification

Autores
Freitas, S; Silva, H; Silva, E;

Publicação
REMOTE SENSING

Abstract
This paper addresses the development of a novel zero-shot learning method for remote marine litter hyperspectral imaging data classification. The work consisted of using an airborne acquired marine litter hyperspectral imaging dataset that contains data about different plastic targets and other materials and assessing the viability of detecting and classifying plastic materials without knowing their exact spectral response in an unsupervised manner. The classification of the marine litter samples was divided into known and unknown classes, i.e., classes that were hidden from the dataset during the training phase. The obtained results show a marine litter automated detection for all the classes, including (in the worst case of an unknown class) a precision rate over 56% and an overall accuracy of 98.71%.

2022

Feedfirst: Intelligent monitoring system for indoor aquaculture tanks

Autores
Teixeira, B; Lima, AP; Pinho, C; Viegas, D; Dias, N; Silva, H; Almeida, J;

Publicação
2022 OCEANS HAMPTON ROADS

Abstract
The Feedfirst Intelligent Monitoring System is a novel tool for intelligent monitoring of fish nurseries in aquaculture scenarios, mainly focusing on monitoring three essential items: water quality control, biomass estimation, and automated feeding. The system is based on machine vision techniques for fish larvae population size detection, and larvae biomass estimation is monitored through size measurement. We also show that the perception-actuation loop in automated fish tanks can be closed by using the vision system output to influence feeding procedures. The proposed solution was tested in a real tank in an aquaculture setting with real-time performance and logging capabilities.

2021

Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection

Autores
Freitas, S; Silva, H; Silva, E;

Publicação
REMOTE SENSING

Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70-80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.

2021

Deep learning point cloud odometry: Existing approaches and open challenges

Autores
Teixeira, B; Silva, H;

Publicação
U.Porto Journal of Engineering

Abstract
Achieving persistent and reliable autonomy for mobile robots in challenging field mission scenarios is a long-time quest for the Robotics research community. Deep learning-based LIDAR odometry is attracting increasing research interest as a technological solution for the robot navigation problem and showing great potential for the task. In this work, an examination of the benefits of leveraging learning-based encoding representations of real-world data is provided. In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-world applications is the focus of a deeper analysis and comprehensive comparison. Furthermore, existing Deep Learning approaches and techniques for point cloud odometry tasks are explored, and the main technological solutions are compared and discussed. Open challenges are also laid out for the reader, hopefully offering guidance to future researchers in their quest to apply deep learning to complex 3D non-matrix data to tackle localization and robot navigation problems.

Teses
supervisionadas

2022

Adopting Auction-Based Task Allocation Towards Decentralized Orchestration in Mist IoT

Autor
David Luís Dias da Silva

Instituição
UP-FEUP

2020

Promoção social com recurso a meios audiovisuais

Autor
Paulo Jorge Nóbrega de Carvalho

Instituição

2016

Metodologias Lean aplicadas a um Serviço de Apoio Domiciliário - Linha de Produção do Serviço de Alimentação

Autor
Orlando José Monteiro Marques Pinto

Instituição
UP-FEUP