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DEEPFIELD SIGNED

DeepField- Deep Learning in Field Robotics: from conceptualization towards implementation

Total Cost €

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EC-Contrib. €

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Partnership

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Project "DEEPFIELD" data sheet

The following table provides information about the project.

Coordinator
INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA 

Organization address
address: RUA DR ROBERTO FRIAS CAMPUS DA FEUP
city: PORTO
postcode: 4200 465
website: www.inescporto.pt

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Portugal [PT]
 Total cost 799˙975 €
 EC max contribution 799˙975 € (100%)
 Programme 1. H2020-EU.4.b. (Twinning of research institutions)
 Code Call H2020-WIDESPREAD-2018-03
 Funding Scheme CSA
 Starting year 2019
 Duration (year-month-day) from 2019-10-01   to  2022-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA PT (PORTO) coordinator 287˙537.00
2    HERIOT-WATT UNIVERSITY UK (EDINBURGH) participant 130˙312.00
3    MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV DE (MUENCHEN) participant 130˙000.00
4    POLITECNICO DI MILANO IT (MILANO) participant 129˙250.00
5    UNIVERSITAT DE GIRONA ES (GIRONA) participant 122˙875.00

Map

 Project objective

Robots are active agents that need to interact with the physical world, to do so, robots are equipped with different sensors, whose data is used to build models that ultimately will allow robots to plan actions and make decisions. Currently, there is strong focus in developing deep learning strategies “data driven” to help solve this perception problem, even though these approaches work well in dataset and benchmark scenarios. There are still strong limitations in the use of this techniques in real world robot activities, specially due to the strong dynamics in robots operational environment, that is pushing the development of new tools and methods to make these approaches feasible in the real world. INESC TEC is strongly committed to become a centre of excellence with focus on field robotics, in particular, in the aerial and underwater robotics domain. In the last years, the centre for Robotics and Autonomous systems, of INESC TEC has advance its scientific knowledge in sensing and perception methods for robots navigation and localization in harsh operational environments. The key objective of INESC TEC is to become one of the European references in field robotics, and help to bring robot technology to solve real life problems where human intervention is still limited or non-existent. This proposal aims at creating solid knowledge and productive links in the global field of deep learning in field robotics between INESC TEC and established leading research European institutions, capable of enhancing the scientific and technological capacity of INESC TEC and linked institutions (as well as the capacity of partnering institutions involved in the twinning action), helping raising its staff’s research profile and its recognition as an European research centre of excellence in field robotics. In particular, it takes INESC TEC and places it as the pivot of a network of excellence, involving four international leaders in deep learning technology and fied robotics.

 Publications

year authors and title journal last update
List of publications.
2019 Sara Freitas, Hugo Silva, José Miguel Almeida, Eduardo Silva
Convolutional neural network target detection in hyperspectral imaging for maritime surveillance
published pages: 172988141984299, ISSN: 1729-8814, DOI: 10.1177/1729881419842991
International Journal of Advanced Robotic Systems 16/3 2019-11-28
2019 Bernardo Teixeira
\"\"\"Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation\"\"\"
published pages: , ISSN: , DOI:
2019-11-28
2019 António Pedro Oliva Afonso
A comparative study of machine learning techniques for underwater visual object recognition
published pages: , ISSN: , DOI:
2019-11-28

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The information about "DEEPFIELD" are provided by the European Opendata Portal: CORDIS opendata.

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