The use of identical robots in the RoboCup Standard Platform League (SPL) made software development the key aspect to achieve good results in competitions. In particular, the visual detection process is crucial for extracting information about the environment. In this paper, we present a novel approach for object detection and classification based on Convolutional Neural Networks (CNN). The approach is designed to be used by NAO robots and is made of two stages: image region segmentation, for reducing the search space, and Deep Learning, for validation. The proposed method can be easily extended to deal with different objects and adapted to be used in other RoboCup leagues. Quantitative experiments have been conducted on a data set of annotated images captured in real conditions from NAO robots in action. The used data set is made available for the community. © 2017, Springer International Publishing AG.

A Deep Learning Approach for Object Recognition with NAO Soccer Robots / Albani, Dario; Youssef, Ali; Suriani, Vincenzo; Nardi, Daniele; Bloisi, Domenico Daniele. - ELETTRONICO. - 9776:(2017), pp. 392-403. (Intervento presentato al convegno 20th Annual RoboCup International Symposium, 2016 tenutosi a Leipzig; Germany; 30 June 2016 through 4 July 2016; Code 203959) [10.1007/978-3-319-68792-6_33].

A Deep Learning Approach for Object Recognition with NAO Soccer Robots

Albani, Dario
;
Youssef, Ali;Suriani, Vincenzo;Nardi, Daniele;Bloisi, Domenico Daniele
2017

Abstract

The use of identical robots in the RoboCup Standard Platform League (SPL) made software development the key aspect to achieve good results in competitions. In particular, the visual detection process is crucial for extracting information about the environment. In this paper, we present a novel approach for object detection and classification based on Convolutional Neural Networks (CNN). The approach is designed to be used by NAO robots and is made of two stages: image region segmentation, for reducing the search space, and Deep Learning, for validation. The proposed method can be easily extended to deal with different objects and adapted to be used in other RoboCup leagues. Quantitative experiments have been conducted on a data set of annotated images captured in real conditions from NAO robots in action. The used data set is made available for the community. © 2017, Springer International Publishing AG.
2017
20th Annual RoboCup International Symposium, 2016
Robot vision; Deep Learning; RoboCup SPL; NAO robots
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A Deep Learning Approach for Object Recognition with NAO Soccer Robots / Albani, Dario; Youssef, Ali; Suriani, Vincenzo; Nardi, Daniele; Bloisi, Domenico Daniele. - ELETTRONICO. - 9776:(2017), pp. 392-403. (Intervento presentato al convegno 20th Annual RoboCup International Symposium, 2016 tenutosi a Leipzig; Germany; 30 June 2016 through 4 July 2016; Code 203959) [10.1007/978-3-319-68792-6_33].
File allegati a questo prodotto
File Dimensione Formato  
Albani_Postprint_A-Deep-Learning_2017.pdf

accesso aperto

Note: https://link.springer.com/chapter/10.1007/978-3-319-68792-6_33
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.57 MB
Formato Adobe PDF
2.57 MB Adobe PDF
Albani_A-Deep-Learning_2017.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.92 MB
Formato Adobe PDF
1.92 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/934382
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 34
  • ???jsp.display-item.citation.isi??? ND
social impact