Fully neural object detection solutions for robot soccer

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• dc.contributor.author Szemenyei, Márton
• dc.contributor.author Estivill-Castro, V. (Vladimir)
• dc.date.accessioned 2021-05-19T07:15:24Z
• dc.date.available 2021-05-19T07:15:24Z
• dc.date.issued 2022
• dc.description.abstract RoboCup is one of the major global AI events, gathering hundreds of teams from the world’s best universities to compete in various tasks ranging from soccer to home assistance and rescue. The commonality of these three seemingly dissimilar tasks is that in order to perform well, the robot needs to excel at the all major AI tasks: perception, control, navigation, strategy and planning. In this work, we focus on the first of these by presenting what is—to our knowledge—the first fully neural vision system for the Nao robot soccer. This is a challenging task, mainly due to the limited computational capabilities of the Nao robot. In this paper, we propose two novel neural network architectures for semantic segmentation and object detection that ensure low-cost inference, while improving accuracy by exploiting the properties of the environment. These models use synthetic transfer learning to be able to learn from a low number of hand-labeled images. The experiments show that our models outperform state-of-the-art methods such as Tiny YOLO at a fraction of the cost.
• dc.description.sponsorship The research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program, and the National Research, Development and Innovation Fund (TUDFO/51757/2019-ITM, Thematic Excellence Program)
• dc.format.mimetype application/pdf
• dc.identifier.citation Szemenyei M, Estivill-Castro V. Fully neural object detection solutions for robot soccer. Neural Comput Appl. 2022;34(24):21419-32. DOI: 10.1007/s00521-021-05972-1
• dc.identifier.doi http://dx.doi.org/10.1007/s00521-021-05972-1
• dc.identifier.issn 0941-0643
• dc.identifier.uri http://hdl.handle.net/10230/47590
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Neural Computing and Applications. 2022;34(24):21419-32
• dc.rights This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Robot soccer
• dc.subject.keyword Neural networks
• dc.subject.keyword Computer vision
• dc.subject.keyword Sim2real
• dc.title Fully neural object detection solutions for robot soccer
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion