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Simulation and evaluation of deep learning autoencoders for image compression in multi-UAV network systems

2023Conference paper Open access
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dc.contributor.authorRamos, Gabryel Silva
dc.contributor.authorLima, Amaro Azevedo de
dc.contributor.authorAlmeida, Luciana F.
dc.contributor.authorLima, José
dc.contributor.authorPinto, Milena F.
dc.date.accessioned2024-01-30T10:51:38Z
dc.date.available2024-01-30T10:51:38Z
dc.date.issued2023
dc.description.abstractMobile multi-robot systems are versatile alternatives for improving single-robot capacities in many applications, such as logistics, environmental monitoring, search and rescue, photogrammetry, etc. In this sense, this kind of system must have a reliable communication network between the vehicles, ensuring that information exchanged within the nodes has little losses. This work simulates and evaluates the use of autoencoders for image compression in a multi-UAV simulation with ROS and Gazebo for a generic surveillance application. The autoencoder model was developed with the Keras library, presenting good training and validation results, with training and validation accuracy of 70%, and a Peak Signal Noise Ratio (PSNR) of 40dB. The use of the CPU for the simulated UAVs for processing and sending compressed images through the network is 25% faster. The results showed that this compression methodology is a good choice for improving the system’s performance without losing too much information.pt_PT
dc.description.sponsorshipThe authors thank CEFET/RJ, UFF, UFRJ, and the Brazilian research agencies CAPES, CNPq, and FAPERJ. Besides, the authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationRamos, Gabryel Silva; Lima, Amaro Azevedo de; Almeida, Luciana F.; Lima, José; Pinto, Milena F. (2023). Simulation and evaluation of deep learning autoencoders for image compression in multi-UAV network systems. In 20th Latin American Robotics Symposium, 15th Brazilian Symposium on Robotics, and 14th Workshop of Robotics in Education (LARS/SBR/WRE). p. 41-46. ISBN 979-8-3503-1538-7pt_PT
dc.identifier.doi10.1109/LARS/SBR/WRE59448.2023.10332986pt_PT
dc.identifier.isbn979-8-3503-1538-7
dc.identifier.urihttp://hdl.handle.net/10198/29393
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationLA/P/0007/2021pt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectComponentpt_PT
dc.subjectFormattingpt_PT
dc.subjectStylept_PT
dc.subjectStylingpt_PT
dc.subjectInsertpt_PT
dc.titleSimulation and evaluation of deep learning autoencoders for image compression in multi-UAV network systemspt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.citation.endPage46pt_PT
oaire.citation.startPage41pt_PT
oaire.citation.title20th Latin American Robotics Symposium, 15th Brazilian Symposium on Robotics, and 14th Workshop of Robotics in Education (LARS/SBR/WRE)pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameLima
person.givenNameJosé
person.identifierR-000-8GD
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id55851941311
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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