dc.creator | Jokić, Aleksandar | |
dc.creator | Đokić, Lazar | |
dc.creator | Petrović, Milica | |
dc.creator | Miljković, Zoran | |
dc.date.accessioned | 2023-02-09T09:52:30Z | |
dc.date.available | 2023-02-09T09:52:30Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-86-7466-894-8 | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/4233 | |
dc.description.abstract | In this paper, we present the novel mobile robot
perception system based on a deep learning framework. The
hardware subsystem consists of an Nvidia Jetson Nano
development board integrated with two parallelly positioned
Basler daA1600-60uc cameras, while the software subsystem is
based on the convolutional neural networks utilized for semantic
segmentation of the environment scene. A Fully Convolutional
neural Network (FCN) based on the ResNet18 backbone
architecture is utilized to provide accurate information about
machine tool models and background position in the image. FCN
model is trained on our custom-developed dataset of a laboratory
model of manufacturing environment and implemented on
mobile robot RAICO (Robot with Artificial Intelligence based
COgnition). | sr |
dc.language.iso | en | sr |
dc.publisher | Belgrade : Društvo za ETRAN | sr |
dc.publisher | Beograd : Akademska misao | sr |
dc.relation | info:eu-repo/grantAgreement/MESTD/inst-2020/200105/RS// | sr |
dc.relation | info:eu-repo/grantAgreement/ScienceFundRS/AI/6523109/RS// | sr |
dc.rights | openAccess | sr |
dc.source | Зборник радова ‐ 65. Конференција за електронику, телекомуникације, рачунарство, аутоматику и нуклеарну технику, Етно село Станишићи, 08‐10.09.2021. године / Proceedings of Papers – 8th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2021, Ethno willage Stanišići, Republic of Srpska, Bosnia and Herzegovina, 2021 | sr |
dc.subject | Deep learning | sr |
dc.subject | Perception System | sr |
dc.subject | Mobile robot | sr |
dc.subject | Semantic Segmentation | sr |
dc.title | A Mobile Robot Visual Perception System based on Deep Learning Approach | sr |
dc.type | conferenceObject | sr |
dc.rights.license | ARR | sr |
dc.citation.epage | 572 | |
dc.citation.rank | M33 | |
dc.citation.spage | 568 | |
dc.identifier.fulltext | http://machinery.mas.bg.ac.rs/bitstream/id/9992/114_ROI_1.3.pdf | |
dc.identifier.rcub | https://hdl.handle.net/21.15107/rcub_machinery_4233 | |
dc.type.version | publishedVersion | sr |