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Electric Vehicle Charging Socket Detection using YOLOv8s Model

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URI
http://hdl.handle.net/20.500.14044/32431
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  • Acta Polytechnica Hungarica [175]
Abstract
This paper introduces the utilization of the latest small You Only Look Once version 8 – YOLOv8s convolutional neural network in an automatic electric vehicle charging application study. The employment of a deep learning-based object detector is a novel and significant aspect in robotic applications, since it is both, the initial and the fundamental step in a series of robotic operations, where the intent is to detect and locate the charging socket on the vehicle’s body surface. The aim was to use a renowned and reliable object detector to ensure the reliable and smooth functioning of the deployed robotic vision system in an industrial environment. The experiments demonstrated that the deployed YOLOv8s model detects the charging socket successfully under various image capturing conditions, with a detection rate of 97.23%.
Title
Electric Vehicle Charging Socket Detection using YOLOv8s Model
Author
Tadic, Vladimir
Odry, Akos
Vizvari, Zoltan
Kiraly, Zoltan
Felde, Imre
Odry, Peter
xmlui.dri2xhtml.METS-1.0.item-date-issued
2024
xmlui.dri2xhtml.METS-1.0.item-rights-access
Open access
xmlui.dri2xhtml.METS-1.0.item-identifier-issn
1785-8860
xmlui.dri2xhtml.METS-1.0.item-language
en
xmlui.dri2xhtml.METS-1.0.item-format-page
19 p.
xmlui.dri2xhtml.METS-1.0.item-subject-oszkar
yolov8s, electric vehicle charging socket, image processing, object detection, robotic applications, automotive applications
xmlui.dri2xhtml.METS-1.0.item-description-version
Kiadói változat
xmlui.dri2xhtml.METS-1.0.item-identifiers
DOI: 10.12700/APH.21.10.2024.10.8
xmlui.dri2xhtml.METS-1.0.item-other-containerTitle
Acta Polytechnica Hungarica
xmlui.dri2xhtml.METS-1.0.item-other-containerPeriodicalYear
2024
xmlui.dri2xhtml.METS-1.0.item-other-containerPeriodicalVolume
21. évf.
xmlui.dri2xhtml.METS-1.0.item-other-containerPeriodicalNumber
10. sz.
xmlui.dri2xhtml.METS-1.0.item-type-type
Tudományos cikk
xmlui.dri2xhtml.METS-1.0.item-subject-area
Műszaki tudományok - közlekedés- és járműtudományok
xmlui.dri2xhtml.METS-1.0.item-publisher-university
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