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Tadic, Vladimir
Odry, Akos
Vizvari, Zoltan
Kiraly, Zoltan
Felde, Imre
Odry, Peter
2025-08-19T09:16:06Z
2025-08-19T09:16:06Z
2024
1785-8860hu_HU
http://hdl.handle.net/20.500.14044/32431
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%.hu_HU
dc.formatPDFhu_HU
enhu_HU
Electric Vehicle Charging Socket Detection using YOLOv8s Modelhu_HU
Open accesshu_HU
Óbudai Egyetemhu_HU
Budapesthu_HU
Óbudai Egyetemhu_HU
Műszaki tudományok - közlekedés- és járműtudományokhu_HU
yolov8shu_HU
electric vehicle charging sockethu_HU
image processinghu_HU
object detectionhu_HU
robotic applicationshu_HU
automotive applicationshu_HU
Tudományos cikkhu_HU
Acta Polytechnica Hungaricahu_HU
local.tempfieldCollectionsFolyóiratcikkekhu_HU
10.12700/APH.21.10.2024.10.8
Kiadói változathu_HU
19 p.hu_HU
10. sz.hu_HU
21. évf.hu_HU
2024hu_HU
Óbudai Egyetemhu_HU


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