Electric Vehicle Charging Socket Detection using YOLOv8s Model
Tadic, Vladimir
Odry, Akos
Vizvari, Zoltan
Kiraly, Zoltan
Felde, Imre
Odry, Peter
2025-08-19T09:16:06Z
2025-08-19T09:16:06Z
2024
1785-8860
hu_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%.
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Electric Vehicle Charging Socket Detection using YOLOv8s Model
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Open access
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Óbudai Egyetem
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Budapest
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Óbudai Egyetem
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