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Visual Foreign Object Detection for Wireless Charging of Electric Vehicles

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Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14362))

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Abstract

Wireless charging of electric vehicles can be achieved by installing a transmitter coil into the ground and a receiver coil at the underbody of a vehicle. In order to charge efficiently, accurate alignment of the charging components must be accomplished, which can be achieved with a camera-based positioning system. Due to an air gap between both charging components, foreign objects can interfere with the charging process and pose potential hazards to the environment. Various foreign object detection systems have been developed with the motivation to increase the safety of wireless charging. In this paper, we propose an object-type independent foreign object detection technique which utilizes the existing camera of an embedded positioning system. To evaluate our approach, we conduct two experiments by analyzing images from a dataset of a wireless charging surface and from a publicly available dataset depicting foreign objects in an airport environment. Our technique outperforms two background subtraction algorithms and reaches accuracy scores that are comparable to the accuracy achieved by a state-of-the-art neural network (97%). While acknowledging the superior accuracy results of the neural network, we observe that our approach requires significantly less resources, which makes it more suitable for embedded devices. The dataset of the first experiment is published alongside this paper and consists of 3652 labeled images recorded by a positioning camera of an operating wireless charging station in an outdoor environment.

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Acknowledgment

This research is funded by the Bundesministerium für Wirtschaft und Energie as part of the TALAKO project [14] (grant number 01MZ19002A).

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Authors and Affiliations

  1. University of Duisburg-Essen, Essen, Germany

    Bijan Shahbaz Nejad, Peter Roch, Marcus Handte & Pedro José Marrón

Authors
  1. Bijan Shahbaz Nejad
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  2. Peter Roch
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  3. Marcus Handte
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  4. Pedro José Marrón
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Corresponding author

Correspondence to Bijan Shahbaz Nejad .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Google Research, Mountain View, CA, USA

    Golnaz Ghiasi

  3. New York University, New York, USA

    Yi Fang

  4. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

  5. Microsoft Research, Beijing, China

    Yue Dong

  6. The University of Oklahoma, Norman, OK, USA

    Chris Weaver

  7. University of Maryland, Collage Park, MD, USA

    Zhicheng Leo

  8. University of Central Florida, Orlando, FL, USA

    Joseph J. LaViola Jr.

  9. InnerOptic Technology, Hillsborough, NC, USA

    Luv Kohli

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Shahbaz Nejad, B., Roch, P., Handte, M., Marrón, P.J. (2023). Visual Foreign Object Detection for Wireless Charging of Electric Vehicles. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14362. Springer, Cham. https://doi.org/10.1007/978-3-031-47966-3_15

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  • DOI: https://doi.org/10.1007/978-3-031-47966-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47965-6

  • Online ISBN: 978-3-031-47966-3

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