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Open AccessArticle
Foreign Object Debris Detection on Wireless Electric Vehicle Charging Pad Using Machine Learning Approach
by
Narayanamoorthi Rajamanickam
Narayanamoorthi Rajamanickam 1,*,
Dominic Savio Abraham
Dominic Savio Abraham 1,
Roobaea Alroobaea
Roobaea Alroobaea 2 and
Waleed Mohammed Abdelfattah
Waleed Mohammed Abdelfattah 3,*
1
Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603 203, India
2
Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
3
College of Engineering, University of Business and Technology, Jeddah 23435, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Processes 2024, 12(8), 1574; https://doi.org/10.3390/pr12081574 (registering DOI)
Submission received: 19 June 2024
/
Revised: 24 July 2024
/
Accepted: 25 July 2024
/
Published: 27 July 2024
Abstract
Foreign object debris (FOD) includes any unwanted and unintentional material lying on the charging lane or parking lots, posing a risk to the wireless charging system, the vehicle, or the people inside. FOD in an Electric Vehicle (EV) wireless charging system can cause problems, including decreased charging efficiency, safety risks, charging system damage, communication issues, and health risks. To address this problem, this paper proposes the deep learning object detection network approach of using YOLOv4 (You Only Look Once), which is a single-shot detector. Additionally, for real-time implementation, YOLOv4-Tiny is suggested, which is a compressed version of YOLOv4 designed for devices with low computational power. YOLOv4-Tiny enables faster inferences and facilitates the deployment of FOD detectors on edge devices. The algorithm is trained using the FOD dataset, consisting of images of common debris on runways or taxiways. Furthermore, utilizing the concept of transfer learning, the last few layers of the pre-trained YOLOv4 model are modified using the COCO (Common Objects in Context) dataset to transfer features to the new network and retrain the model on the FOD dataset. The results obtained using this YOLOv4 model yielded a precision rate of 99.05%, while the results from YOLOv4-Tiny achieved a precision rate of 97.74%, with an average inference time of 150 ms under the ambient light and weather conditions.
Share and Cite
MDPI and ACS Style
Rajamanickam, N.; Abraham, D.S.; Alroobaea, R.; Abdelfattah, W.M.
Foreign Object Debris Detection on Wireless Electric Vehicle Charging Pad Using Machine Learning Approach. Processes 2024, 12, 1574.
https://doi.org/10.3390/pr12081574
AMA Style
Rajamanickam N, Abraham DS, Alroobaea R, Abdelfattah WM.
Foreign Object Debris Detection on Wireless Electric Vehicle Charging Pad Using Machine Learning Approach. Processes. 2024; 12(8):1574.
https://doi.org/10.3390/pr12081574
Chicago/Turabian Style
Rajamanickam, Narayanamoorthi, Dominic Savio Abraham, Roobaea Alroobaea, and Waleed Mohammed Abdelfattah.
2024. "Foreign Object Debris Detection on Wireless Electric Vehicle Charging Pad Using Machine Learning Approach" Processes 12, no. 8: 1574.
https://doi.org/10.3390/pr12081574
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