Visual Foreign Object Detection for Wireless Charging of Electric Vehicles

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 (similar to 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.