Image-based Foreign Object Detection using YOLO v7 Algorithm for Electric Vehicle Wireless Charging Applications

Abstract

As problems like climate change, greenhouse emissions, etc. become common, Electric Vehicles (EVs) are now being looked at as an alternative. In order for EVs to succeed, numerous charging networks must be established in a user-friendly environment and the optimum charging solution must be selected. Wireless Power Transfer (WPT) System is an option that simplifies the battery charging process for EVs as it eliminates the need for annoying cables and ensures the user's safety. Inductive power transfer is currently the most popular and mature WPT technology, which uses magnetic fields for power transfer. In order to comply with transmission and safety criteria, Foreign Object Detection (FOD) is an essential module that has to be taken care of. Deep Learning (DL) has been successfully applied to various applications by using algorithms such as CNNs, You Only Look Once (YOLO), etc. In this work, a unique approach has been taken toward FOD by using DL, particularly the YOLO v7 algorithm. The pre-trained YOLO v7 algorithm has been further customized to detect (a) coins (b) screws and (c) paper clips as part of FOD and towards the end, Mean Average Precision of 97%, 93.1% precision, and 95.3% recall values have been achieved.