AUTHOR=Chunnapiya Phasuwut , Visutsak Porawat 

TITLE=Enhancing automatic electric vehicle charging: a deep learning approach with YOLO and feature extraction techniques

JOURNAL=Frontiers in Computer Science

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1505446

DOI=10.3389/fcomp.2025.1505446

ISSN=2624-9898

ABSTRACT=This research addresses the challenge of automating electric vehicle (EV) charging in Thailand, where five distinct EV charging plug types are prevalent. We propose a deep learning approach using YOLO (You Only Look Once) to accurately identify these plug types, enabling robots to perform charging tasks efficiently. The study evaluates four YOLO versions (V5s, V6s, V7, and V8s) to determine the optimal model for this application. Our results demonstrate that YOLO V8s achieves the highest accuracy with a Mean Average Precision (mAP) of 0.95, while YOLO V7 exhibits superior performance in certain real-world scenarios. This research contributes to the development of automated EV charging systems by providing a robust and accurate model for detecting all five types of EV charging plugs used in Thailand. The model’s ability to accurately detect and classify EV charging plugs paves the way for the design of automated charging robots, addressing a key challenge in EV charging infrastructure and promoting the wider adoption of electric vehicles.