Image-to-Image Translation-based Data Augmentation for Robust EV Charging Inlet Detection

TL;DR

This paper introduces a new dataset and a novel image-to-image translation-based data augmentation method to improve EV charging inlet detection, demonstrating enhanced detection performance and environment control capabilities.

Contribution

The paper presents the first EV charging inlet dataset and a new environment-guided image translation method for data augmentation in EV inlet detection.

Findings

The proposed method outperforms state-of-the-art detection techniques.

It effectively controls synthesized images' environments using guide vectors.

Augmented data improves detection robustness in varied conditions.

Abstract

This work addresses the task of electric vehicle (EV) charging inlet detection for autonomous EV charging robots. Recently, automated EV charging systems have received huge attention to improve users' experience and to efficiently utilize charging infrastructures and parking lots. However, most related works have focused on system design, robot control, planning, and manipulation. Towards robust EV charging inlet detection, we propose a new dataset (EVCI dataset) and a novel data augmentation method that is based on image-to-image translation where typical image-to-image translation methods synthesize a new image in a different domain given an image. To the best of our knowledge, the EVCI dataset is the first EV charging inlet dataset. For the data augmentation method, we focus on being able to control synthesized images' captured environments (e.g., time, lighting) in an intuitive way. To achieve this, we first propose the environment guide vector that humans can intuitively interpret. We then propose a novel image-to-image translation network that translates a given image towards the environment described by the vector. Accordingly, it aims to synthesize a new image that has the same content as the given image while looking like captured in the provided environment by the environment guide vector. Lastly, we train a detection method using the augmented dataset. Through experiments on the EVCI dataset, we demonstrate that the proposed method outperforms the state-of-the-art methods. We also show that the proposed method is able to control synthesized images using an image and environment guide vectors.