  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.
