CG-based dataset generation and adversarial image conversion for deep cucumber recognition

This paper deals with deep cucumber recognition using CG (Computer Graphics)-based dataset generation. The variety and the size of the dataset are crucial in deep learning. Although there are many public datasets for common situations like traffic scenes, we need to make a dataset for a particular scene like cucumber farms. As it is costly and time-consuming to annotate much data manually, we proposed generating images by CG and converting them to realistic ones using adversarial learning approaches. We compare several image conversion methods using real cucumber plant images.