Vehicle Occlusion Removal from Single Aerial Images Using Generative Adversarial Networks

Removing occluding objects such as vehicles from drivable areas allows precise extraction of road boundaries and related semantic objects such as lane-markings, which is crucial for several applications such as generating high-definition maps for autonomous driving. Conventionally, multiple images of the same area taken at different times or from various perspectives are used to remove occlusions and to reconstruct the occluded areas. Nevertheless, these approaches require large amounts of data, which are not always available. Furthermore, they do not work for static occlusions caused by, among others, parked vehicles. In this paper, we address occlusion removal based on single aerial images using generative adversarial networks (GANs), which are able to deal with the mentioned challenges. To this end, we adapt several state-of-the-art GAN-based image inpainting algorithms to reconstruct the missing information. Results indicate that the StructureFlow algorithm outperforms the competitors and the restorations obtained are robust, with high visual fidelity in real-world applications. Furthermore, due to the lack of annotated aerial vehicle removal datasets, we generate a new dataset for training and validating the algorithms, the Aerial Vehicle Occlusion Removal (AVOR) dataset. To the best of our knowledge, our work is the first to address vehicle removal using deep learning algorithms to enhance maps