Abstract
Automatic analysis of aerial imagery acquired by satellites, planes and UAVs facilitates several applications such as traffic monitoring, surveillance, search and rescue tasks. These applications have in common the need for an accurate object detection. In recent years, applying Faster R-CNN, a deep learning based detection method, outperformed conventional detection methods for the task of vehicle detection in aerial imagery. For this, adaptations to the characteristics of aerial imagery are necessary. In this paper, we adapt several state-of-the-art detectors including Faster R-CNN, SSD, and YOLOv2 and analyze the detection performance with respect to object categories, ground sampling distances and inference time. Furthermore, we examine the impact of adding more semantic information for each detector separately. For that purpose, we propose an extension of YOLOv2 by adding a deconvolutional module. We achieve state-of-the-art results on the publicly available DOTA dataset.