UAV Detection with a dataset augmented by domain randomization

Abstract

Object detection for computer vision systems continues to be a complicated problem in real-world situations. For instance, autonomous vehicles need to operate with very small margins of error as they encounter safety-critical scenarios such as pedestrian and vehicle detection. The increased use of unmanned aerial vehicles (UAVs) by both government and private citizens has created a need for systems which can reliably detect UAVs in a large variety of conditions and environments. In order to achieve small margins of error, object detection systems, especially those reliant on deep learning methods, require large amounts of annotated data. The use of synthetic datasets provides a way to alleviate the need to collect annotated data. Unfortunately, the nature of synthetic dataset generation introduces a reality and simulation gap that hinders an object detector's ability to generalize on real world data. Domain randomization is a technique that generates a variety of different scenarios in a randomized fashion both to close the reality and simulation gap and to augment a hand-crafted dataset. In this paper, we combine the AirSim simulation environment with domain randomization to train a robust object detector. As a final step, we fine-tune our object detector on real-world data and compare it with object detectors trained solely on real-world data.