Automatic target recognition (ATR) technology is likely to play an increasingly prevalent role in maintaining situational awareness in the modern battlefield. Progress in deep learning has enabled considerable progress in the development of ATR algorithms; however, these algorithms require large amounts of high-quality annotated data to train and that is often the main bottleneck. Synthetic data offers a potential solution to this problem, especially given recent proliferation of tools and techniques to synthesize custom data. Here, we focus on ATR, in the visible domain, from the perspective of a small drone, which represents a domain of growing importance to the Army. We describe custom simulators built to support synthetic data for multiple targets in a variety of environments. We describe a field experiment where we compared a baseline (YOLOv5) model, trained on off-the-shelf large generic public datasets, with a model augmented with specialized synthetic data. We deployed the models on a VOXL platform in a small drone. Our results showed a considerable boost in performance when using synthetic data of over 40% in target detection accuracy (average precision with at least 50% overlap). We discuss the value of synthetic data for this domain, the opportunities it creates, but also the novel challenges it introduces.
We discuss our efforts with event-based vision and describe our large-scale, heterogeneous robotic dataset that will add to the growing number of event-based datasets currently publicly available. Our dataset comprises over 10 hours of runtime from a mobile robot equipped with two DAVIS240C cameras and an Astra depth camera randomly wandering in an indoor environment while two other independently moving robots randomly wander in the same scene. Vicon ground truth pose is provided for all three robots. To our knowledge, this is the largest event-based dataset with ground truthed independently moving entities.
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