  We present an algorithm to detect unseen road debris using a small set of
synthetic models. Early detection of road debris is critical for safe
autonomous or assisted driving, yet the development of a robust road debris
detection model has not been widely discussed. There are two main challenges to
building a road debris detector: first, data collection of road debris is
challenging since hazardous objects on the road are rare to encounter in real
driving scenarios; second, the variability of road debris is broad, ranging
from a very small brick to a large fallen tree. To overcome these challenges,
we propose a novel approach to few-shot learning of road debris that uses
semantic augmentation and domain randomization to augment real road images with
synthetic models. We constrain the problem domain to uncommon objects on the
road and allow the deep neural network, HazardNet, to learn the semantic
meaning of road debris to eventually detect unseen road debris. Our results
demonstrate that HazardNet is able to accurately detect real road debris when
only trained on synthetic objects in augmented images.
