  Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is
becoming increasingly useful. Despite the great success of the generic object
detection methods trained on ground-to-ground images, a huge performance drop
is observed when they are directly applied to images captured by UAVs. The
unsatisfactory performance is owing to many UAV-specific nuisances, such as
varying flying altitudes, adverse weather conditions, dynamically changing
viewing angles, etc. Those nuisances constitute a large number of fine-grained
domains, across which the detection model has to stay robust. Fortunately, UAVs
will record meta-data that depict those varying attributes, which are either
freely available along with the UAV images, or can be easily obtained. We
propose to utilize those free meta-data in conjunction with associated UAV
images to learn domain-robust features via an adversarial training framework
dubbed Nuisance Disentangled Feature Transform (NDFT), for the specific
challenging problem of object detection in UAV images, achieving a substantial
gain in robustness to those nuisances. We demonstrate the effectiveness of our
proposed algorithm, by showing state-of-the-art performance (single model) on
two existing UAV-based object detection benchmarks. The code is available at
https://github.com/TAMU-VITA/UAV-NDFT.
