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
this https URL

本文提出了一种利用元数据和相关无人机图像来学习领域鲁棒特征的对抗训练框架 —— 干扰解缠特征转换（NDFT）算法，以解决无人机图像中目标检测的具体挑战问题，实现了在两个现有基于无人机的目标检测基准测试上的最新性能。