Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with
  Weak Supervision
Authors: name: Xiao Fang, name: Minhyek Jeon, name: Zheyang Qin, name: Stanislav Panev, name: Celso de Melo, name: Shuowen Hu, name: Shayok Chakraborty, name: Fernando De la Torre
Organization: Carnegie Mellon University

Detecting vehicles in aerial imagery is a critical task with applications in
traffic monitoring, urban planning, and defense intelligence. Deep learning
methods have provided state-of-the-art (SOTA) results for this application.
However, a significant challenge arises when models trained on data from one
geographic region fail to generalize effectively to other areas. Variability in
factors such as environmental conditions, urban layouts, road networks, vehicle
types, and image acquisition parameters (e.g., resolution, lighting, and angle)
leads to domain shifts that degrade model performance. This paper proposes a
novel method that uses generative AI to synthesize high-quality aerial images
and their labels, improving detector training through data augmentation. Our
key contribution is the development of a multi-stage, multi-modal knowledge
transfer framework utilizing fine-tuned latent diffusion models (LDMs) to
mitigate the distribution gap between the source and target environments.
Extensive experiments across diverse aerial imagery domains show consistent
performance improvements in AP50 over supervised learning on source domain
data, weakly supervised adaptation methods, unsupervised domain adaptation
methods, and open-set object detectors by 4-23%, 6-10%, 7-40%, and more than
50%, respectively. Furthermore, we introduce two newly annotated aerial
datasets from New Zealand and Utah to support further research in this field.
Project page is available at: https://humansensinglab.github.io/AGenDA