Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with
  Weak Supervision
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
6888dca266e37b2a6b291f63
Xiao Fang
claimed_verified
2025-07-30T09:05:02.766Z
6697d30fbfec78c1bf6a4962
/avatars/17cfd7d7eb2b89b5051bd912e41e5a62.svg
Xiao Fang
user
xiaofanghf
6888dca266e37b2a6b291f64
Minhyek Jeon
claimed_verified
2025-07-31T08:16:05.128Z
65b53aa41e45f6ee8bc63e28
/avatars/09ee1059cbb3e48f4fb30d84e8c4e809.svg
Minhyek Jeon
user
Min0326
6888dca266e37b2a6b291f65
Zheyang Qin
6888dca266e37b2a6b291f66
Stanislav Panev
6888dca266e37b2a6b291f67
Celso de Melo
6888dca266e37b2a6b291f68
Shuowen Hu
6888dca266e37b2a6b291f69
Shayok Chakraborty
6888dca266e37b2a6b291f6a
Fernando De la Torre
Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with
  Weak Supervision
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
A multi-stage, multi-modal knowledge transfer framework using fine-tuned latent diffusion models improves vehicle detection in aerial imagery across different domains.
latent diffusion models data augmentation domain adaptation aerial imagery vehicle detection distribution gap AP50 weakly supervised adaptation unsupervised domain adaptation open-set object detectors