Multi-Scale Adversarial Cross-Domain Vehicle Detection from UAV to Satellite Imagery

Vehicle detection based on satellite imagery has extensive applications in the field of intelligent transportation. However, current algorithms generally rely on expensive data annotation, leading to increased cost burdens. To address this challenge, our paper proposes a domain-adaptive multi-scale adversarial cross-domain vehicle detection network, enabling the transfer of knowledge learned from annotated and easily accessible UAV imagery to the unlabeled satellite imagery domain. Specifically, our network, built upon the two-stage object detection framework, achieves domain alignment at the image-level by embedding a multi-scale information aggregation module between the source domain (UAV imagery) and the target domain (satellite imagery) to handle data differences at various scales effectively. To tackle the sample misalignment issue, an adversarial gradient reversal layer is designed to adversarially mine samples that are challenging to align. The integration of domain adaptation methods further enhances the performance of unsupervised cross-domain vehicle detection. Experimental results demonstrate a significant improvement in the VisDrone → UCAS-AOD cross-domain vehicle detection scenario with substantial domain scale differences. Compared to non-domain adaptation methods, our proposed approach achieves a notable 15.15% increase in AP.