Efficient Aerial Image Object Detection with Imaging Condition Decomposition

Object detection in aerial images faces domain adaptive challenges, such as changes in shooting height, viewing angle, and weather. These changes constitute a large number of fine-grained domains that place greater demands on network's generalizability. To tackle these challenges, we propose a submodule named Fine-grained Feature Disentanglement which decomposes image features into domain-invariant and domain-specific using practical imaging condition parameters. The composite feature can improve the domain generalization and single domain accuracy compared to the conventional fine-grained domain detection method. The proposed algorithm is compared with state-of-the-art fine-grained domain detectors on the UAVDT and VisDrone datasets. The results show that it achieves an average detection precision improvement of 5.7 and 2.4, respectively.