Scale-Invariant Feature Disentanglement via Adversarial Learning for UAV-based Object Detection
<|reference_start|>Scale-Invariant Feature Disentanglement via Adversarial Learning for UAV-based Object Detection: Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream approaches typically utilize multi-stage inferences. Despite their remarkable detecting accuracies, real-time efficiency is sacrificed, making them less practical to handle real applications. To this end, we propose to improve the single-stage inference accuracy through learning scale-invariant features. Specifically, a Scale-Invariant Feature Disentangling module is designed to disentangle scale-related and scale-invariant features. Then an Adversarial Feature Learning scheme is employed to enhance disentanglement. Finally, scale-invariant features are leveraged for robust UAV-based object detection. Furthermore, we construct a multi-modal UAV object detection dataset, State-Air, which incorporates annotated UAV state parameters. We apply our approach to three state-of-the-art lightweight detection frameworks on three benchmark datasets, including State-Air. Extensive experiments demonstrate that our approach can effectively improve model accuracy. Our code and dataset are provided in Supplementary Materials and will be publicly available once the paper is accepted.<|reference_end|>
@article{liu2024scale-invariant,
    title={Scale-Invariant Feature Disentanglement via Adversarial Learning for
  UAV-based Object Detection},
    author={Fan Liu, Liang Yao, Chuanyi Zhang, Ting Wu, Xinlei Zhang, Xiruo Jiang,
  Jun Zhou},
    journal={arXiv preprint arXiv:2405.15465},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15465},
    primaryClass={cs.CV}
}