Scale-Invariant Feature Disentanglement via Adversarial Learning for UAV-based Object Detection

• URL: http://arxiv.org/abs/2405.15465v2
• Date: Fri, 31 May 2024 08:08:23 GMT
• Title: Scale-Invariant Feature Disentanglement via Adversarial Learning for UAV-based Object Detection
• Authors: Fan Liu, Liang Yao, Chuanyi Zhang, Ting Wu, Xinlei Zhang, Xiruo Jiang, Jun Zhou,
• Abstract summary: We propose to improve single-stage inference accuracy through learning scale-invariant features. We apply our approach to three state-of-the-art lightweight detection frameworks on three benchmark datasets.
• Score: 18.11107031800982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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