Boost UAV-based Ojbect Detection via Scale-Invariant Feature Disentanglement and Adversarial Learning

TL;DR

This paper introduces a novel method for UAV object detection that disentangles scale-invariant features using adversarial learning, significantly improving accuracy while maintaining real-time performance, and provides a new UAV detection dataset.

Contribution

The paper proposes a scale-invariant feature disentangling module combined with adversarial learning for improved single-stage UAV object detection.

Findings

Achieves state-of-the-art detection accuracy on two benchmark datasets.

Effectively improves model robustness to scale variations.

Provides a new annotated UAV detection dataset, State-Air.

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 lightweight detection frameworks on two benchmark datasets. Extensive experiments demonstrate that our approach can effectively improve model accuracy and achieve state-of-the-art (SoTA) performance on two datasets. Our code and dataset will be publicly available once the paper is accepted.