Unlocking Drone Perception in Low AGL Heights: Progressive Semi-Supervised Learning for Ground-to-Aerial Perception Knowledge Transfer

We explore the novel challenge of drone perception across varying low AGL (above ground level) heights, a task essential for dynamic tasks, unlike the fixed ground viewpoint in autonomous driving. Supervised learning for this incurs high annotation costs, and current semi-supervised methods struggle with viewpoint differences. In this paper, we introduce ground-to-aerial perception knowledge transfer and propose a progressive semi-supervised learning framework for drone perception using only labeled data from the ground viewpoint and unlabeled data from flying viewpoints. The framework hinges on four key components: 1) a dense viewpoint sampling strategy, segmenting the vertical flight height range into evenly distributed intervals; 2) nearest neighbor pseudo-labeling, inferring labels of the nearest neighbor viewpoint using a model learned on the preceding viewpoint; 3) MixView, generating augmented images among different viewpoints to mitigate viewpoint differences; and 4) a progressive distillation strategy, gradually learning until reaching the maximum flying height. To validate our approach, we create both synthesized and real-world datasets. Extensive experimental analyses reveal a remarkable relative accuracy improvement of 25.7% and 16.9% for the synthesized dataset and the real world, respectively. Code and datasets are available on https://github.com/FreeformRobotics/Progressive-Self-Distillation-for-Ground-to-Aerial-Perception-Knowledge-Transfer.