  An increasingly massive number of remote-sensing images spurs the development
of extensible object detectors that can detect objects beyond training
categories without costly collecting new labeled data. In this paper, we aim to
develop open-vocabulary object detection (OVD) technique in aerial images that
scales up object vocabulary size beyond training data. The fundamental
challenges hinder open vocabulary object detection performance: the qualities
of the class-agnostic region proposals and the pseudo-labels that can
generalize well to novel object categories. To simultaneously generate
high-quality proposals and pseudo-labels, we propose CastDet, a CLIP-activated
student-teacher open-vocabulary object Detection framework. Our end-to-end
framework following the student-teacher self-learning mechanism employs the
RemoteCLIP model as an extra omniscient teacher with rich knowledge. By doing
so, our approach boosts not only novel object proposals but also
classification. Furthermore, we devise a dynamic label queue strategy to
maintain high-quality pseudo labels during batch training. We conduct extensive
experiments on multiple existing aerial object detection datasets, which are
set up for the OVD task. Experimental results demonstrate our CastDet achieving
superior open-vocabulary detection performance, e.g., reaching 40.5\% mAP,
which outperforms previous methods Detic/ViLD by 23.7%/14.9% on the VisDroneZSD
dataset. To our best knowledge, this is the first work to apply and develop the
open-vocabulary object detection technique for aerial images.
