Weakly-semi-supervised object detection in remotely sensed imagery

Ji Hun Wang, Jeremy Andrew Irvin, Beri Kohen Behar, Ha Tran, Raghav Samavedam, Quentin Hsu, Andrew Y. Ng

Abstract:

Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However,
these models often require large datasets labeled with bounding box annotations
which are expensive to curate, prohibiting the development of models for new tasks
and geographies. To address this challenge, we develop weakly-semi-supervised
object detection (WSSOD) models on remotely sensed imagery which can leverage
a small amount of bounding boxes together with a large amount of point labels that
are easy to acquire at scale in geospatial data. We train WSSOD models which
use large amounts of point-labeled images with varying fractions of bounding box
labeled images in FAIR1M and a wind turbine detection dataset, and demonstrate
that they substantially outperform fully supervised models trained with the same
amount of bounding box labeled images on both datasets. Furthermore, we find that
the WSSOD models trained with 2-10x fewer bounding box labeled images can
perform similarly to or outperform fully supervised models trained on the full set
of bounding-box labeled images. We believe that the approach can be extended to
other remote sensing tasks to reduce reliance on bounding box labels and increase
development of models for impactful applications.