  Detection of small objects in large swaths of imagery is one of the primary
problems in satellite imagery analytics. While object detection in ground-based
imagery has benefited from research into new deep learning approaches,
transitioning such technology to overhead imagery is nontrivial. Among the
challenges is the sheer number of pixels and geographic extent per image: a
single DigitalGlobe satellite image encompasses >64 km2 and over 250 million
pixels. Another challenge is that objects of interest are minuscule (often only
~10 pixels in extent), which complicates traditional computer vision
techniques. To address these issues, we propose a pipeline (You Only Look
Twice, or YOLT) that evaluates satellite images of arbitrary size at a rate of
>0.5 km2/s. The proposed approach can rapidly detect objects of vastly
different scales with relatively little training data over multiple sensors. We
evaluate large test images at native resolution, and yield scores of F1 > 0.8
for vehicle localization. We further explore resolution and object size
requirements by systematically testing the pipeline at decreasing resolution,
and conclude that objects only ~5 pixels in size can still be localized with
high confidence. Code is available at https://github.com/CosmiQ/yolt.
