  In this work, we compare the detection accuracy and speed of several
state-of-the-art models for the task of detecting oil and gas fracking wells
and small cars in commercial electro-optical satellite imagery. Several models
are studied from the single-stage, two-stage, and multi-stage object detection
families of techniques. For the detection of fracking well pads (50m - 250m),
we find single-stage detectors provide superior prediction speed while also
matching detection performance of their two and multi-stage counterparts.
However, for detecting small cars, two-stage and multi-stage models provide
substantially higher accuracies at the cost of some speed. We also measure
timing results of the sliding window object detection algorithm to provide a
baseline for comparison. Some of these models have been incorporated into the
Lockheed Martin Globally-Scalable Automated Target Recognition (GATR)
framework.
