Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comparison of Deep Learning Object Detection Models for Satellite
  Imagery   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.

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The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol