A Comparison of Deep Learning Object Detection Models for Satellite Imagery

• URL: http://arxiv.org/abs/2009.04857v1
• Date: Thu, 10 Sep 2020 13:43:14 GMT
• Title: A Comparison of Deep Learning Object Detection Models for Satellite Imagery
• Authors: Austen Groener, Gary Chern, Mark Pritt
• Abstract summary: 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. 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. For detecting small cars, two-stage and multi-stage models provide substantially higher accuracies at the cost of some speed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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