论文标题
卫星图像深度学习对象检测模型的比较
A Comparison of Deep Learning Object Detection Models for Satellite Imagery
论文作者
论文摘要
在这项工作中，我们比较了几种最先进模型的检测准确性和速度，用于在商业电 - 光学卫星图像中检测油气水裂井和小型汽车的任务。从单阶段，两阶段和多阶段对象检测技术族研究了几种模型。为了检测压裂井垫（50m-250m），我们发现单阶段检测器提供了较高的预测速度，同时还可以匹配其两个和多阶段对应物的检测性能。但是，对于检测小型汽车，两阶段和多阶段模型以某种速度为代价提供了更高的精度。我们还测量了滑动窗口对象检测算法的定时结果，以提供比较的基线。其中一些模型已被整合到洛克希德·马丁（Lockheed Martin）全球可自动化目标识别（GATR）框架中。
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.
