Globally-scalable Automated Target Recognition (GATR)

Gary Chern, A. Groener, Michael Harner, Tyler Kuhns, A. Lam, Stephen O’Neill, M. D. Pritt
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引用次数: 4

Abstract

GATR (Globally-scalable Automated Target Recognition) is a Lockheed Martin software system for real-time object detection and classification in satellite imagery on a worldwide basis. GATR uses GPU-accelerated deep learning software to quickly search large geographic regions. On a single GPU it processes imagery at a rate of over 16 km2/sec (or more than 10 Mpixels/sec), and it requires only two hours to search the entire state of Pennsylvania for gas fracking wells. The search time scales linearly with the geographic area, and the processing rate scales linearly with the number of GPUs. GATR has a modular, cloud-based architecture that uses Maxar’s GBDX platform and provides an ATR analytic as a service. Applications include broad area search, watch boxes for monitoring ports and airfields, and site characterization. ATR is performed by deep learning models including RetinaNet and Faster R-CNN. Results are presented for the detection of aircraft and fracking wells and show that the recalls exceed 90% even in geographic regions never seen before. GATR is extensible to new targets, such as cars and ships, and it also handles radar and infrared imagery.
全球可扩展的自动目标识别
GATR(全球可扩展自动目标识别)是洛克希德·马丁公司的软件系统,用于在全球范围内对卫星图像进行实时目标检测和分类。GATR使用gpu加速的深度学习软件来快速搜索大型地理区域。在单个GPU上,它处理图像的速度超过16平方公里/秒(或超过1000万像素/秒),只需要两个小时就可以搜索整个宾夕法尼亚州的天然气压裂井。搜索时间与地理区域成线性关系,处理速度与gpu数量成线性关系。GATR采用模块化、基于云的架构,使用Maxar的GBDX平台,并提供ATR分析服务。应用包括广域搜索,监视港口和机场的监视箱,以及现场表征。ATR由包括RetinaNet和Faster R-CNN在内的深度学习模型执行。对飞机和压裂井的检测结果表明,即使在从未见过的地理区域,召回率也超过90%。GATR可以扩展到新的目标,如汽车和船只,它还可以处理雷达和红外图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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