论文标题
全球可自动化目标识别（GATR）
Globally-scalable Automated Target Recognition (GATR)
论文作者
论文摘要
GATR（全球可自动化的目标识别）是洛克希德·马丁软件系统，用于在全球范围内在卫星图像中实时对象检测和分类。 GATR使用GPU加速深度学习软件来快速搜索大型地理区域。在单个GPU上，它以超过16平方km/sec（或超过10 mpixels/sec）的速度处理图像，仅需要两个小时才能搜索宾夕法尼亚州的整个状态以寻找气体压裂井。搜索时间与地理区域线性缩放，并且处理速率随GPU的数量线性缩放。 GATR具有基于模块化的基于云的体系结构，该体系结构使用Maxar GBDX平台，并提供ATR分析作为服务。应用程序包括广泛的区域搜索，监视端口和机场的观察框以及场地表征。 ATR由包括视网膜和更快的R-CNN在内的深度学习模型执行。提出了用于检测飞机和压裂井的结果，并表明即使在从未见过的地理区域中，召回也超过90％。 GATR对新目标（例如汽车和船只）来说是可扩展的，并且还可以处理雷达和红外图像。
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 square km/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 the Maxar 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.
