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Globally-scalable Automated Target Recognition (GATR)

机译:全球可扩展的自动目标识别(GATR)

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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.
机译: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较大的地理区域。在单个GPU上,它以超过16 km的速度处理图像 2 /秒(或超过10 Mpixels / sec),并且仅需两个小时即可在宾夕法尼亚州的整个州搜索天然气压裂井。搜索时间与地理区域成线性比例,处理速度与GPU数量成线性比例。 GATR采用基于云的模块化架构,该架构使用Maxar的GBDX平台,并提供ATR分析即服务。应用程序包括广域搜索,监视港口和机场的监视盒以及站点特征。 ATR由包括RetinaNet和Faster R-CNN在内的深度学习模型执行。给出的结果用于飞机和压裂井的检测,并表明即使在从未见过的地理区域中,召回率也超过了90%。 GATR可以扩展到新的目标,例如汽车和轮船,还可以处理雷达和红外图像。

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