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Using convolutional neural networks to automatically select small artificial space objects on optical images of a starry sky

机译:使用卷积神经网络在星空的光学图像上自动选择小型人造空间物体

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摘要

This article discusses the use of convolutional neural networks to solve the problem of automatically selecting moving objects on a moving starry background when their images exhibit speed blur. The article gives the results of testing several networks that have substantially less structural complexity than does the prototype. The estimates obtained for the accuracy and selection rate of several of the networks studied here are evidence that it is promising to use such networks to detect, classify, and estimate the location of two types of objects in the instrument's coordinate system when resources are severely limited. (C) 2019 Optical Society of America
机译:本文讨论了使用卷积神经网络来解决当图像呈现速度模糊时在移动的星空背景上自动选择移动物体的问题。本文给出了测试几个网络的结果,这些网络的结构复杂度比原型低得多。本文研究的几个网络的精度和选择率的估计值证明,当资源严重有限时,使用此类网络来检测、分类和估计仪器坐标系中两类物体的位置是有希望的。(C) 2019年美国光学学会

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