论文标题
点建议网络：通过深度学习加速点源检测
Point Proposal Network: Accelerating Point Source Detection Through Deep Learning
论文作者
论文摘要
点源检测技术用于识别和定位点源在射电射击调查中。随着平方公里阵列（SKA）望远镜的发展，调查图像将使从吉像素到terapixels的大小大大增加。在SKA Pathfinder望远镜进行的最近进行的调查中，Point Source检测已被证明是一个挑战。本文提出了点提案网络（PPN）：利用深层卷积神经网络进行快速源检测的点源检测器。在模拟的Meerkat图像上测量的结果表明，尽管与领先的替代方法相比，PPN的执行速度更快，但与替代方法不同，PPN可以更快地执行源检测，并且能够扩展到大图像。
Point source detection techniques are used to identify and localise point sources in radio astronomical surveys. With the development of the Square Kilometre Array (SKA) telescope, survey images will see a massive increase in size from Gigapixels to Terapixels. Point source detection has already proven to be a challenge in recent surveys performed by SKA pathfinder telescopes. This paper proposes the Point Proposal Network (PPN): a point source detector that utilises deep convolutional neural networks for fast source detection. Results measured on simulated MeerKAT images show that, although less precise when compared to leading alternative approaches, PPN performs source detection faster and is able to scale to large images, unlike the alternative approaches.
