DetectoRS: Detecting Objects with Recursive Feature Pyramidand Switchable Atrous Convolution

摘要

许多新出的目标检测器都表现出优秀的性能，通多使用三思而后行的机制进行检测。在本文中，我们探索了这个机制在设计目标检测骨干中。在宏观层面，我们提出了递归特征金字塔，它包含来自特征金字塔的额外反馈连接进入自下而上的骨干层。在微观层面，我们提出了可切换的膨胀卷积，它包含不同膨胀卷积率的特征，收集结果使用开关功能，将它们的结果结合，就是当前的检测器，它极大提高了目标检测的性能。在coco数据集上，该检测器实现了最好的54.7的框目标检测效果，实现了47.1的掩码实例分割识别效果，和49.6的全景分割识别效果。代码是公开有效的。

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