Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available.

翻译：许多现代天体探测器通过使用视觉和思维机制两次展示了杰出的性能。 在本文中, 我们在天体探测的主干设计中探索了这一机制。 在宏观层面, 我们提议了 Recurive Featural Pyramid Pyramid, 其中包括从地貌金字网到自下而上主干层的额外反馈连接。 在微观层面, 我们提议了可切换的突变, 它将不同速度的特征混杂在一起, 并使用开关功能收集结果。 将它们合并在探测器中的结果, 大大改进了天体探测的性能。 在COCO测试- dev上, 探测器实现了天体探测的55.7 % 方程式状态, 48.5% 的防腐蚀 AP 用于例分解, 50.0 PQ 用于光谱分割 。 该代码被公开提供 。