This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture. Instead of using only two independent points, we design a multi-point supervision formulation to encode more clues in order to reduce the impact of inaccurate prediction of specific points. To take the full advantage of the correlation of points in a grid, we propose a two-stage information fusion strategy to fuse feature maps of neighbor grid points. The grid guided localization approach is easy to be extended to different state-of-the-art detection frameworks. Grid R-CNN leads to high quality object localization, and experiments demonstrate that it achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.

本文提出了一个名为Grid R-CNN的新型物体检测框架，它采用网格引导的定位机制实现准确的目标检测，其与传统的基于回归的方法进行了不同的创新，显式地捕获了空间信息并享有卷积架构的位置敏感性，同时采用多点监督公式设计来编码更多信息以减少特定点预测的不准确影响。为了充分利用网格中各点的关联性，提出了一种两阶段信息融合策略来融合相邻网格点的特征图，基于这些创新特性，Grid R-CNN实现了高品质的目标定位，实验显示，与Res50和FPN架构的Faster R-CNN相比，在COCO基准测试中，在IoU=0.8和0.9时，AP分别增加了4.1%和10.0%。

网格R-CNN