State-of-the-art object detection networks depend on region proposal algorithms to
hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the
running time of these detection networks, exposing region proposal computation as a
bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-
image convolutional features with the detection network, thus enabling nearly cost-free
region proposals. An RPN is a fully-convolutional network that simultaneously predicts …

State-of-the-art object detection networks depend on region proposal algorithms to
hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced
the running time of these detection networks, exposing region proposal computation as a
bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-
image convolutional features with the detection network, thus enabling nearly cost-free
region proposals. An RPN is a fully convolutional network that simultaneously predicts …