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Faster R-CNN: Towards Real-Time Object
Detection with Region Proposal Networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun
Abstract—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 object bounds and objectness scores at each position. The RPN is trained end-to-end to
generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN
into a single network by sharing their convolutional features—using the recently popular terminology of neural network
work/net/region/Fast/RPN/object/detection/R-CNN/works/features/
work/net/region/Fast/RPN/object/detection/R-CNN/works/features/
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