Faster_R-CNN中英文对照翻译1
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Faster R-CNN: Towards Real-Time Object Detection with
Region Proposal Networks
Faster R-CNN：通过 Region Proposal 网络实现实时目标检
测
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 networks
with “attention” mechanisms, the RPN component tells the unified network
where to look. For the very deep VGG-16 model [3], our detection system
has a frame rate of 5fps (including all steps) on a GPU, while achieving
state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012,
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