Faster R-CNN 论文翻译——中英文对照
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
Networks
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,
and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO
2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place
winning entries in several tracks. Code has been made publicly available.
摘要
最先进的目标检测网络依靠区域提出算法来假设目标的位置。SPPnet[1]和
Fast R-CNN[2]等研究已经减少了这些检测网络的运行时间，使得区域提出计算
成为一个瓶颈。在这项工作中，我们引入了一个区域提出网络（RPN），该网络
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wsfd6002019-05-03文件损坏了呀
