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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren · Kaiming He · Ross Girshick · Jian Sun

Mon Dec 07 04:00 PM -- 08:59 PM (PST) @ 210 C #6 #None

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 object bounds and objectness scores at each position. RPNs are trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. With a simple alternating optimization, RPN and Fast R-CNN can be trained to share convolutional features. For the very deep VGG-16 model, 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 (73.2% mAP) and 2012 (70.4% mAP) using 300 proposals per image. Code is available at https://github.com/ShaoqingRen/faster_rcnn.

Author Information

Shaoqing Ren (USTC)
Kaiming He (Microsoft Research Asia)
Ross Girshick (Microsoft Research)
Jian Sun (Microsoft Research Asia)

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