# Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

“Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (2016), by Shaoqing Ren et al. Computer Science research paper. Original abstract below, unchanged. No free PDF was found.

- Authors: Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
- Published: June 6, 2016
- Field: Computer Science
- DOI: https://doi.org/10.1109/tpami.2016.2577031
- Source: https://doi.org/10.1109/tpami.2016.2577031

## 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 5 fps (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.

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