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

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          Abstract

          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. 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, 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.

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          Author and article information

          Journal
          arXiv
          2015
          04 June 2015
          05 June 2015
          13 September 2015
          15 September 2015
          06 January 2016
          07 January 2016
          June 2015
          Article
          10.48550/ARXIV.1506.01497
          27295650
          f91508a6-fe67-42be-bf59-c7fb0a31ac04

          arXiv.org perpetual, non-exclusive license

          History

          Computer Vision and Pattern Recognition (cs.CV),FOS: Computer and information sciences

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