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Shaoqing Ren • Kaiming He • Ross Girshick • Jian Sun•
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. (read more)PDF
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Object Detection||PASCAL VOC 2007||Faster R-CNN||MAP||73.2%||# 14|
|Real-Time Object Detection||PASCAL VOC 2007||Faster R-CNN||MAP||73.2%||# 5|
|Real-Time Object Detection||PASCAL VOC 2007||Faster R-CNN||FPS||7||# 6|