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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren Kaiming He Ross Girshick Jian Sun Microsoft Research v-shren, kahe, rbg, jiansun@ Abstract State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region pro- posal computation as a bottleneck. In this work, we introduce a Region Pro- posal Network (RPN) that shares full-image convolutional features with the de- tection network, thus enabling nearly cost-free region proposals. An RPN is a fully-convolutional network that simultaneously predicts object bounds and ob- jectness 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 [ 19], 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 /ShaoqingRen/faster_rcnn. 1 Introduction Recent advances in object detection are driven by the success of region proposal methods (e.g., [22]) and region-based convolutional neural networks (R-CNNs) [6]. Although region-based CNNs were computationally expensive as originally developed in [6], their