Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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 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 highquality 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 https://github.com/ShaoqingRen/faster_rcnn.
The paper presents a region proposal network (RPN) which shares the conv features with R-CNN and includes two fully connected layers that predicts object bounds and objectness scores at each position. RPN is heuristically combined with Fast R-CNN for object detection and achieves state-of-the-art performance on VOC07 and VOC12. The running time of generating region proposals is greatly reduced since RPN shares the conv layers with the detection network.Pros: - Smart idea on sharing conv layers between RPN and R-CNN which greatly reduced the run-time of object proposals.- State-of-the-art performance on object detection.- Well-written paper.Cons:- The training of RPN and fast R-CNN are weakly combined by alternating optimization and there is no convergence proof.- RPN can be used independently as a region proposal method. However, it doesn't seem to outperform selective search.