Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun S. Ren is with University of Science and Technology of China, Hefei, China. This work was done when S. Ren was an intern at Microsoft Research. Email: K. He and J. Sun are with Visual Computing Group, Microsoft Research. E-mail: R. Girshick is with Facebook AI Research. The majority of this work was done when R. Girshick was with Microsoft Research. E-mail:
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 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
中文速览
目标检测领域长期依赖"先生成候选区域、再做分类"的两阶段流程，但候选区域的生成（如Selective Search）速度慢、独立于检测网络，成为整个系统的速度瓶颈。Faster R-CNN提出了区域候选网络（Region Proposal Network，RPN），让它与检测网络共享同一套卷积特征图，通过在特征图上滑动小网络并引入多尺度、多长宽比的"锚框（anchor）"机制，同时预测候选框位置和前景得分，使候选区域生成的额外耗时压缩到每张图仅约10毫秒。将RPN与Fast R-CNN合并为一个端到端的统一网络后，系统在GPU上以5帧/秒的速度运行，同时在PASCAL VOC、MS COCO等主流基准上达到当时最优的检测精度，并在2015年ILSVRC和COCO竞赛多个赛道中夺得第一。这项工作第一次真正把候选区域生成纳入深度网络统一训练，使"近乎零成本的候选区域"成为现实，深刻影响了此后目标检测乃至实例分割、图像描述等众多视觉任务的研究方向。
原文 arXiv:1506.01497;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1506.01497v3