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

06/04/2015
by   Shaoqing Ren, et al.
0

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 FULL TEXT

page 2

page 3

page 4

page 12

page 13

research
04/03/2016

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

Almost all of the current top-performing object detection networks emplo...
research
01/25/2016

Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection

R-CNN style methods are sorts of the state-of-the-art object detection m...
research
07/25/2018

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

Accurately localising object proposals is an important precondition for ...
research
01/19/2016

Scale-aware Pixel-wise Object Proposal Networks

Object proposal is essential for current state-of-the-art object detecti...
research
01/26/2017

Deep Region Hashing for Efficient Large-scale Instance Search from Images

Instance Search (INS) is a fundamental problem for many applications, wh...
research
07/17/2023

Random Boxes Are Open-world Object Detectors

We show that classifiers trained with random region proposals achieve st...
research
01/16/2022

YOLO – You only look 10647 times

With this work we are explaining the "You Only Look Once" (YOLO) single-...

Please sign up or login with your details

Forgot password? Click here to reset