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

Date: May 5th, 2016

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

Author: Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun

Novelties:

Introduce Region proposal networks(RPNs) that share convolutional layers to reduce the computing marginal costs.

Contributions:

Improve the region-based CNNs with RPNs.

Technical Summarizes:

Region proposal network is to output set of rectangular object proposals with score on an input image. Our goal is to share computation and conv layers with Fast R-CNN object detection networks.
In this work, they investigate ZF and VGG models.
The network is implemented with 3x3 conv layer followed by two sibling 1x1 conv layers. This network shares across spatial locations since it operates in sliding-window fashion. Each sliding position is with k=9 anchors for translation invariant properties.

Then they should learn conv layers that are shared between RPN defined before and Fast R-CNN with joint optimizing. The steps are training the RPN with ImageNet pre-trained model, training the Fast R-CNN using the proposals generated by RPN, fixing the shared conv layers, fine-tuning the unique layer in RPN, and fine-tuning the fc layers of the Fast R-CNN.

Experiments:

The dataset is PASCAL VOC 2007, which contains 5k trainval images and 5k test images over 20 categories. The RPN with Fast R-CNN has the mAP 59.9% with up to 300 proposals which is much faster, too.

The SS takes 1.51s on average, Fast R-CNN with VGG-16 takes 320ms, and their system takes only 198 ms in total.