READING NOTE: R-FCN: Object Detection via Region-based Fully Convolutional Networks

转载自：

http://blog.csdn.net/joshua_1988/article/details/51484412

 

TITLE: R-FCN: Object Detection via Region-based Fully Convolutional Networks

AUTHER: Jifeng Dai, Yi Li, Kaiming He, Jian Sun

ASSOCIATION: MSRA, Tsinghua University

FROM: arXiv:1605.06409

CONTRIBUTIONS

1. A framework called Region-based Fully Convolutional Network (R-FCN) is develpped for object detection, which consists of shared, fully convolutional architectures.
2. A set of position-sensitive score maps are introduced to enalbe FCN representing translation variance.
3. A unique ROI pooling method is proposed to shepherd information from metioned score maps.

METHOD

1. The image is processed by a FCN manner network.
2. At the end of FCN, a RPN (Region Proposal Network) is used to generate ROIs.
3. On the other hand, a score map of k 2 (C+1)  channels is generated using a bank of specialized convolutional layers.
4. For each ROI, a selective ROI pooling is utilized to generate a C+1  channel score map.
5. The scores in the score map are averaged to vote for category.
6. Another 4k 2   dim convolutional layer is learned for bounding box regression.

Training Details

1. R-FCN is trained end-to-end with pre-computed region proposals. Both category and position are learnt with the loss function: L(s,t x,y,w,h )=L cls (s c ∗  )+λ[c ∗ >0]L reg (t,t ∗ )  .
2. For each image, N proposals are generated and B out of N proposals are selected to train weights according to the highest losses. B is set to 128 in this work.
3. 4-step alternating training is utilized to realizing feature sharing between R-FCN and RPN.

ADVANTAGES

1. It is fast (170ms/image, 2.5-20x faster than Faster R-CNN).
2. End-to-end training is easier to process.
3. All learnable layers are convolutional and shared on the entire image, yet encode spatial information required for object detection.

DISADVANTAGES

1. Compared with Single Shot methods, more computation resource is needed