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Advanced Convolutional Neural Networks
The next iteration of the R-CNN network was called the Fast R-CNN. The Fast
R-CNN still gets its region proposals from an external tool, but instead of feeding
each region proposal through the CNN, the entire image is fed through the CNN
and the region proposals are projected onto the resulting feature map. Each region of
interest is fed through an Region of Interest (ROI) pooling layer and then to a fully
connected network, which produces a feature vector for the ROI.
ROI pooling is a widely used operation in object detection tasks using convolutional
neural networks. The ROI pooling layer uses max pooling to convert the features
inside any valid region of interest into a small feature map with a fixed spatial extent
of H × W (where H and W are two hyperparameters). The feature vector is then
fed into two fully connected networks, one to predict the class of the ROI and the
other to correct the bounding box coordinates for the proposal. This is illustrated
in Figure 6:
Figure 6: Fast R-CNN network architecture
The Fast R-CNN is about 25x faster than the R-CNN. The next improvement, called
the Faster R-CNN (an implementation can be found at https://github.com/
tensorpack/tensorpack/tree/master/examples/FasterRCNN), removes the
external region proposal mechanism and replaces it with a trainable component,
called the Region Proposal Network (RPN), within the network itself.
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