**Object detection** is the task of drawing one bounding box around each instance of the type of object one wants to detect. Typically, image classification is done before object detection. With neural networks, the usual procedure for object detection is to train a classification network, replace the last layer with a regression layer which essentially predicts pixel-wise if the object is there or not. An bounding box inference algorithm is added at last to make a consistent prediction (see [Deep Neural Networks for Object Detection](http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf)).
The paper introduces RPNs (Region Proposal Networks). They are end-to-end trained to generate region proposals.They simoultaneously regress region bounds and bjectness scores at each location on a regular grid.
RPNs are one type of fully convolutional networks. They take an image of any size as input and output a set of rectangular object proposals, each with an objectness score.
## See also
* [Fast R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen)
* [Faster R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/nips/RenHGS15#martinthoma)
* [Mask R-CNN](http://www.shortscience.org/paper?bibtexKey=journals/corr/HeGDG17)