Object proposal methods were classically adopted as external modules independent of the detectors. Widely used object proposal methods include those based on grouping super-pixels (e.g., Selective Search) and those based on sliding windows (e.g., objectness in windows).
In Faster RCNN the RPN Network is an objectness based region proposal network that is integrated with the CNN network.
In the original RCNN method and Fast-RCNN, Selective Search is used to generate region proposals while the CNN part is used to classify them into object categories or background independently. Thus the CNN part mainly plays as a classifier, and it does not predict object bounds (except for refining by a bounding box regression layer). Therefore its accuracy depends on the performance of the region proposal module.
The contribution of Faster-RCNN on the other hand, is that the authors proposed a unified way to accomplish both tasks. A simplified way to think of this is that they replaced Selective Search by adopting an RPN. The RPN shared convolutional features with a CNN object detector. Thus the classification loss of the RPN network is used for a binary classification task with the following classes: object, not object, while the classification loss of the CNN is used for classification of object category.
The experiments they performed showed that when the two networks share features (instead of using separate RPN and CNN networks), detection performance is improved.
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