You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: There have been object detectors that can be thought of as “fully convolutional” models. OverFeat [21] detects objects by sliding multi-scale windows on the shared convolutional feature maps; similarly, in Fast R-CNN [6] and [12], sliding windows that replace region proposals are investigated. In these cases, one can recast a sliding window of a single scale as a single convolutional layer. The RPN component in Faster R-CNN [18] is a fully convolutional detector that predicts bounding boxes with respect to reference boxes (anchors) of multiple sizes. The original RPN is class-agnostic in [18], but its class-specific counterpart is applicable (see also [14]) as we evaluate in the following.

Question: What was Faster R-CNN developed to overcome in the Fast R-CNN?
Faster R-CNN uses a RPN component to predict bounding boxes of objects it detects instead of sliding windows, which is what Fast R-CNN uses. From the names of the two models, it can be inferred that the change in methodology allowed Faster R-CNN to be more computationally efficient while still performing well.