The highest accuracy object detectors to date are based on a two-stage approach
popularized by R-CNN, where a classifier is applied to a sparse set of candidate object
locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of
possible object locations have the potential to be faster and simpler, but have trailed the
accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case.
We discover that the extreme foreground-background class imbalance encountered during�…