  The class imbalance problem in deep learning has been explored in several
studies, but there has yet to be a systematic analysis of this phenomenon in
object detection. Here, we present comprehensive analyses and experiments of
the foreground-background (F-B) imbalance problem in object detection, which is
very common and caused by small, infrequent objects of interest. We
experimentally study the effects of different aspects of F-B imbalance (object
size, number of objects, dataset size, object type) on detection performance.
In addition, we also compare 9 leading methods for addressing this problem,
including Faster-RCNN, SSD, OHEM, Libra-RCNN, Focal-Loss, GHM, PISA, YOLO-v3,
and GFL with a range of datasets from different imaging domains. We conclude
that (1) the F-B imbalance can indeed cause a significant drop in detection
performance, (2) The detection performance is more affected by F-B imbalance
when fewer training data are available, (3) in most cases, decreasing object
size leads to larger performance drop than decreasing number of objects, given
the same change in the ratio of object pixels to non-object pixels, (6) among
all selected methods, Libra-RCNN and PISA demonstrate the best performance in
addressing the issue of F-B imbalance. (7) When the training dataset size is
large, the choice of method is not impactful (8) Soft-sampling methods,
including focal-loss, GHM, and GFL, perform fairly well on average but are
relatively unstable.
