  Two-stage deep object detectors generate a set of regions-of-interest (RoI)
in the first stage, then, in the second stage, identify objects among the
proposed RoIs that sufficiently overlap with a ground truth (GT) box. The
second stage is known to suffer from a bias towards RoIs that have low
intersection-over-union (IoU) with the associated GT boxes. To address this
issue, we first propose a sampling method to generate bounding boxes (BB) that
overlap with a given reference box more than a given IoU threshold. Then, we
use this BB generation method to develop a positive RoI (pRoI) generator that
produces RoIs following any desired spatial or IoU distribution, for the
second-stage. We show that our pRoI generator is able to simulate other
sampling methods for positive examples such as hard example mining and prime
sampling. Using our generator as an analysis tool, we show that (i) IoU
imbalance has an adverse effect on performance, (ii) hard positive example
mining improves the performance only for certain input IoU distributions, and
(iii) the imbalance among the foreground classes has an adverse effect on
performance and that it can be alleviated at the batch level. Finally, we train
Faster R-CNN using our pRoI generator and, compared to conventional training,
obtain better or on-par performance for low IoUs and significant improvements
when trained for higher IoUs for Pascal VOC and MS COCO datasets. The code is
available at: https://github.com/kemaloksuz/BoundingBoxGenerator.
