Generating Positive Bounding Boxes for Balanced Training of Object Detectors

Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

Abstract:

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 secondstage. 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.