  Model ensembles are becoming one of the most effective approaches for
improving object detection performance already optimized for a single detector.
Conventional methods directly fuse bounding boxes but typically fail to
consider proposal qualities when combining detectors. This leads to a new
problem of confidence discrepancy for the detector ensembles. The confidence
has little effect on single detectors but significantly affects detector
ensembles. To address this issue, we propose a novel ensemble called the
Probabilistic Ranking Aware Ensemble (PRAE) that refines the confidence of
bounding boxes from detectors. By simultaneously considering the category and
the location on the same validation set, we obtain a more reliable confidence
based on statistical probability. We can then rank the detected bounding boxes
for assembly. We also introduce a bandit approach to address the confidence
imbalance problem caused by the need to deal with different numbers of boxes at
different confidence levels. We use our PRAE-based non-maximum suppression
(P-NMS) to replace the conventional NMS method in ensemble learning.
Experiments on the PASCAL VOC and COCO2017 datasets demonstrate that our PRAE
method consistently outperforms state-of-the-art methods by significant
margins.
