  In object detection with deep neural networks, the box-wise objectness score
tends to be overconfident, sometimes even indicating high confidence in
presence of inaccurate predictions. Hence, the reliability of the prediction
and therefore reliable uncertainties are of highest interest. In this work, we
present a post processing method that for any given neural network provides
predictive uncertainty estimates and quality estimates. These estimates are
learned by a post processing model that receives as input a hand-crafted set of
transparent metrics in form of a structured dataset. Therefrom, we learn two
tasks for predicted bounding boxes. We discriminate between true positives
($\mathit{IoU}\geq0.5$) and false positives ($\mathit{IoU} < 0.5$) which we
term meta classification, and we predict $\mathit{IoU}$ values directly which
we term meta regression. The probabilities of the meta classification model aim
at learning the probabilities of success and failure and therefore provide a
modelled predictive uncertainty estimate. On the other hand, meta regression
gives rise to a quality estimate. In numerical experiments, we use the publicly
available YOLOv3 network and the Faster-RCNN network and evaluate meta
classification and regression performance on the Kitti, Pascal VOC and COCO
datasets. We demonstrate that our metrics are indeed well correlated with the
$\mathit{IoU}$. For meta classification we obtain classification accuracies of
up to 98.92% and AUROCs of up to 99.93%. For meta regression we obtain an $R^2$
value of up to 91.78%. These results yield significant improvements compared to
other network's objectness score and other baseline approaches. Therefore, we
obtain more reliable uncertainty and quality estimates which is particularly
interesting in the absence of ground truth.
