In the semantic segmentation of street scenes the reliability of the prediction and therefore uncertainty measures are of highest interest. We present a method that generates for each input image a hierarchy of nested crops around the image center and presents these, all re-scaled to the same size, to a neural network for semantic segmentation. The resulting softmax outputs are then post processed such that we can investigate mean and variance over all image crops as well as mean and variance of uncertainty heat maps obtained from pixel-wise uncertainty measures, like the entropy, applied to each crop's softmax output. In our tests, we use the publicly available DeepLabv3+ MobilenetV2 network (trained on the Cityscapes dataset) and demonstrate that the incorporation of crops improves the quality of the prediction and that we obtain more reliable uncertainty measures. These are then aggregated over predicted segments for either classifying between IoU = 0 and IoU > 0 (meta classification) or predicting the IoU via linear regression (meta regression). The latter yields reliable performance estimates for segmentation networks, in particular useful in the absence of ground truth. For the task of meta classification we obtain a classification accuracy of 81.93% and an AUROC of 89.89%. For meta regression we obtain an R 2 value of 84.77%. These results yield significant improvements compared to other approaches.

In object detection with deep neural networks, the boxwise 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 handcrafted set of transparent metrics in form of a structured dataset. Therefrom, we learn two tasks for predicted bounding boxes. We discriminate between true positives (IoU ≥ 0.5) and false positives (IoU < 0.5) which we term meta classification, and we predict 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 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.

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Reliable epistemic uncertainty estimation is an essential component for backend applications of deep object detectors in safety-critical environments. Modern network architectures tend to give poorly calibrated confidences with limited predictive power. Here, we introduce novel gradient-based uncertainty metrics and investigate them for different object detection architectures. Experiments on the MS COCO, PASCAL VOC and the KITTI dataset show significant improvements in true positive / false positive discrimination and prediction of intersection over union as compared to network confidence. We also find improvement over Monte-Carlo dropout uncertainty metrics and further significant boosts by aggregating different sources of uncertainty metrics. The resulting uncertainty models generate well-calibrated confidences in all instances. Furthermore, we implement our uncertainty quantification models into object detection pipelines as a means to discern true against false predictions, replacing the ordinary score-threshold-based decision rule. In our experiments, we achieve a significant boost in detection performance in terms of mean average precision. With respect to computational complexity, we find that computing gradient uncertainty metrics results in floating point operation counts similar to those of Monte-Carlo dropout.

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