  Deep learning has been revolutionary for computer vision and semantic
segmentation in particular, with Bayesian Deep Learning (BDL) used to obtain
uncertainty maps from deep models when predicting semantic classes. This
information is critical when using semantic segmentation for autonomous driving
for example. Standard semantic segmentation systems have well-established
evaluation metrics. However, with BDL's rising popularity in computer vision we
require new metrics to evaluate whether a BDL method produces better
uncertainty estimates than another method. In this work we propose three such
metrics to evaluate BDL models designed specifically for the task of semantic
segmentation. We modify DeepLab-v3+, one of the state-of-the-art deep neural
networks, and create its Bayesian counterpart using MC dropout and Concrete
dropout as inference techniques. We then compare and test these two inference
techniques on the well-known Cityscapes dataset using our suggested metrics.
Our results provide new benchmarks for researchers to compare and evaluate
their improved uncertainty quantification in pursuit of safer semantic
segmentation.
