  Deep neural object detection or segmentation networks are commonly trained
with pristine, uncompressed data. However, in practical applications the input
images are usually deteriorated by compression that is applied to efficiently
transmit the data. Thus, we propose to add deteriorated images to the training
process in order to increase the robustness of the two state-of-the-art
networks Faster and Mask R-CNN. Throughout our paper, we investigate an
autonomous driving scenario by evaluating the newly trained models on the
Cityscapes dataset that has been compressed with the upcoming video coding
standard Versatile Video Coding (VVC). When employing the models that have been
trained with the proposed method, the weighted average precision of the R-CNNs
can be increased by up to 3.68 percentage points for compressed input images,
which corresponds to bitrate savings of nearly 48 %.
