  Surface crack segmentation poses a challenging computer vision task as
background, shape, colour and size of cracks vary. In this work we propose
optimized deep encoder-decoder methods consisting of a combination of
techniques which yield an increase in crack segmentation performance.
Specifically we propose a decoder-part for an encoder-decoder based deep
learning architecture for semantic segmentation and study its components to
achieve increased performance. We also examine the use of different encoder
strategies and introduce a data augmentation policy to increase the amount of
available training data. The performance evaluation of our method is carried
out on four publicly available crack segmentation datasets. Additionally, we
introduce two techniques into the field of surface crack segmentation,
previously not used there: Generating results using test-time-augmentation and
performing a statistical result analysis over multiple training runs. The
former approach generally yields increased performance results, whereas the
latter allows for more reproducible and better representability of a methods
results. Using those aforementioned strategies with our proposed
encoder-decoder architecture we are able to achieve new state of the art
results in all datasets.
