Surface crack segmentation poses a challenging computer vision task as background, shape, color 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.

由于裂缝的背景，形状，颜色和大小各不相同，因此表面裂缝的分割带来了具有挑战性的计算机视觉任务。在这项工作中，我们提出了优化的深度编码器/解码器方法，该方法由多种技术组成，可以提高裂纹分割性能。具体来说，我们为基于编码器-解码器的深度学习架构的语义分割提出了解码器部分，并研究其组件以实现更高的性能。我们还研究了不同编码器策略的使用，并引入了数据增强策略以增加可用训练数据的数量。我们的方法的性能评估是在四个公开的裂纹分割数据集上进行的。此外，我们将两种技术引入了表面裂纹分割领域，这些技术以前在这里还没有使用：使用测试时间扩展来生成结果，并在多个训练运行中执行统计结果分析。前一种方法通常可以提高性能结果，而后一种方法可以使方法结果具有更高的可重复性和更好的可表示性。使用我们建议的编码器-解码器体系结构中的上述策略，我们可以在所有数据集中获得最新的技术成果。