Supervised and semi-supervised semantic segmentation algorithms require significant amount of annotated data to achieve a good performance. In many situations, the data is either not available or the annotation is expensive. The objective of this work is to show that by incorporating domain knowledge along with deep learning architectures, we can achieve similar performance with less data. We have used publicly available crack segmentation datasets and shown that selecting the input images using knowledge can significantly boost the performance of deep-learning based architectures. Our proposed approaches have many fold advantages such as low annotation and training cost, and less energy consumption. We have measured the performance of our algorithm quantitatively in terms of mean intersection over union (mIoU) and F score. Our algorithms, developed with 23% of the overall data; have a similar performance on the test data and significantly better performance on multiple blind datasets.

翻译：监督式和半监督式语义分割算法需要大量标注数据才能取得良好性能。在许多情况下，数据要么不可获取，要么标注成本高昂。本研究表明，通过将领域知识与深度学习架构相结合，我们可以在较少数据条件下达到相近性能。我们采用公开的裂缝分割数据集，证明基于知识选取输入图像可显著提升深度学习架构的性能。本文提出的方法具有多重优势，包括标注成本低、训练成本低以及能耗少。我们通过平均交并比（mIoU）和F分数对算法性能进行了定量评估。仅使用全部数据23%开发的算法，在测试数据上表现出与全数据方法相近的性能，并在多个盲测数据集上取得了显著更优的结果。