Semantic segmentation of cracks: Data challenges and architecture

Deep Learning (DL) semantic image segmentation is a technique used in several fields of research. The present paper analyses semantic crack segmentation as a case study to review the up to date research on semantic segmentation in the presence of fine structures and the effectiveness of established approaches to address the inherent class imbalance issue. The established UNet architecture is tested against networks consisting exclusively of stacked convolution without pooling layers (straight networks), with regard to the resolution of their segmentation results. Dice and Focal losses are also compared against each other to evaluate their effectiveness on highly imbalanced data. With the same aim, dropout and data augmentation approaches are tested, as additional regularizing mechanisms, to address the uneven distribution of the dataset. The experiments show that the good selection of the loss function has more impact in handling the class imbalance and boosting the detection performance than all the other regularizers with regards to segmentation resolution. Moreover, UNet, the architecture considered as reference, clearly outperforms the networks with no pooling layers both in performance and training time. The authors argue that UNet architectures, compared to the networks with no pooling layers, achieve high detection performance at a very low cost in terms of training time. Therefore, the authors consider such architecture as the state of the art for semantic segmentation of cracks. On the other hand, once computational cost is not an issue anymore thanks to constant improvements of technology, the application of networks without pooling layers might become attractive again because of their simplicity of and high performance.