CrackT-net: a method of convolutional neural network and transformer for crack segmentation

Automatic crack segmentation plays an important and challenging role in pavement maintenance. In recent years, researchers have been trying to figure out the task that long dependencies and global context information could not get well established using convolutional neural networks. A method of convolutional neural network (CNN) and transformer called CrackT-net is proposed to address this issue. In the aspect of the backbone network, we propose a new backbone network named richer features (RF) UNet++, in which skip connections, gated channel transformation, and polarized self-attention are added to the UNet++ to enhance feature representation capabilities. Then, to capture more long dependencies and global context information, the last feature extraction layer is replaced by the transformer in our network. In the deep supervision module, the proposed module can progressively polish the multilevel features to be more accurate. To prove the effectiveness of our proposed method, we evaluate it on the three public crack datasets, DeepCrack, CFD, and Crack500, which achieves F-score (F-1) values of 0.856, 0.700, and 0.637, respectively. After guided filtering, our method achieves F-1 values of 0.859, 0.710, and 0.637 on these three datasets. (C) 2022 SPIE and IS&T