UTE-CrackNet: transformer-guided and edge feature extraction U-shaped road crack image segmentation

Cracks in the road surface can cause significant harm. Road crack detection, segmentation, and immediate repair can help reduce the occurrence of risks. Some methods based on convolutional neural networks still have some problems, such as fuzzy edge information, small receptive fields, and insufficient perception ability of local information. To solve the above problems, this paper offers UTE-CrackNet, a novel road crack segmentation network that attempts to increase the generalization ability and segmentation accuracy of road crack segmentation networks. To begin, our design combines the U-shaped structure that enables the model to learn more features. Given the lack of skip connections, we designed the multi-convolution coordinate attention block to reduce semantic differences in cascaded features and the gated residual attention block to get more local features. Because most fractures have strip characteristics, we propose the transformer edge atlas spatial pyramid pooling module, which innovatively applies the transformer module and edge detection module to the network so that the network can better capture the edge information and context information of the fracture area. In addition, we use focus loss in training to solve the problem of positive and negative sample imbalances. Experiments were conducted on four publicly available road crack segmentation datasets: Rissbilder, GAPS384, CFD, and CrackTree200. The experimental results reveal that the network outperforms the standard road fracture segmentation models. The code and models are publicly available at https://github.com/mushan0929/UTE-crackNet.