Work

An improved U-Net based method for predicting cable tunnel cracks

Year: 2025

Type: article

Abstract: To rapidly and accurately predict cable tunnel crack, a method based on improved U-Net model was proposed in this research. The new semantic segmentation network called IRHC U-Net is developed based on U-Net, which effectively integrates Inception-residual convolution (IRC) module, hybrid attention (HA) module, hybrid dilated convolution (HDC) module, and cross-level gathering decoder (CLGD) module. The IRC module can increase the network depth and width, and solve the problem of the gradient decreasing; The HA module highlights the cracks of edge features; The HDC module can obtain the more detail information in the en-coder; The CLGD module narrows the semantic gap between encoder and decoder. The prediction results of cable tunnel cracks showed that the improved U-Net model outperformed the other models. The mean intersection over union (MIOU), Accuracy, and Precision of the improved U-Net model on the cable tunnel cracks dataset were 84.79%, 99.82%, and 91.35%, respectively. The results indicate that the improved U-Net model can accurately predict cable tunnel crack. This study provides technical support for the rapid and accurate segmentation of cable tunnel cracks.

Source: Array

DOI: 10.1016/j.array.2025.100421