LTGGNet:Concrete Crack Detection Network Based on Local Topology and Global Grouping

Crack detection is an important part of building structural health monitoring. However, the traditional convolution is difficult to capture the characteristics of tiny concrete cracks and the complex topology, which ultimately leads to the misclassification of crack segmentation. To address the above problems, a concrete crack detection network based on local topology and global group attention (LTGGNet) is proposed. In the encoding part, a deformable local attention module (DLA) is designed to improve the ability of the network to extract tiny crack features and complex topologies. It uses deformable convolution to extract the crack regional features and topology information, and strengthens the network’s discriminability of the tiny crack pixel features. In the decoding part, a global grouping context aggregation (GGC) module is proposed to enhance the global modeling ability of the network to capture images of crack structures. It uses the self-attention mechanism to establish the correlation representation of the global information and enhance the long-range dependency of the topology, thereby achieving the accurate localization of cracks and the recovery of details. The experimental results show that the proposed network achieves 75.84%, 86.26%, 59.20%, and 74.37% in the metrics IoU and F1-Score, respectively, on the public crack datasets Deepcrack and Crack500. Compared to the comparison network, the proposed network has a better concrete crack segmentation effect. The ablation experiments further verify the effectiveness of the network.