Automatic crack segmentation network based on large kernel pooling transformer

Abstract

Automatic crack segmentation is crucial for ensuring the safe and stable operation of civil concrete buildings. However, due to the irregularity of cracks, low image quality, and complex background environment, automatic crack segmentation on concrete building surfaces still faces significant challenges. To address these issues, an automatic segmentation network (LKT-Net) based on a large kernel pooling Transformer is proposed, aiming to improve the comprehensiveness and accuracy of crack feature extraction while maintaining a lightweight design. First, the large kernel pooling Transformer (LKT) is proposed as the fundamental building block of LKT-Net, which combines large kernel convolution with pooling layers and attention mechanisms to effectively enhance global perception and capture local details at a lower computational cost. To extract edge information accurately, the Feedforward network is improved by integrating the Laplacian operator with multi-scale convolutions, thereby enhancing multiscale edge detection capabilities. Finally, to mitigate information loss during downsampling, we propose a feature enhancement module (FEM) to replace traditional skip-connections, thereby enhancing cross-level feature interactions. The experimental results showed that on three public datasets (DeepCrack537, CrackLS315, and CrackTree260), compared with eight advanced networks, LKT-Net achieved mean Intersection over Union (mIoU) scores of 86.23%, 70.82%, and 83.67%, respectively, demonstrating excellent segmentation performance. The codes are available at: https://github.com/wjxcsust2024/LKT-Net.