Pavement crack detection based on local enhancement attention mechanism and deep semantic-guided multi-feature fusion

Cracks, as a typical type of road defect, are important to be detected and repaired in time for transportation maintenance. However, fine-grained crack detection remains a challenge because of the slender shape, backdrop interference, and noise. We propose an automatic fine-grained crack detection algorithm based on a local enhancement self-attention mechanism and a deep semantic-guided multi-scale fusion strategy. The proposed network is built around an encoder–decoder framework. Inspired by the characteristic of global integrity and local continuity, we employ a Local Enhancement Self-Attention Block (LE Self-AB) in the encoder part. It captures the overall trend of cracks from a global perspective while focusing on the local details in the crack regions. The decoder network adopts a deep semantic-guided multi-scale fusion strategy. This strategy enables the model to benefit from both global and local information through cross-scale feature transfer and fusion. To validate the effectiveness and reliability of our approach, training and evaluation are conducted on the CrackTree260 dataset. The generalization of the model is tested on the CRKWH100 and CrackLS315 datasets. The experimental results demonstrate that the proposed method outperforms other advanced crack detection methods with an Optimal Dataset Scale of 0.919, 0.918, and 0.894 on three datasets, respectively.