To ensure road quality and safety， automated crack detection is crucial for the maintenance of concrete pavement. To address the issue of pixel information loss caused by excessive down-sampling in the existing deep learning-based crack detection methods， a concrete crack detection network based on progressive context interaction and attention mechanisms was proposed. Firstly， with an optimized UNet++ as the backbone， asymmetric convolution blocks were applied to enhance feature extraction ability. Secondly， Progressive Context Interaction Mechanism （PCIM） was introduced to capture and fuse multi-scale features of adjacent feature maps efficiently. Thirdly， in the feature enhancement phase， the Attention Combination （AC） approach was used to improve feature representation capability. Finally， in the feature fusion phase， a Multi-Semantic Attention Dynamic Fusion Module （MADFM） was utilized to enhance detail recovery and retention effects. Test results on three public datasets show that compared to DeepCrack， CrackFormer， and PAF-Net （Progressive and Adaptive feature Fusion Network）， the proposed network achieves superior performance. Specifically， the proposed network has the F-score improved by 1.33， 5.07， and 3.93 percentage points， respectively， on the DeepCrack test set； enhanced by 3.04， 4.35， and 0.82 percentage points， respectively， on the Crack500 test set； and increased by 3.03， 6.00， and 4.73 percentage points， respectively， on the CFD test set. These results verify fully that the proposed network achieves enhanced accuracy in crack detection and has excellent robust performance on different test sets.