Abstract: Cracks are the most important type of pavement diseases, and the accurate crack segmentation is an important decision basis for national preventive maintenance management of roads. To address the problem of crack segmentation accuracy of existing models for pavement under complex background, an end-to-end crack segmentation model based on convolutional neural network was proposed, which used a layered structure of ConvNeXt encoder to extract multi-scale features. A pyramid pooling module was used to further obtain the global priori features by the top layer of features, and the feature fusion was performed through a pyramid structure with lateral connections and top-down. A weighted cross-entropy loss function was employed to enhance the detection performance of model for the crack and background imbalance problem. In addition, a crack dataset UCrack with 2 876 cracks covering multiple crack types and a wide range of backgrounds was created to provide rich features for model learning. Experiments show that, compared with other best-performing models, the model recall and F1 score on the UCrack test dataset are improved by 2.68% and 6.89%, respectively. The test on the CrackDataset dataset achieves recall of 85.68% and F1 score of 80.11%, which implies that the model has better generalization capability and can cope with pavement crack segmentation with complicated scenarios.