Pavement cracks can affect driving safety and service life, thereby increasing the risk of traffic accidents. Therefore, detecting and managing pavement cracks in a timely manner is particularly important. To address the problems of limited receptive field, inability to add location information, and poor effectiveness of traditional convolutional neural networks, a pavement crack segmentation model that integrates multiple attention mechanisms is proposed. ResNeSt and Swin Transformer enhance the information transmission effect of the model for the network to better utilize information at different levels and generate more accurate predictions. Among public online datasets, a dataset with 8251 real road images is used for the experiment, obtaining intersection over union, precision, recall, and F1 score values of 73.24%, 82.84%, 86.12%, and 84.44%, respectively. Although the recall is slightly inferior to that of DeepLab v3+, better performance is exhibited in terms of crack recognition accuracy and robustness in complex road environments.