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Cracks are the most common road pavement damage. Due to the propagation of cracks, the detection of early cracks has great practical significance. Traditional manual crack detection is extremely time-consuming and labor-intensive. Researchers have turned their attention to automated crack detection. Although automated crack detection has been extensively researched over the past decades, it is still a challenging task due to the intensity inhomogeneity of cracks and complexity of the pavement environment, e.g. To solve these problems, we propose an efficient pavement crack segmentation model based on deep learning. The model uses pre-trained DenseNet121 as an encoder to extract pavement features. Feature Pyramid Attention module fuses features under different pyramid scales and provides precise pixel-attention. The Global Attention Upsample module which is a combination of convolutional neural network and pyramid module acts as a decoder. The sum of Cross-entropy loss and Dice loss is selected as loss function. We use poly policy to tune learning rate. In order to verify the effectiveness of the proposed method, we conduct training and testing on the Crack500 dataset and MCD dataset. Our method achieves a Dice coefficient of 0.7681, an IoU of 0.6235 on the Crack500 dataset and 0.6909, 0.5278 on the MCD dataset. We perform ablation study to verify the effectiveness of the loss function on improving the performance of our model.