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Aiming at the problem that the traditional retinal blood vessel segmentation network leads to the loss of small feature information and the low sensitivity of network segmentation with the deepening of the network depth, An asymmetric retinal vessel segmentation structure is proposed, which is different from the traditional symmetric encoder-decoder module. The amount of network weight parameters is 7.2 MB, and the residual attention module and the multi-scale dilated convolution module are used as the basic feature extraction modules. The maximum number of channel layers of the feature map is only 64 layers, and the size of the feature map is halved and the deconvolution operation is only twice, which can reduce the information loss phenomenon caused by the change of feature map size. The test accuracy of the proposed method on the DRIVE and CHASE-DB1 datasets is 96.85% and 97.39%, respectively, the sensitivity is 84.03% and 86.50%, the specificity is 98.08% and 98.12%, and the AUC is 98.63%, respectively and 98.99%.