In　fundus　images,　precisely　segmenting　retinal　blood　vessels　is　important　for　diagnosing　eye-related　conditions,　such　as　diabetic　retinopathy　and　hypertensive　retinopathy　or　other　eye-related　disorders.　In　this　work,　we　propose　an　enhanced　U-shaped　network　with　dual-attention,　named　DAU-Net,　divided　into　encoder　and　decoder　parts.　Wherein,　we　replace　the　traditional　convolutional　layers　with　ConvNeXt　Block　and　SnakeConv　Block　to　strengthen　its　recognition　ability　for　different　forms　of　blood　vessels　while　lightweight　the　model.　Additionally,　we　designed　two　efficient　attention　modules,　namely　Local-Global　Attention　(LGA)　and　Cross-Fusion　Attention　(CFA).　Specifically,　LGA　conducts　attention　calculations　on　the　features　extracted　by　the　encoder　to　accentuate　vessel-related　characteristics　while　suppressing　irrelevant　background　information;　CFA　addresses　potential　information　loss　during　feature　extraction　by　globally　modeling　pixel　interactions　between　encoder　and　decoder　features.　Comprehensive　experiments　in　terms　of　public　datasets　DRIVE,　CHASE_DB1,　and　STARE　demonstrate　that　DAU-Net　obtains　excellent　segmentation　results　on　all　three　datasets.　The　results　show　an　AUC　of　0.9818,　ACC　of　0.8299,　and　F1　score　of　0.9585　on　DRIVE;　0.9894,　0.8499,　and　0.9700　on　CHASE_DB1;　and　0.9908,　0.8620,　and　0.9712　on　STARE,　respectively.　These　results　strongly　demonstrate　the　effectiveness　of　DAU-Net　in　retinal　vessel　segmentation,　highlighting　its　potential　for　practical　clinical　use.