In　view　of　the　problem　that　the　number　of　pavement　crack　images　cannot　meet　basic　needs　for　deep　learning,according　to　using　generative　adversary　network　to　expand　the　data-set,a　pavement　segmentation　algorithm　based　on　the　U-Net　network　is　proposed.　Firstly,the　data-set　was　initially　expanded　by　traditional　image　generation,according　to　the　principle　of　generative　adversary　network,a　algorithm　of　pavement　crack　segmentation　based　on　semantic　segmentation　was　proposed,which　was　used　to　expand　the　data-set　again.　Secondly,based　on　the　U-Net,an　algorithm　of　pavement　crack　segmentation　based　on　semantic　segmentation　was　proposed,which　increased　the　number　of　network　layers　and　added　Batch　Normalization　and　dropout　layer.　Finally,the　semantic　segmentation　model　of　pavement　cracks　was　used　to　extract　cracks　in　the　expanded　data　image,and　compared　with　the　traditional　detection　algorithm　and　the　existing　mainstream　segmentation　algorithm　FCN.　The　results　show　that　the　segmentation　accuracy　of　the　algorithm　is　better　than　other　two　algorithms,which　more　precisely　segments　pavement　crack　images　and　avoids　error　detection　when　background　pixel　is　complicated.　The　mean　pixel　accuracy　and　mean　intersection　over　union　of　the　algorithm　are　92.43%　and　83.43%,respectively.　In　the　practical　scene　application,it　has　better　detection　effect　and　stronger　generalization　performance.　©　2023　Editorial　Board　of　Jilin　University.　All　rights　reserved.