Automatic　crack　detection　from　pavement　images　is　an　import　research　field.　Meanwhile,　crack　detection　is　a　challenge　task:　(1)　manual　labels　are　subjective　because　of　low　contrast　between　crack　and　the　surrounding　pavement　and　heavy　workload;　(2)　the　excessive　dependence　of　supervised　deep　learning　training　on　labels.　To　address　these　problems,　we　present　an　unsupervised　method　for　learning　mapping　to　translate　crack　images　to　binary　images　based　on　generative　adversarial　network.　We　introduce　the　cyclic　consistent　loss　to　increase　accuracy　of　crack　localization.　Eight　residual　blocks　connected　convolutional　neural　network　for　feature　extraction　is　used　as　generator　and　a　5-layer　fully　convolutional　network　is　used　as　discriminator.　We　analyze　the　proposed　framework　and　provide　qualitative　and　quantitative　comparison.　The　experimental　results　show　that　the　proposed　method　achieves　a　better　performance　than　several　existing　methods.　©　2020　ACM.