As　one　of　the　most　important　properties　of　road,　the　adaption　to　roads　with　different　macro-textures　may　significantly　affect　the　autonomous　driving　technologies　since　road　texture　directly　affects　the　skidding　resistance　and　tire　noise.　Therefore,　it　is　of　great　significance　to　detect　and　analyze　the　road　macro　texture　with　respect　to　different　pavement　types　and　service　conditions.　Generally,　transportation　engineers　may　face　problems　such　as　small　dataset　size,　unbalanced　dataset,　etc.　To　solve　these　problems,　this　study　aims　to　recognize　the　pavement　texture　using　the　deep　learning　approaches.　The　pavement　texture　data　was　first　visualized　using　image　processing　methods,　and　then　augmented　using　the　traditional　methods　as　well　as　a　deep　learning　approach,　i.e.　Generative　Adversarial　Network　(GAN)　model.　The　Random　Forest　(RF)　algorithm　and　the　DenseNet　network　were　both　employed,　where　the　overall　classification　accuracy　of　the　original　dataset　was　50%　and　59%,　respectively,　and　the　accuracy　of　the　data　augmented　by　the　traditional　methods　was　58%　and　70%,　respectively.　Test　results　show　that,　after　250,000　generations　of　training,　GAN　model　was　able　to　generate　new　pavement　texture　images　with　high　quality,　and　the　classification　accuracy　on　the　test　dataset　using　DenseNet　improved　to　82%.　It　was　discovered　that　the　deep　learning　methods　had　a　better　performance　for　pavement　texture　recognition　than　manual　classification　and　traditional　machine　learning　methods.　Furthermore,　it　was　also　found　that　adding　noise　in　the　original　datasets　as　an　augmentation　method　had　a　negative　impact　on　the　classification　accuracy.