The　automatic　detection　of　pavement　cracks　can　significantly　improve　the　efficiency　of　road　maintenance　for　pavement　engineers.　At　present,　the　Artificial　Intelligence-based　pavement　crack　detection　may　have　the　problems　of　insufficient　training　dataset　or　large　dataset　of　pavement　images　that　require　high　costs　for　manual　classification　and　labeling.　To　solve　these　problems,　based　on　the　small-scale　pavement　image　dataset,　a　study　on　the　applications　of　the　supervised　and　unsupervised　deep　learning　models　using　the　convolutional　auto-encoder　(CAE)　method　was　conducted　to　identify　different　pavement　objects,　including　the　pavement　cracks.　Based　on　the　traditional　data　augmentation　using　geometry　transformation,　comparison　tests　based　on　different　data　augmentation　methods　were　conducted　to　validate　the　accuracy　of　the　proposed　research.　Considering　the　problems　that　the　background　of　the　asphalt　pavement　crack　image　is　dark,　the　crack　characteristics　are　not　clear,　and　the　unsupervised　clustering　is　difficult,　a　deep　clustering　algorithm　DCEC　(deep　convolutional　embedded　clustering)　based　on　CAE　pre-training　is　proposed　to　study　the　road　images.　Test　results　show　that:　after　100　iterations　of　DenseNet　network　training,　under　the　same　test　set,　the　test　accuracy　of　network　classification　based　on　the　original　data　set　is　78.43%,　the　test　accuracy　based　on　the　traditional　data　augmentation　using　image　transformation　method　is　83.44%,　and　the　test　accuracy　based　on　the　method　proposed　in　this　study　is　87.19%.　It　can　also　be　found　that,　under　the　same　dataset　sample　size,　compared　with　the　traditional　data　augmentation　methods　such　as　geometric　transformation　and　pixel　color　transformation,　the　data　augmentation　method　using　CAE　reconstruction　has　a　higher　recognition　accuracy.　Results　show　that　the　CAE　data　augmentation　method　is　more　easily　affected　by　the　quality　and　sample　size　of　the　training　data　set.　After　the　data　set　is　augmented　by　the　traditional　method,　CAE　learning　is　then　carried　out,　and　the　reconstructed　image　sample　is　more　easily　recognized.　Compared　with　the　traditional　method,　the　DCEC　deep　clustering　method　can　improve　the　accuracy　of　clustering　by　about　10%,　which　preliminarily　realizes　the　end-to-end　intelligent　recognition　of　road　targets　without　manual　annotation.　©　2020,　Editorial　Department　of　China　Journal　of　Highway　and　Transport.　All　right　reserved.