Crack　recognition　is　important　in　periodic　pavement　inspection　and　maintenance.　The　wide　application　of　image　recognition　technology　in　daily　inspection　and　maintenance　makes　the　health　monitoring　of　asphalt　pavement　defects　more　effective,　both　intelligently　and　sustainably.　In　this　study,　a　mobile　automatic　system　integrating　fifth-generation　wireless　communication　technology　(5G),　cloud　computing,　and　artificial　intelligence　(AI)　was　proposed　for　transportation　infrastructure　object　recognition.　The　original　dataset　contained　344　images　of　pavement　defects,　including　longitudinal　cracks,　transverse　cracks,　alligator　cracks,　and　broken　road　markings.　Three　lightweight　algorithms　for　automatic　pavement　crack　identification　were　used　and　compared,　including　MobileNetV2,　ShuffleNetV2,　and　Res-Net50　networks,　respectively.　The　results　showed　that　the　model　based　on　ShuffieNetV2　achieved　the　best　overall　predictive　accuracy　(ACC　=　95.52　percent).　A　mobile　automatic　monitoring　system　based　on　the　cloud　platform　and　Android　framework　was　then　established.　With　the　help　of　5G　technology,　the　cloud-network-terminal＇　interconnection　can　be　achieved　to　provide　fast　and　stable　information　transmission　between　transportation　infrastructure　and　road　users.　The　proposed　system　provides　an　engineering　reference　for　the　transportation　infrastructure　inspection　and　maintenance　using　the　5G　communication　technology.