Due　to　the　rapid　advancement　of　the　transportation　industry　and　the　continual　increase　in　pavement　infrastructure,　it　is　difficult　to　keep　up　with　the　huge　road　maintenance　task　by　relying　only　on　the　traditional　manual　detection　method.　Intelligent　pavement　detection　technology　with　deep　learning　techniques　is　available　for　the　research　and　industry　areas　by　the　gradual　development　of　computer　vision　technology.　Due　to　the　different　characteristics　of　pavement　distress　and　the　uncertainty　of　the　external　environment,　this　kind　of　object　detection　technology　for　distress　classification　and　location　still　faces　great　challenges.　This　paper　discusses　the　development　of　object　detection　technology　and　analyzes　classical　convolutional　neural　network　(CNN)　architecture.　In　addition　to　the　one-stage　and　two-stage　object　detection　frameworks,　object　detection　without　anchor　frames　is　introduced,　which　is　divided　according　to　whether　the　anchor　box　is　used　or　not.　This　paper　also　introduces　attention　mechanisms　based　on　convolutional　neural　networks　and　emphasizes　the　performance　of　these　mechanisms　to　further　enhance　the　accuracy　of　object　recognition.　Lightweight　network　architecture　is　introduced　for　mobile　and　industrial　deployment.　Since　stereo　cameras　and　sensors　are　rapidly　developed,　a　detailed　summary　of　three-dimensional　object　detection　algorithms　is　also　provided.　While　reviewing　the　history　of　the　development　of　object　detection,　the　scope　of　this　review　is　not　only　limited　to　the　area　of　pavement　crack　detection　but　also　guidance　for　researchers　in　related　fields　is　shared.　©　2024　The　Authors