Vision　monitoring　of　distress　has　emerged　as　a　new　trend　in　intelligent　transportation　infrastructure　systems,　including　roads　and　bridges.　Recently,　transfer　learning　methods　and　lightweight　networks　have　been　used　to　realize　efficient　distress　identification　without　large　amount　of　human-labor　work.　This　paper　proposed　an　engineering　approach　that　integrated　transfer　learning　with　lightweight　models　to　classify　and　detect　concrete　bridge　distresses.　Two　datasets　named　Distress　Dataset　of　Asphalt　Pavement　(DDAP)　with　2500　asphalt　pavement　distresses　images　and　Distress　Dataset　of　Concrete　Bridge　(DDCB)　with　906　concrete　bridge　distresses　images　were　used.　The　lightweight　models　MobileNet　and　MobileNet-SSD　were　employed　to　conduct　6　comparative　experiments　for　exploring　the　model　performance　with　different　transfer　learning　modes　(Mode　I　and　II)　and　procedures　(one-step　and　two-step　procedure)　in　classification　and　detection　tasks.　Based　on　the　comparison　of　the　results,　the　optimum　model　for　two　tasks　was　respectively　recognized.　For　classification　task,　the　model　directly　transferred　specific　parameters　of　partial　convolution　layers　from　ImageNet-based　model　achieved　the　highest　accuracy　of　97.8%.　For　detection　task,　the　two-step　transfer　learning　model　using　an　intermediate　transfer　learning　step　trained　by　DDAP　reached　a　mean　average　precision　of　87.16%.　The　proposed　approach　has　the　application　potential　for　practical　road　inspection　work　in　intelligent　transportation　infrastructure　maintenance.