Regular　inspection　and　repair　of　drainage　pipes　is　an　important　part　of　urban　construction.　Currently,　many　classification　methods　have　been　used　for　defect　diagnosis　using　images　inside　pipelines.　However,　most　of　these　classification　models　train　the　classifier　with　the　goal　of　maximizing　accuracy　without　considering　the　unequal　error　classification　cost　in　defect　diagnosis.　In　this　study,　the　authors　analyze　the　characteristics　of　sewer　pipeline　defect　detection　and　design　an　automated　detection　framework　based　on　the　cost-sensitive　deep　convolutional　neural　network　(CNN).　The　method　makes　the　CNN　network　cost　sensitive　by　introducing　learning　theories　at　the　structural　and　loss　levels　of　the　network.　To　minimize　misclassification　costs,　the　authors　propose　a　new　auxiliary　loss　function　Cost-Mean　Loss,　which　allows　the　model　to　obtain　the　original　parameters　of　the　network　to　maximize　the　accuracy　and　improve　the　performance　of　the　model　by　minimizing　total　misclassification　costs　in　the　learning　process.　Theoretical　analysis　shows　that　the　new　auxiliary　loss　function　can　be　applied　to　the　classification　task　to　optimize　the　expected　value　of　misclassification　costs.　The　inspection　images　collected　from　multiple　drainage　pipes　were　used　to　train　and　test　the　network.　Results　show　that　after　the　cost-sensitive　strategy　was　added,　the　defect　detection　rate　decreased　from　2.1%　to　0.45%.　Moreover,　the　model　with　Cost-Mean　Loss　has　better　performance　than　the　original　model.　©　2019　Association　for　Computing　Machinery.