{"id":"https://openalex.org/W3000939100","doi":"https://doi.org/10.1145/3372806.3372816","title":"Automated Detection of Sewer Pipe Defects Based on Cost-Sensitive Convolutional Neural Network","display_name":"Automated Detection of Sewer Pipe Defects Based on Cost-Sensitive Convolutional Neural Network","publication_year":2019,"publication_date":"2019-11-27","ids":{"openalex":"https://openalex.org/W3000939100","doi":"https://doi.org/10.1145/3372806.3372816","mag":"3000939100"},"language":"en","primary_location":{"id":"doi:10.1145/3372806.3372816","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372806.3372816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100650507","display_name":"Yuhan Chen","orcid":"https://orcid.org/0000-0003-3954-1608"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhan Chen","raw_affiliation_strings":["College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080341674","display_name":"Shangping Zhong","orcid":"https://orcid.org/0000-0001-7768-7339"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangping Zhong","raw_affiliation_strings":["College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029471219","display_name":"Kaizhi Chen","orcid":"https://orcid.org/0000-0003-2859-8924"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaizhi Chen","raw_affiliation_strings":["College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069814620","display_name":"Shoulong Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shoulong Chen","raw_affiliation_strings":["College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100770310","display_name":"Song Zheng","orcid":"https://orcid.org/0000-0001-9649-7610"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Zheng","raw_affiliation_strings":["College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.975600004196167,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.741699755191803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7348881363868713},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.5964613556861877},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5773571133613586},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5493646264076233},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5075951814651489},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4946455657482147},{"id":"https://openalex.org/keywords/cost-estimate","display_name":"Cost estimate","score":0.45984068512916565},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4251979887485504},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4106552004814148},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39971715211868286},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18172761797904968}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.741699755191803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7348881363868713},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.5964613556861877},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5773571133613586},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5493646264076233},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5075951814651489},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4946455657482147},{"id":"https://openalex.org/C93983250","wikidata":"https://www.wikidata.org/wiki/Q795053","display_name":"Cost estimate","level":2,"score":0.45984068512916565},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4251979887485504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4106552004814148},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39971715211868286},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18172761797904968},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3372806.3372816","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372806.3372816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W845365781","https://openalex.org/W1989587560","https://openalex.org/W1992733578","https://openalex.org/W1994088605","https://openalex.org/W1994198993","https://openalex.org/W2012035409","https://openalex.org/W2012444155","https://openalex.org/W2015452969","https://openalex.org/W2043414346","https://openalex.org/W2070274273","https://openalex.org/W2107143616","https://openalex.org/W2132562800","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2163352848","https://openalex.org/W2259894692","https://openalex.org/W2314918367","https://openalex.org/W2440599146","https://openalex.org/W2509322645","https://openalex.org/W2769758779","https://openalex.org/W2785860907","https://openalex.org/W2788686737","https://openalex.org/W2792741217","https://openalex.org/W2889035772","https://openalex.org/W2913697492","https://openalex.org/W2962712569","https://openalex.org/W2964050365","https://openalex.org/W6922057760"],"related_works":["https://openalex.org/W4380433113","https://openalex.org/W4386072068","https://openalex.org/W252339960","https://openalex.org/W2390529043","https://openalex.org/W2378320433","https://openalex.org/W2358343511","https://openalex.org/W2071821326","https://openalex.org/W2051877971","https://openalex.org/W1970117064","https://openalex.org/W1787170397"],"abstract_inverted_index":{"Regular":[0],"inspection":[1,173],"and":[2,66,96,132,184],"repair":[3],"of":[4,11,30,40,61,99,125,136,169],"drainage":[5,178],"pipes":[6,179],"is":[7],"an":[8,68],"important":[9],"part":[10],"urban":[12],"construction.":[13],"Currently,":[14],"many":[15],"classification":[16,32,48,162],"methods":[17],"have":[18],"been":[19],"used":[20,181],"for":[21],"defect":[22,51,64,198],"diagnosis":[23],"using":[24],"images":[25,174],"inside":[26],"pipelines.":[27],"However,":[28],"most":[29],"these":[31],"models":[33],"train":[34,183],"the":[35,38,45,56,59,74,84,94,100,106,118,122,126,130,134,137,145,152,161,166,186,192,197,207,216],"classifier":[36],"with":[37,209],"goal":[39],"maximizing":[41],"accuracy":[42,131],"without":[43],"considering":[44],"unequal":[46],"error":[47],"cost":[49,87],"in":[50,144],"diagnosis.":[52],"In":[53],"this":[54],"study,":[55],"authors":[57,107],"analyze":[58],"characteristics":[60],"sewer":[62],"pipeline":[63],"detection":[65,70,199],"design":[67],"automated":[69],"framework":[71],"based":[72],"on":[73],"cost-sensitive":[75,193],"deep":[76],"convolutional":[77],"neural":[78],"network":[79,86,127],"(CNN).":[80],"The":[81,172],"method":[82],"makes":[83],"CNN":[85],"sensitive":[88],"by":[89,139],"introducing":[90],"learning":[91,146],"theories":[92],"at":[93],"structural":[95],"loss":[97,112,155],"levels":[98],"network.":[101,187],"To":[102],"minimize":[103],"misclassification":[104,142,170],"costs,":[105],"propose":[108],"a":[109],"new":[110,153],"auxiliary":[111,154],"function":[113,156],"Cost-Mean":[114,210],"Loss,":[115],"which":[116],"allows":[117],"model":[119,138,208],"to":[120,128,160,164,182,204],"obtain":[121],"original":[123,217],"parameters":[124],"maximize":[129],"improve":[133],"performance":[135,214],"minimizing":[140],"total":[141],"costs":[143],"process.":[147],"Theoretical":[148],"analysis":[149],"shows":[150],"that":[151,190],"can":[157],"be":[158],"applied":[159],"task":[163],"optimize":[165],"expected":[167],"value":[168],"costs.":[171],"collected":[175],"from":[176,202],"multiple":[177],"were":[180],"test":[185],"Results":[188],"show":[189],"after":[191],"strategy":[194],"was":[195],"added,":[196],"rate":[200],"decreased":[201],"2.1%":[203],"0.45%.":[205],"Moreover,":[206],"Loss":[211],"has":[212],"better":[213],"than":[215],"model.":[218]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
