{"id":"https://openalex.org/W3147027508","doi":"https://doi.org/10.1145/3445815.3445842","title":"Automatic Sewer Cracks Localization using Deformable Bounding Boxes","display_name":"Automatic Sewer Cracks Localization using Deformable Bounding Boxes","publication_year":2020,"publication_date":"2020-12-11","ids":{"openalex":"https://openalex.org/W3147027508","doi":"https://doi.org/10.1145/3445815.3445842","mag":"3147027508"},"language":"en","primary_location":{"id":"doi:10.1145/3445815.3445842","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3445815.3445842","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 4th International Conference on Computer Science and Artificial Intelligence","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/A5022108337","display_name":"Muhammad Umar Zeshan","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muhammad Umar Zeshan","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101975457","display_name":"Gang Pan","orcid":"https://orcid.org/0000-0003-2155-4689"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pan Gang","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":1.2502,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.80230562,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"159","last_page":"167"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":1.0,"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":1.0,"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/T12233","display_name":"Geotechnical Engineering and Underground Structures","score":0.9958999752998352,"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":0.994700014591217,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7094619870185852},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.686704158782959},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6682249307632446},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6076024770736694},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5379311442375183},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5202454328536987},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4933549463748932},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4842509925365448},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.44767463207244873},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.4384848475456238},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3789772391319275},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3367448151111603},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19774672389030457},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.10072353482246399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7094619870185852},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.686704158782959},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6682249307632446},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6076024770736694},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5379311442375183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5202454328536987},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4933549463748932},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4842509925365448},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.44767463207244873},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.4384848475456238},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3789772391319275},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3367448151111603},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19774672389030457},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.10072353482246399},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3445815.3445842","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3445815.3445842","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 4th International Conference on Computer Science and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/6"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1989587560","https://openalex.org/W1994198993","https://openalex.org/W2029400316","https://openalex.org/W2031489346","https://openalex.org/W2057221234","https://openalex.org/W2086991944","https://openalex.org/W2088282794","https://openalex.org/W2093462724","https://openalex.org/W2097376151","https://openalex.org/W2106238010","https://openalex.org/W2140694235","https://openalex.org/W2145295358","https://openalex.org/W2170737910","https://openalex.org/W2561081145","https://openalex.org/W2590209538","https://openalex.org/W2598457882","https://openalex.org/W2793707857","https://openalex.org/W2913697492","https://openalex.org/W2914584698","https://openalex.org/W2982512126"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W3166204570","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"The":[0,18,146,195,247,306],"underground":[1,19],"sewage":[2,20],"system":[3,21],"is":[4,22,100,183,193],"a":[5,23,77,89,123,134,169,209,214,223],"crucial":[6,24],"part":[7,25],"of":[8,11,26,29,40,53,62,73,79,105,142,153,189,213,231,268,304],"the":[9,16,27,34,38,51,60,70,140,150,163,174,187,199,229,241,266,269,309],"infrastructure":[10,28],"any":[12,30],"modern":[13,31],"municipality":[14,32],"across":[15,33,117],"globe.":[17,35],"However,":[36],"with":[37,217,234,250,256,276,317],"passage":[39,61],"time,":[41],"sewer":[42],"pipes":[43,75,108,113],"systematically":[44],"become":[45],"prone":[46],"to":[47,84,161,239],"distortion":[48],"depending":[49],"upon":[50],"degree":[52],"usage":[54],"and":[55,92,126,165,204,262,282,292],"location":[56],"factors":[57],"such":[58],"as":[59,82,206],"heavy":[63],"traffic":[64],"nearby":[65],"or":[66],"deep-rooted":[67],"trees.":[68],"So,":[69],"regular":[71],"inspection":[72,99,121],"these":[74,111],"requires":[76],"lot":[78],"technical":[80],"attention":[81,220],"failure":[83],"do":[85],"so":[86],"can":[87,227],"cause":[88],"huge":[90],"financial":[91],"environmental":[93],"disaster.":[94],"In":[95,129],"this":[96,130],"regard,":[97],"CCTV":[98],"considered":[101],"an":[102],"effective":[103,177],"way":[104],"monitoring":[106],"buried":[107],"worldwide.":[109],"As":[110],"drainage":[112],"are":[114,274,296,315],"extensively":[115],"spread":[116],"several":[118],"miles,":[119],"their":[120],"becomes":[122],"time-consuming,":[124],"tiring,":[125],"expensive":[127],"task.":[128],"study,":[131],"we":[132],"propose":[133],"crack":[135,253],"localization":[136],"framework":[137,147],"by":[138,156],"taking":[139],"advantages":[141],"deep":[143],"learning":[144],"algorithms.":[145],"focuses":[148],"on":[149],"proper":[151],"organization":[152],"input":[154,278],"data":[155],"using":[157],"deformable":[158],"bounding":[159,279],"boxes":[160],"label":[162],"images":[164,259,311],"prepare":[166],"them":[167],"in":[168,222,298,312],"suitable":[170],"format":[171],"for":[172,260],"training":[173],"model.":[175],"An":[176],"Convolutional":[178],"Neural":[179],"Network":[180],"(CNN)":[181],"model":[182,197,248,300,307],"proposed":[184,196],"which":[185,299,313],"addresses":[186],"problems":[188],"detecting":[190],"arbitrary-shaped":[191],"objects,":[192],"implemented.":[194],"provides":[198],"best":[200],"tradeoff":[201],"between":[202],"speed":[203],"accuracy":[205],"it":[207],"uses":[208],"segmentation":[210],"head":[211],"consisting":[212],"convolutional":[215],"block":[216],"two":[218],"pivotal":[219],"modules":[221],"sequential":[224],"manner":[225],"that":[226],"enhance":[228],"quality":[230],"extracted":[232],"features":[233],"very":[235],"low":[236],"computational":[237],"cost,":[238],"support":[240],"lightweight":[242],"ResNet":[243],"backbone":[244],"detection":[245],"algorithm.":[246],"trained":[249],"3150":[251],"unique":[252],"image":[254],"samples,":[255],"700":[257],"extra":[258],"validation":[261],"testing.":[263],"To":[264],"determine":[265],"performance":[267],"model,":[270],"final":[271],"ground-truth":[272],"results":[273],"compared":[275],"testing":[277],"box":[280],"values":[281],"other":[283],"state-of-the-art":[284],"quantitative":[285],"metrics":[286],"e.g.":[287],"Precision":[288,294],"\u00d7":[289],"Recall":[290],"curve":[291],"Average":[293],"(AP)":[295],"explained":[297],"showed":[301,308],"AP":[302],"value":[303],"80.06%.":[305],"output":[310],"cracks":[314],"localized":[316],"excellent":[318],"accuracy.":[319]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
