{"id":"https://openalex.org/W7147741029","doi":"https://doi.org/10.1109/cnml68938.2026.11452318","title":"Building Surface Crack Detection Based on Deep Convolutional Neural Networks and Ensemble Learning","display_name":"Building Surface Crack Detection Based on Deep Convolutional Neural Networks and Ensemble Learning","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147741029","doi":"https://doi.org/10.1109/cnml68938.2026.11452318"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"},"type":"article","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/A5132550259","display_name":"Yichi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yichi Li","raw_affiliation_strings":["Sichuan University,School of Architecture and Environment,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Sichuan University,School of Architecture and Environment,Chengdu,China","institution_ids":["https://openalex.org/I24185976","https://openalex.org/I4210125143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5132550259"],"corresponding_institution_ids":["https://openalex.org/I24185976","https://openalex.org/I4210125143"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93866002,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"560","last_page":"564"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.98089998960495,"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":0.98089998960495,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11850","display_name":"Concrete Corrosion and Durability","score":0.0015999999595806003,"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/ensemble-learning","display_name":"Ensemble learning","score":0.7102000117301941},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6794000267982483},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5748999714851379},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5252000093460083},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5189999938011169},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4320000112056732},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4205999970436096},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.4115999937057495}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7103000283241272},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7102000117301941},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6794000267982483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.640500009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.583899974822998},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5748999714851379},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5252000093460083},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5189999938011169},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4205999970436096},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4115999937057495},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3384000062942505},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2653999924659729}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2089109306","https://openalex.org/W2992308087","https://openalex.org/W3026226589","https://openalex.org/W4307823382","https://openalex.org/W4352994031"],"related_works":[],"abstract_inverted_index":{"Cracks":[0],"on":[1,39],"the":[2,31,49,160,179],"surface":[3],"of":[4,34,82,145,163,181],"building":[5],"structures":[6],"are":[7,21,115,125],"key":[8],"indicators":[9],"for":[10,201],"evaluating":[11],"their":[12,112],"health":[13],"and":[14,24,71,76,92,111,118,137,152,170,185,197],"safety":[15,26],"status.":[16],"Traditional":[17],"manual":[18],"detection":[19,36,190],"methods":[20,105],"inefficient,":[22],"subjective,":[23],"involve":[25],"risks,":[27],"which":[28],"have":[29],"driven":[30],"rapid":[32],"development":[33],"automated":[35],"technologies":[37],"based":[38],"computer":[40],"vision.":[41],"This":[42,174],"paper":[43],"conducts":[44],"systematic":[45],"research":[46],"to":[47],"address":[48],"challenges":[50],"faced":[51],"by":[52],"existing":[53],"deep":[54],"learning":[55,130,187],"models":[56,169],"in":[57,66,188],"crack":[58,69,189],"detection,":[59],"such":[60,106],"as":[61,107],"complex":[62],"background":[63],"interference,":[64],"difficulty":[65],"extracting":[67],"multi-scale":[68],"features,":[70],"extreme":[72],"imbalance":[73],"between":[74],"positive":[75],"negative":[77],"samples.":[78],"The":[79],"classification":[80],"performance":[81],"six":[83],"Convolutional":[84],"Neural":[85],"Network":[86],"(CNN)":[87],"architectures,":[88],"including":[89,132],"ResNet,":[90],"MobileNet,":[91],"EfficientNet,":[93],"is":[94],"comprehensively":[95],"compared.":[96],"To":[97],"improve":[98],"model":[99],"generalization":[100],"ability,":[101],"advanced":[102,182],"data":[103,183],"augmentation":[104,149,156,184],"MixUp,":[108],"CutMix,":[109],"AugMix,":[110],"combination":[113],"strategies":[114],"systematically":[116],"introduced":[117],"evaluated.":[119],"Furthermore,":[120],"multiple":[121],"superior":[122],"base":[123],"learners":[124],"fused":[126],"through":[127],"various":[128],"ensemble":[129,186],"strategies,":[131],"hard":[133],"voting,":[134,136],"soft":[135],"Stacking.":[138],"Experimental":[139],"results":[140],"show":[141],"that":[142],"Stacking":[143],"integration":[144,172],"ResNet":[146],"with":[147,154],"\"traditional":[148,155],"+":[150,157],"CutMix\"":[151],"MobileNet":[153],"AugMix\"":[158],"achieves":[159],"best":[161],"F1-Score":[162],"0.9544,":[164],"significantly":[165],"outperforming":[166],"all":[167],"single":[168],"voting":[171],"methods.":[173],"study":[175],"not":[176],"only":[177],"verifies":[178],"effectiveness":[180],"tasks":[191],"but":[192],"also":[193],"provides":[194],"a":[195],"high-precision":[196],"robust":[198],"intelligent":[199],"solution":[200],"practical":[202],"engineering":[203],"applications.":[204]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}