{"id":"https://openalex.org/W4404788196","doi":"https://doi.org/10.1109/tits.2024.3500030","title":"An Unsupervised Learning Approach for Pavement Distress Diagnosis via Siamese Networks","display_name":"An Unsupervised Learning Approach for Pavement Distress Diagnosis via Siamese Networks","publication_year":2024,"publication_date":"2024-11-27","ids":{"openalex":"https://openalex.org/W4404788196","doi":"https://doi.org/10.1109/tits.2024.3500030"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3500030","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3500030","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5076915758","display_name":"Ruiqi Ren","orcid":"https://orcid.org/0000-0002-6225-6605"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruiqi Ren","raw_affiliation_strings":["School of Rail Transportation, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011394027","display_name":"Peixin Shi","orcid":"https://orcid.org/0000-0001-8432-4915"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peixin Shi","raw_affiliation_strings":["School of Rail Transportation, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077223689","display_name":"Pengjiao Jia","orcid":"https://orcid.org/0000-0001-6605-793X"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengjiao Jia","raw_affiliation_strings":["School of Rail Transportation, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100434594","display_name":"Jinwoo Kim","orcid":"https://orcid.org/0000-0002-2237-4965"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinwoo Kim","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Hanyang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Hanyang University, Seoul, South Korea","institution_ids":["https://openalex.org/I4575257"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5076915758"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.5265,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65500265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"26","issue":"2","first_page":"1876","last_page":"1888"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9947999715805054,"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":0.9947999715805054,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9682999849319458,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.515338122844696},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5136229395866394},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5107276439666748},{"id":"https://openalex.org/keywords/distress","display_name":"Distress","score":0.44713374972343445},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4262502193450928},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3973746597766876},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3302925229072571},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.20523211359977722},{"id":"https://openalex.org/keywords/psychotherapist","display_name":"Psychotherapist","score":0.08152765035629272}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.515338122844696},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5136229395866394},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5107276439666748},{"id":"https://openalex.org/C139265228","wikidata":"https://www.wikidata.org/wiki/Q5283089","display_name":"Distress","level":2,"score":0.44713374972343445},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4262502193450928},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3973746597766876},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3302925229072571},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.20523211359977722},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.08152765035629272}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2024.3500030","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3500030","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4699999988079071}],"awards":[{"id":"https://openalex.org/G1104385578","display_name":null,"funder_award_id":"52278405","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1960097882","https://openalex.org/W2029184015","https://openalex.org/W2097886433","https://openalex.org/W2144801789","https://openalex.org/W2194775991","https://openalex.org/W2582691673","https://openalex.org/W2626688319","https://openalex.org/W2913939497","https://openalex.org/W2914570111","https://openalex.org/W2962858109","https://openalex.org/W2963045681","https://openalex.org/W2964308596","https://openalex.org/W2981988113","https://openalex.org/W3010219363","https://openalex.org/W3022492425","https://openalex.org/W3035253074","https://openalex.org/W3035524453","https://openalex.org/W3109771882","https://openalex.org/W3112350297","https://openalex.org/W3133297714","https://openalex.org/W3138786441","https://openalex.org/W3145450063","https://openalex.org/W3159481202","https://openalex.org/W3166166117","https://openalex.org/W3171007011","https://openalex.org/W3192782092","https://openalex.org/W3201890785","https://openalex.org/W3203168750","https://openalex.org/W3203607638","https://openalex.org/W4210746697","https://openalex.org/W4229051684","https://openalex.org/W4281750281","https://openalex.org/W4285254182","https://openalex.org/W4288391486","https://openalex.org/W4312041865","https://openalex.org/W4313156423","https://openalex.org/W4320518953","https://openalex.org/W4386352556","https://openalex.org/W4389107895","https://openalex.org/W4393906060","https://openalex.org/W4399342419","https://openalex.org/W6779997284","https://openalex.org/W6796761347","https://openalex.org/W6803919275","https://openalex.org/W6840223274","https://openalex.org/W6840913410"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Accurate,":[0],"automated":[1],"diagnosis":[2],"of":[3,92,107,170],"pavement":[4,44,60,163],"distress":[5,61,93,109],"is":[6],"essential":[7],"for":[8,39,59,161],"effective":[9],"roadway":[10],"maintenance":[11,164],"but":[12],"presents":[13],"considerable":[14],"challenges.":[15],"Supervised":[16],"learning":[17,29,121,174],"methods":[18],"are":[19,31,98],"constrained":[20],"by":[21],"limited":[22],"labeled":[23,153],"data,":[24],"while":[25],"existing":[26,118],"unsupervised":[27,57,119],"representation":[28,120],"approaches":[30],"difficult":[32],"to":[33,100,129,149],"capture":[34],"the":[35,152,168],"fine-grained":[36,90],"details":[37],"needed":[38],"precise":[40],"pixel-level":[41],"segmentation":[42,62,106],"in":[43],"images":[45],"with":[46],"similar":[47],"backgrounds.":[48],"To":[49],"address":[50],"these":[51],"limitations,":[52],"we":[53],"propose":[54],"a":[55,65,80],"novel":[56],"approach":[58,116],"that":[63,114],"employs":[64],"new":[66],"pretext":[67],"task":[68],"within":[69],"Siamese":[70],"networks.":[71],"Our":[72],"method":[73,137,158],"integrates":[74],"an":[75],"explicit":[76],"prediction":[77],"head":[78],"and":[79,88,122,141,166],"high-dimensional":[81],"cross-entropy":[82],"loss,":[83],"enabling":[84],"implicit":[85],"class":[86],"labeling":[87],"enhancing":[89,167],"recognition":[91],"patterns.":[94],"Additionally,":[95],"vision":[96],"transformers":[97],"employed":[99],"leverage":[101],"self-attention":[102],"mechanisms,":[103],"facilitating":[104],"accurate":[105],"foreground":[108],"regions.":[110],"Experimental":[111],"results":[112],"demonstrate":[113],"our":[115,136],"outperforms":[117],"anomaly":[123],"detection":[124],"methods.":[125],"Notably,":[126],"when":[127],"used":[128],"pre-train":[130],"backbone":[131],"networks":[132],"such":[133],"as":[134],"ResNet-50,":[135],"yields":[138],"higher":[139],"accuracy":[140],"faster":[142],"convergence":[143],"on":[144,151],"downstream":[145],"supervised":[146,172],"tasks":[147],"compared":[148],"pre-training":[150],"ImageNet":[154],"dataset.":[155],"The":[156],"proposed":[157],"holds":[159],"promise":[160],"advancing":[162],"decision-making":[165],"performance":[169],"traditional":[171],"deep":[173],"models.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-10-10T00:00:00"}
