{"id":"https://openalex.org/W7154592891","doi":"https://doi.org/10.48550/arxiv.2604.13322","title":"Towards Successful Implementation of Automated Raveling Detection: Effects of Training Data Size, Illumination Difference, and Spatial Shift","display_name":"Towards Successful Implementation of Automated Raveling Detection: Effects of Training Data Size, Illumination Difference, and Spatial Shift","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154592891","doi":"https://doi.org/10.48550/arxiv.2604.13322"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.13322","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13322","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.13322","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133773361","display_name":"Xinan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xinan","raw_affiliation_strings":["James"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"James","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133812855","display_name":"Haolin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haolin","raw_affiliation_strings":["James"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"James","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133816369","display_name":"Zhongyu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhongyu","raw_affiliation_strings":["James"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"James","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133793022","display_name":"Yi-Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi-Chang","raw_affiliation_strings":["James"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"James","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133811266","display_name":"Tsai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tsai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9445000290870667,"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":0.9445000290870667,"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/T10264","display_name":"Asphalt Pavement Performance Evaluation","score":0.037300001829862595,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.0019000000320374966,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/robustness","display_name":"Robustness (evolution)","score":0.7652999758720398},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6083999872207642},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5299000144004822},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5171999931335449},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4950999915599823},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.48069998621940613},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.38280001282691956}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7652999758720398},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6083999872207642},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5694000124931335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5672000050544739},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5299000144004822},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5284000039100647},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5171999931335449},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4950999915599823},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.48069998621940613},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.38280001282691956},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3449999988079071},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3163999915122986},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.29899999499320984},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.2635999917984009}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.13322","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13322","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.13322","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13322","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Raveling,":[0],"the":[1,63,76,95,158,167,177,190],"loss":[2],"of":[3,9,65,78,97,135,160,171,179],"aggregates,":[4],"is":[5,141],"a":[6,66,122,184,197,204],"major":[7],"form":[8],"asphalt":[10],"pavement":[11],"surface":[12],"distress,":[13],"especially":[14],"on":[15,36,222],"highways.":[16],"While":[17],"research":[18],"has":[19],"shown":[20],"that":[21,89,165,242],"machine":[22],"learning":[23],"and":[24,57,69,85,102,105,169,238],"deep":[25],"learning-based":[26],"methods":[27],"yield":[28],"promising":[29],"results":[30],"for":[31,72,219,230],"raveling":[32,132,236],"detection":[33,237],"by":[34,143],"classification":[35],"range":[37],"images,":[38],"their":[39],"performance":[40],"often":[41],"degrades":[42],"in":[43,131,187,194,208,214,235],"large-scale":[44],"deployments":[45],"where":[46],"more":[47,67,231],"diverse":[48,192,246],"inference":[49],"data":[50,173],"may":[51],"originate":[52],"from":[53],"different":[54],"runs,":[55],"sensors,":[56],"environmental":[58],"conditions.":[59,115,247],"This":[60],"degradation":[61],"highlights":[62],"need":[64],"generalizable":[68],"robust":[70],"solution":[71],"real-world":[73,114,240],"implementation.":[74],"Thus,":[75],"objectives":[77],"this":[79,117],"study":[80,199],"are":[81,174],"to":[82,109,125,129,156,176,203,245],"1)":[83],"identify":[84],"assess":[86],"potential":[87],"variations":[88,130],"impact":[90,159],"model":[91,111,127,233],"robustness,":[92],"such":[93],"as":[94],"quantity":[96,168],"training":[98,172],"data,":[99,139],"illumination":[100],"difference,":[101],"spatial":[103],"shift;":[104],"2)":[106],"leverage":[107],"findings":[108,202],"enhance":[110],"robustness":[112,128],"under":[113,189],"To":[116],"end,":[118],"we":[119],"propose":[120],"RavelingArena,":[121],"benchmark":[123],"designed":[124],"evaluate":[126],"detection.":[133],"Instead":[134],"collecting":[136],"extensive":[137],"new":[138],"it":[140],"built":[142],"augmenting":[144],"an":[145],"existing":[146],"dataset":[147],"with":[148],"diverse,":[149],"controlled":[150],"variations,":[151],"thereby":[152],"enabling":[153],"variation-controlled":[154],"experiments":[155],"quantify":[157],"each":[161],"variation.":[162],"Results":[163],"demonstrate":[164],"both":[166],"diversity":[170],"critical":[175],"accuracy":[178,188],"models,":[180],"achieving":[181],"at":[182],"least":[183],"9.2%":[185],"gain":[186],"most":[191],"conditions":[193],"experiments.":[195],"Additionally,":[196],"case":[198],"applying":[200],"these":[201],"multi-year":[205],"test":[206],"section":[207],"Georgia,":[209],"U.S.,":[210],"shows":[211],"significant":[212],"improvements":[213],"year-to-year":[215],"consistency,":[216],"laying":[217],"foundations":[218],"future":[220],"studies":[221],"temporal":[223],"deterioration":[224],"modeling.":[225],"These":[226],"insights":[227],"provide":[228],"guidance":[229],"reliable":[232],"deployment":[234],"other":[239],"tasks":[241],"require":[243],"adaptability":[244]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-17T00:00:00"}
