{"id":"https://openalex.org/W7081564117","doi":"https://doi.org/10.21227/2etq-xh36","title":"\"3DCrack\"","display_name":"\"3DCrack\"","publication_year":2025,"publication_date":"2025-09-12","ids":{"openalex":"https://openalex.org/W7081564117","doi":"https://doi.org/10.21227/2etq-xh36"},"language":"en","primary_location":{"id":"doi:10.21227/2etq-xh36","is_oa":true,"landing_page_url":"https://doi.org/10.21227/2etq-xh36","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/2etq-xh36","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xinan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinan Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Haolin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haolin Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yung-An Hsieh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yung-An Hsieh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhongyu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhongyu Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Anthony Yezzi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anthony Yezzi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Yi-Chang Tsai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi-Chang 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":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6843000054359436,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6843000054359436,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13067","display_name":"Geological Modeling and Analysis","score":0.028200000524520874,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.017799999564886093,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5533000230789185},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.40209999680519104},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3885999917984009},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.3741999864578247},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.3495999872684479},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.33489999175071716},{"id":"https://openalex.org/keywords/cracking","display_name":"Cracking","score":0.33309999108314514}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5533000230789185},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47440001368522644},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43149998784065247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4034000039100647},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.35249999165534973},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C58396970","wikidata":"https://www.wikidata.org/wiki/Q212749","display_name":"Cracking","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3287000060081482},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3100000023841858},{"id":"https://openalex.org/C3019007443","wikidata":"https://www.wikidata.org/wiki/Q568742","display_name":"3d model","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C141349535","wikidata":"https://www.wikidata.org/wiki/Q1361664","display_name":"Laser scanning","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2797999978065491},{"id":"https://openalex.org/C520434653","wikidata":"https://www.wikidata.org/wiki/Q38867","display_name":"Laser","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/2etq-xh36","is_oa":true,"landing_page_url":"https://doi.org/10.21227/2etq-xh36","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/2etq-xh36","is_oa":true,"landing_page_url":"https://doi.org/10.21227/2etq-xh36","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"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":{"\"We":[0],"introduce":[1],"3DCrack,":[2],"a":[3,35,48],"new":[4],"3D":[5,28],"pavement":[6,37,91],"image":[7],"dataset":[8,70],"to":[9,42],"support":[10],"deep":[11],"learning-based":[12],"crack":[13],"detection.":[14],"Data":[15],"was":[16],"collected":[17],"using":[18],"the":[19],"Georgia":[20],"Tech":[21],"Sensing":[22],"Vehicle":[23],"(GTSV),":[24],"equipped":[25],"with":[26,55],"dual":[27],"line":[29],"laser":[30],"sensors.":[31],"These":[32],"sensors":[33],"capture":[34],"4-meter-wide":[36],"profile":[38],"at":[39],"speeds":[40],"up":[41],"60":[43],"mph.":[44],"Each":[45],"frame":[46],"produces":[47],"dense":[49],"1,000":[50],"\\u00d7":[51,58],"2,080":[52],"point":[53],"cloud,":[54],"1":[56,59],"mm":[57,60,66],"resolution":[61],"after":[62],"interpolation,":[63],"and":[64,83,93],"0.5":[65],"height":[67],"precision.":[68],"The":[69],"covers":[71],"diverse":[72],"cracking":[73],"conditions,":[74],"including":[75],"varying":[76],"geometries":[77],"such":[78],"as":[79,86,88],"transverse,":[80],"longitudinal,":[81],"alligator,":[82],"compound":[84],"cracks,":[85],"well":[87],"challenges":[89],"like":[90],"joints":[92],"other":[94],"visual":[95],"distractions,":[96],"ensuring":[97],"robustness":[98],"in":[99],"trained":[100],"models":[101],"across":[102],"real-world":[103],"scenarios.\"":[104]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
