{"id":"https://openalex.org/W4399618434","doi":"https://doi.org/10.1109/tits.2024.3402110","title":"A Pavement Crack Translator for Data Augmentation and Pixel-Level Detection Based on Weakly Supervised Learning","display_name":"A Pavement Crack Translator for Data Augmentation and Pixel-Level Detection Based on Weakly Supervised Learning","publication_year":2024,"publication_date":"2024-06-13","ids":{"openalex":"https://openalex.org/W4399618434","doi":"https://doi.org/10.1109/tits.2024.3402110"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3402110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3402110","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/A5033668657","display_name":"Jingtao Zhong","orcid":"https://orcid.org/0000-0001-7977-2043"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jingtao Zhong","raw_affiliation_strings":["Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020510791","display_name":"Yuetan Ma","orcid":"https://orcid.org/0000-0001-8756-9096"},"institutions":[{"id":"https://openalex.org/I56934997","display_name":"Changsha University of Science and Technology","ror":"https://ror.org/03yph8055","country_code":"CN","type":"education","lineage":["https://openalex.org/I56934997"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuetan Ma","raw_affiliation_strings":["School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China","institution_ids":["https://openalex.org/I56934997"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357909","display_name":"Miaomiao Zhang","orcid":"https://orcid.org/0000-0002-9025-5458"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Miaomiao Zhang","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017408803","display_name":"Rui Xiao","orcid":"https://orcid.org/0000-0003-1025-6651"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Xiao","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of California at Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of California at Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025692085","display_name":"Guantao Cheng","orcid":"https://orcid.org/0000-0002-2102-4672"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guantao Cheng","raw_affiliation_strings":["Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087008316","display_name":"Baoshan Huang","orcid":"https://orcid.org/0000-0001-8551-0082"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Baoshan Huang","raw_affiliation_strings":["Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5033668657"],"corresponding_institution_ids":["https://openalex.org/I75027704"],"apc_list":null,"apc_paid":null,"fwci":4.9213,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.95855495,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"25","issue":"10","first_page":"13350","last_page":"13363"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9991000294685364,"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":0.9991000294685364,"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":0.98580002784729,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9677000045776367,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/pixel","display_name":"Pixel","score":0.61521315574646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5421013236045837},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5313618183135986},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.43607765436172485},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3961448073387146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3903684914112091},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3759351074695587},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16001716256141663}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.61521315574646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5421013236045837},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5313618183135986},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.43607765436172485},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3961448073387146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3903684914112091},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3759351074695587},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16001716256141663}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2024.3402110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3402110","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":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1982471090","https://openalex.org/W2075631365","https://openalex.org/W2144801789","https://openalex.org/W2407692387","https://openalex.org/W2593414223","https://openalex.org/W2748643398","https://openalex.org/W2814406141","https://openalex.org/W2899242765","https://openalex.org/W2944441395","https://openalex.org/W2962793481","https://openalex.org/W2964308596","https://openalex.org/W3001456352","https://openalex.org/W3002057796","https://openalex.org/W3007515473","https://openalex.org/W3011200270","https://openalex.org/W3011849609","https://openalex.org/W3021470593","https://openalex.org/W3040786507","https://openalex.org/W3096831136","https://openalex.org/W3175064897","https://openalex.org/W3192378566","https://openalex.org/W3208568670","https://openalex.org/W4205215834","https://openalex.org/W4225984668","https://openalex.org/W4283215527","https://openalex.org/W4293071267","https://openalex.org/W4307210881","https://openalex.org/W4308118148","https://openalex.org/W4310264525","https://openalex.org/W4313491188","https://openalex.org/W4390628502","https://openalex.org/W4390707509","https://openalex.org/W4392372815","https://openalex.org/W6637373629","https://openalex.org/W6685352114","https://openalex.org/W6779669310"],"related_works":["https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W2922442631","https://openalex.org/W4390494008","https://openalex.org/W4285328440","https://openalex.org/W4390062853","https://openalex.org/W4389256085","https://openalex.org/W4313644201"],"abstract_inverted_index":{"Recent":[0],"state-of-the-art":[1,176],"pavement":[2,20,52,108,252,262],"crack":[3,21,41,53,70,75,84,91,109,116,126,147,167,171,209,225,243,253,263,266],"detection":[4,34,110],"algorithms":[5],"are":[6],"data-driven":[7],"and":[8,31,94,111,128,134,174,193,220,237,255,277],"domain-sensitive":[9],"due":[10],"to":[11,44,105,120,138,154,189,201,250],"their":[12],"heavy":[13],"reliance":[14],"on":[15,234],"datasets.":[16,239],"Establishing":[17],"a":[18,63,202],"high-quality":[19],"dataset":[22,123],"with":[23,55,80,124,232],"various":[24,125],"scenarios":[25],"could":[26],"help":[27],"improve":[28],"the":[29,40,98,115,122,140,156,159,165,241],"robustness":[30],"generality":[32],"of":[33,142,158,180],"models.":[35,161],"Therefore,":[36,240],"this":[37],"study":[38],"presented":[39],"translator":[42,76,85,92,168,172,197,226,244,267],"structure":[43],"achieve":[45,106],"image":[46,112,184],"style":[47],"transfer,":[48],"which":[49],"can":[50,246],"generate":[51],"images":[54,127,254],"different":[56],"backgrounds":[57],"for":[58,72,183,208,217,222,260,273],"data":[59,143,275],"augmentation.":[60],"To":[61],"get":[62],"better":[64],"translation":[65,185],"performance,":[66],"we":[67],"proposed":[68,90,160,166,242],"two":[69],"translators":[71,117],"comparison.":[73],"The":[74,89,224],"v2":[77,95,169,227,245,268],"was":[78,103],"designed":[79],"attention":[81],"blocks":[82],"whereas":[83],"v1":[86,93,173],"did":[87],"not.":[88],"served":[96],"as":[97],"weakly":[99],"supervised":[100],"method":[101],"that":[102,164],"used":[104,119,153,249],"pixel-level":[107,261,278],"transfer.":[113],"Additionally,":[114],"were":[118,136,152],"establish":[121],"corresponding":[129],"labels.":[130],"Moreover,":[131],"U-Net,":[132,218],"SegNet,":[133,219],"W-segnet":[135],"compared":[137,231],"evaluate":[139],"effectiveness":[141],"augmentation":[144,199,207,276],"by":[145,215],"using":[146],"translators.":[148],"Two":[149],"open-source":[150],"datasets":[151],"demonstrate":[155,163],"generalization":[157],"Results":[162],"outperforms":[170],"other":[175],"methods":[177],"in":[178],"terms":[179],"evaluation":[181],"metrics":[182],"from":[186],"normal":[187],"background":[188],"shadow,":[190],"wet,":[191],"night,":[192],"grooved":[194],"backgrounds.":[195],"Crack":[196],"(v2)-based":[198],"contributes":[200],"greater":[203],"improvement":[204],"than":[205],"conventional":[206],"segmentation":[210],"where":[211],"F1":[212],"has":[213],"increased":[214],"5%":[216],"7%":[221],"W-segnet.":[223],"yields":[228],"equivalent":[229],"results":[230],"U-Net":[233],"holdout":[235],"EdmCrack600":[236],"CRKWH100":[238],"be":[247],"effectively":[248],"augment":[251],"it":[256],"shows":[257],"great":[258],"potential":[259],"detection.":[264],"Proposed":[265],"provides":[269],"an":[270],"integrated":[271],"model":[272],"both":[274],"segmentation.":[279]},"counts_by_year":[{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}