{"id":"https://openalex.org/W7161412906","doi":"https://doi.org/10.1016/j.asoc.2026.115487","title":"A unified generation framework for crack images and masks with edge-guided supervision","display_name":"A unified generation framework for crack images and masks with edge-guided supervision","publication_year":2026,"publication_date":"2026-05-16","ids":{"openalex":"https://openalex.org/W7161412906","doi":"https://doi.org/10.1016/j.asoc.2026.115487"},"language":"en","primary_location":{"id":"doi:10.1016/j.asoc.2026.115487","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.115487","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.asoc.2026.115487","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136292892","display_name":"Xun Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xun Zhang","raw_affiliation_strings":["State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136314286","display_name":"Jianming Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianming Ding","raw_affiliation_strings":["State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079076715","display_name":"Kaiyun Wang","orcid":"https://orcid.org/0000-0003-0958-4260"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiyun Wang","raw_affiliation_strings":["State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5136314286"],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":{"value":3350,"currency":"USD","value_usd":3350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66995961,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"201","issue":null,"first_page":"115487","last_page":"115487"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10514","display_name":"Numerical methods in engineering","score":0.3808000087738037,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T10514","display_name":"Numerical methods in engineering","score":0.3808000087738037,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.09099999815225601,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.05909999832510948,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2639999985694885},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.2563999891281128},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2363000065088272}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6748999953269958},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.52920001745224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49070000648498535},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.34049999713897705},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.33500000834465027},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2563999891281128},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2433999925851822},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2363000065088272}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.asoc.2026.115487","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.115487","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.asoc.2026.115487","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.115487","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322720","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2133665775","https://openalex.org/W2748643398","https://openalex.org/W2772486537","https://openalex.org/W2954996726","https://openalex.org/W2966399807","https://openalex.org/W3013406096","https://openalex.org/W3166908170","https://openalex.org/W3216522454","https://openalex.org/W4224237033","https://openalex.org/W4309028162","https://openalex.org/W4309310506","https://openalex.org/W4324352705","https://openalex.org/W4362453650","https://openalex.org/W4386378590","https://openalex.org/W4389816582","https://openalex.org/W4392971772","https://openalex.org/W4394603234","https://openalex.org/W4394951972","https://openalex.org/W4395688050","https://openalex.org/W4402929289","https://openalex.org/W4402987798","https://openalex.org/W4405756900","https://openalex.org/W4406526284","https://openalex.org/W4406938298","https://openalex.org/W4407063593","https://openalex.org/W4412693096","https://openalex.org/W4417064533"],"related_works":[],"abstract_inverted_index":{"To":[0],"overcome":[1],"the":[2,7,69,79,86,103,116,134,139],"annotation-intensive":[3],"bottleneck":[4],"that":[5,120],"hinders":[6],"scalability":[8,137],"of":[9,30,138],"crack":[10,31,66,87,110,144],"image":[11],"datasets,":[12],"a":[13,52],"novel":[14],"edge-guided":[15],"pseudo-supervised":[16,91],"generative":[17],"adversarial":[18],"framework,":[19],"termed":[20],"AutoMask-GAN":[21],"(AMGAN),":[22],"is":[23],"proposed.":[24],"AMGAN":[25],"enables":[26],"end-to-end":[27],"joint":[28],"generation":[29],"images":[32,44],"and":[33,45,50,108,128,136],"corresponding":[34],"masks":[35,83],"without":[36],"requiring":[37],"any":[38],"human-annotated":[39],"data.":[40],"It":[41],"takes":[42],"background":[43],"random":[46],"noise":[47],"as":[48],"inputs":[49],"incorporates":[51],"content-aware":[53],"loss":[54],"to":[55,81,99],"preserve":[56],"structural":[57],"consistency":[58],"in":[59],"non-crack":[60],"regions,":[61],"while":[62],"simultaneously":[63],"enabling":[64],"diverse":[65],"synthesis.":[67],"In":[68],"early":[70],"training":[71],"stages,":[72],"edge-detected":[73],"pseudomasks":[74],"provide":[75],"initial":[76],"supervision,":[77],"guiding":[78],"model":[80,118],"generate":[82],"consistent":[84],"with":[85,130],"patterns.":[88],"A":[89],"progressive":[90],"learning":[92],"strategy":[93],"gradually":[94],"shifts":[95],"supervision":[96],"from":[97],"edge-based":[98],"model-generated":[100],"pseudomasks,":[101],"helping":[102],"network":[104],"learn":[105],"more":[106],"refined":[107],"coherent":[109],"masks.":[111],"Experimental":[112],"results":[113],"based":[114],"on":[115],"YOLOv11s-seg":[117],"demonstrate":[119],"AMGAN-generated":[121],"samples":[122],"outperform":[123],"those":[124],"produced":[125],"by":[126],"DCGAN":[127],"CycleGAN":[129],"pretrained":[131],"pseudolabels,":[132],"validating":[133],"effectiveness":[135],"proposed":[140],"framework":[141],"for":[142],"annotation-free":[143],"dataset":[145],"construction.":[146]},"counts_by_year":[],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2026-05-17T00:00:00"}
