{"id":"https://openalex.org/W4398151645","doi":"https://doi.org/10.1109/access.2024.3403389","title":"Optimizing Concrete Crack Detection: An Attention-Based SWIN U-Net Approach","display_name":"Optimizing Concrete Crack Detection: An Attention-Based SWIN U-Net Approach","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4398151645","doi":"https://doi.org/10.1109/access.2024.3403389"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3403389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3403389","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10535162.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10535162.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077234719","display_name":"Ali Sarhadi","orcid":"https://orcid.org/0000-0001-8403-5646"},"institutions":[{"id":"https://openalex.org/I155419210","display_name":"Islamic Azad University, Science and Research Branch","ror":"https://ror.org/03187yj51","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I155419210"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Ali Sarhadi","raw_affiliation_strings":["Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran"],"raw_orcid":"https://orcid.org/0000-0001-8403-5646","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I155419210"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019329918","display_name":"Mehdi Ravanshadnia","orcid":"https://orcid.org/0000-0002-4990-7127"},"institutions":[{"id":"https://openalex.org/I155419210","display_name":"Islamic Azad University, Science and Research Branch","ror":"https://ror.org/03187yj51","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I155419210"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mehdi Ravanshadnia","raw_affiliation_strings":["Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran"],"raw_orcid":"https://orcid.org/0009-0006-1197-3782","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I155419210"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076747824","display_name":"Armin Monirabbasi","orcid":null},"institutions":[{"id":"https://openalex.org/I55547365","display_name":"Payame Noor University","ror":"https://ror.org/031699d98","country_code":"IR","type":"education","lineage":["https://openalex.org/I55547365"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Armin Monirabbasi","raw_affiliation_strings":["Department of Civil Engineering, Payame Noor University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Payame Noor University, Tehran, Iran","institution_ids":["https://openalex.org/I55547365"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020091057","display_name":"Milad Ghanbari","orcid":"https://orcid.org/0000-0001-9550-5711"},"institutions":[{"id":"https://openalex.org/I136830121","display_name":"Islamic Azad University South Tehran Branch","ror":"https://ror.org/02xc21a77","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I136830121"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Milad Ghanbari","raw_affiliation_strings":["Department of Civil Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran"],"raw_orcid":"https://orcid.org/0000-0001-9550-5711","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I136830121"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5077234719"],"corresponding_institution_ids":["https://openalex.org/I155419210"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":9.8119,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.98980333,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"12","issue":null,"first_page":"77575","last_page":"77585"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9549000263214111,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9538000226020813,"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/computer-science","display_name":"Computer science","score":0.7892563343048096},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7813454866409302},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6421123147010803},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6252139806747437},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4523332118988037},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.438857764005661},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4306086301803589},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4282280504703522},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.42034536600112915},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3973984122276306},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3875875473022461},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.18786770105361938}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7892563343048096},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7813454866409302},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6421123147010803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6252139806747437},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4523332118988037},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.438857764005661},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4306086301803589},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4282280504703522},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.42034536600112915},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3973984122276306},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3875875473022461},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.18786770105361938}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3403389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3403389","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10535162.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9d319dc05ff3475cac536adda2aaae54","is_oa":true,"landing_page_url":"https://doaj.org/article/9d319dc05ff3475cac536adda2aaae54","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 77575-77585 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3403389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3403389","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10535162.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4398151645.pdf"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1499677203","https://openalex.org/W1862829300","https://openalex.org/W1960097882","https://openalex.org/W2126052502","https://openalex.org/W2144801789","https://openalex.org/W2598457882","https://openalex.org/W2748643398","https://openalex.org/W2765854388","https://openalex.org/W2786174052","https://openalex.org/W2800346298","https://openalex.org/W2912350898","https://openalex.org/W2983902176","https://openalex.org/W3002978252","https://openalex.org/W3007268491","https://openalex.org/W3014583121","https://openalex.org/W3039918387","https://openalex.org/W3119707588","https://openalex.org/W3138516171","https://openalex.org/W3158447174","https://openalex.org/W3205451806","https://openalex.org/W3212004861","https://openalex.org/W4211035980","https://openalex.org/W4220727636","https://openalex.org/W4223510716","https://openalex.org/W4281750281","https://openalex.org/W4283459038","https://openalex.org/W4317509311","https://openalex.org/W4320496585","https://openalex.org/W4362551393","https://openalex.org/W4375952448","https://openalex.org/W4383959653","https://openalex.org/W4392561748","https://openalex.org/W4392843888","https://openalex.org/W4392913747","https://openalex.org/W6629644485","https://openalex.org/W6747842307","https://openalex.org/W6774070773","https://openalex.org/W6794715895"],"related_works":["https://openalex.org/W3099518927","https://openalex.org/W3162172987","https://openalex.org/W4387800741","https://openalex.org/W3037097571","https://openalex.org/W4376626823","https://openalex.org/W3113922403","https://openalex.org/W3080935557","https://openalex.org/W4388821232","https://openalex.org/W2607836359","https://openalex.org/W4308016144"],"abstract_inverted_index":{"Utilizing":[0],"convolutional":[1,45],"neural":[2,46],"network":[3],"(CNN)":[4],"models,":[5],"computer":[6],"vision":[7],"technology":[8],"has":[9],"become":[10],"a":[11,88,97,167],"reliable":[12],"and":[13,52,128,137,166,200,209,214,232],"powerful":[14],"tool":[15],"for":[16],"detecting":[17],"potential":[18,133],"damage":[19],"in":[20,227],"concrete":[21,39],"structures":[22],"at":[23],"the":[24,50,62,66,69,78,86,102,106,145,157,161,186,206,221],"pixel":[25],"level.":[26],"In":[27],"this":[28],"study,":[29],"an":[30,180],"advanced":[31],"SWIN":[32,63],"U-Net":[33],"architecture":[34],"was":[35,108,150,174,188,195],"introduced":[36],"to":[37,48,71,131,144],"detect":[38],"cracks.":[40],"The":[41,58,110,148,172,183,217],"model":[42,70,149,173,187,219],"integrates":[43],"attention-based":[44],"networks":[47],"enhance":[49],"speed":[51],"accuracy":[53],"of":[54,61,68,73,91,170,185,225],"crack":[55,92],"detection":[56],"significantly.":[57],"distinctive":[59],"features":[60],"Transformer":[64],"make":[65],"application":[67],"images":[72],"varying":[74],"sizes":[75],"possible":[76],"while":[77],"computational":[79],"resources":[80],"are":[81],"used":[82],"efficiently.":[83],"To":[84],"train":[85],"model,":[87],"dataset":[89],"consisting":[90],"images,":[93],"each":[94],"accompanied":[95],"by":[96],"corresponding":[98],"mask":[99],"that":[100],"highlighted":[101],"relevant":[103],"regions":[104],"within":[105],"image,":[107],"used.":[109],"training":[111],"data":[112],"were":[113,142,203,212],"augmented":[114],"using":[115,152,190],"Flip,":[116],"Rotate,":[117],"Random":[118,120,122],"Contrast,":[119],"Gamma,":[121],"Brightness,":[123],"Elastic":[124],"Transformation,":[125],"Grid":[126],"Distortion,":[127],"Optical":[129],"Distortion":[130],"counter":[132],"overfitting.":[134],"Additionally,":[135],"L2":[136],"spatial":[138],"dropout":[139],"regularization":[140],"techniques":[141],"applied":[143],"proposed":[146],"model.":[147],"fine-tuned":[151],"stochastic":[153],"gradient":[154],"descent":[155],"with":[156,179,197],"Adam":[158],"optimizer,":[159],"employing":[160],"binary":[162],"cross-entropy":[163],"loss":[164,208],"function,":[165],"learning":[168],"rate":[169],"0.001.":[171],"trained":[175,218],"over":[176],"100":[177],"epochs":[178],"adjustment":[181],"scheduler.":[182],"performance":[184,223],"evaluated":[189],"various":[191],"metrics.":[192],"Then,":[193],"it":[194],"compared":[196],"three":[198],"benchmarks":[199],"impressive":[201],"results":[202],"achieved.":[204],"Notably,":[205],"Dice":[207],"IOU":[210],"values":[211],"93%":[213],"79%,":[215],"respectively.":[216],"had":[220],"exceptional":[222],"score":[224],"0.99":[226],"accuracy,":[228],"precision,":[229],"recall,":[230],"F1,":[231],"sensitivity.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":32}],"updated_date":"2026-06-02T09:04:35.204637","created_date":"2025-10-10T00:00:00"}