{"id":"https://openalex.org/W4411552379","doi":"https://doi.org/10.1109/cscwd64889.2025.11033374","title":"Fabric Defect Detection with Fine-tuned YOLOv7","display_name":"Fabric Defect Detection with Fine-tuned YOLOv7","publication_year":2025,"publication_date":"2025-05-05","ids":{"openalex":"https://openalex.org/W4411552379","doi":"https://doi.org/10.1109/cscwd64889.2025.11033374"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd64889.2025.11033374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-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/A5118596316","display_name":"Salman Shehzad","orcid":null},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Salman Shehzad","raw_affiliation_strings":["School of Artificial Intelligence, Guangzhou University,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103905162","display_name":"Chunsheng Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunsheng Yang","raw_affiliation_strings":["School of Artificial Intelligence, Guangzhou University,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064709384","display_name":"Yuan\u2010Gen Wang","orcid":"https://orcid.org/0000-0003-3010-4196"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan-Gen Wang","raw_affiliation_strings":["School of Artificial Intelligence, Guangzhou University,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102915414","display_name":"Bitang Zhu","orcid":"https://orcid.org/0000-0002-9251-3684"},"institutions":[{"id":"https://openalex.org/I13985625","display_name":"East China Jiaotong University","ror":"https://ror.org/05x2f1m38","country_code":"CN","type":"education","lineage":["https://openalex.org/I13985625"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bitang Zhu","raw_affiliation_strings":["School of Civil Engineering and Architecture, East China Jiaotong University,Nanchang,China"],"affiliations":[{"raw_affiliation_string":"School of Civil Engineering and Architecture, East China Jiaotong University,Nanchang,China","institution_ids":["https://openalex.org/I13985625"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5118596316"],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23728902,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"117","last_page":"122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9988999962806702,"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/computer-science","display_name":"Computer science","score":0.4336296319961548}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4336296319961548}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cscwd64889.2025.11033374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2193145675","https://openalex.org/W2560311620","https://openalex.org/W2790642356","https://openalex.org/W2884068670","https://openalex.org/W2942685721","https://openalex.org/W2963037989","https://openalex.org/W2966350350","https://openalex.org/W3006075760","https://openalex.org/W3009131247","https://openalex.org/W3012653848","https://openalex.org/W3034552520","https://openalex.org/W3130934537","https://openalex.org/W3149758324","https://openalex.org/W3185379701","https://openalex.org/W4211256381","https://openalex.org/W4304128598","https://openalex.org/W4308866570","https://openalex.org/W4319298454","https://openalex.org/W4322487373","https://openalex.org/W4323314156","https://openalex.org/W4372347372","https://openalex.org/W4385421912","https://openalex.org/W4386076325","https://openalex.org/W4391559323","https://openalex.org/W4400057023","https://openalex.org/W4400411529","https://openalex.org/W4400737789","https://openalex.org/W6632581963","https://openalex.org/W6777046832"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0],"defect":[1,28,49,66,116],"on":[2,128],"the":[3,9,13,20,40,46,53,90,106,123,150,171,179],"fabric":[4,43,65,115],"surface":[5],"is":[6],"one":[7],"of":[8,15,23,42,48,175],"important":[10],"factors":[11],"affecting":[12],"quality":[14,24],"fabrics.":[16],"Defect":[17],"detection":[18,29,36,75],"becomes":[19],"core":[21],"means":[22],"control.":[25],"Current":[26],"deep-Iearning-based":[27],"methods":[30],"present":[31],"a":[32,59,72,80,100,114,160],"significant":[33],"challenge":[34],"in":[35,164],"accuracy":[37],"due":[38],"to":[39,83,121,149,159],"diversity":[41],"patterns":[44],"and":[45,85,95,104,118,143,173],"scarcity":[47],"samples.":[50],"Inspired":[51],"by":[52,138,141,146],"transfer-learning":[54],"paradigm,":[55],"this":[56],"article":[57],"proposes":[58],"novel":[60],"YOLOv7-based":[61],"fine-tuning":[62,156],"method":[63,157],"for":[64],"detection.":[67],"Specifically,":[68],"we":[69,97,111],"first":[70],"employ":[71],"well-trained":[73],"object":[74],"network":[76,181],"(i.e.":[77],"YOLOv7)":[78],"as":[79],"benchmark":[81],"model":[82,152,165],"locate":[84],"classify":[86],"defect-picking":[87],"points.":[88],"Considering":[89],"fabrics":[91],"are":[92],"mostly":[93],"occluded":[94],"shaded,":[96],"therefore":[98],"introduce":[99],"multi-channel":[101],"attention":[102],"mechanism":[103],"design":[105],"corresponding":[107],"loss":[108],"function.":[109],"Finally,":[110],"manually":[112],"clean":[113],"dataset":[117],"use":[119],"it":[120],"fine-tune":[122],"YOLOv7.":[124,153],"Extensive":[125],"experiments":[126],"tested":[127],"public":[129],"datasets":[130],"show":[131],"that":[132],"our":[133,155,176],"fine-tuned":[134],"YOLOv7":[135],"improves":[136],"mAP":[137],"3.5%,":[139],"precision":[140],"6.3%,":[142],"recall":[144],"rate":[145],"5.7%":[147],"compared":[148],"baseline":[151],"Especially,":[154],"leads":[158],"6":[161],"MB":[162],"reduction":[163],"size.":[166],"Further":[167],"ablation":[168],"studies":[169],"demonstrate":[170],"effectiveness":[172],"contribution":[174],"designs":[177],"toward":[178],"whole":[180],"model.":[182]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}