{"id":"https://openalex.org/W4399418580","doi":"https://doi.org/10.1145/3651671.3651723","title":"Light-Trans YOLO: A Lightweight Network Based on Transformer for Defect Detection of Industrial Lace Surface","display_name":"Light-Trans YOLO: A Lightweight Network Based on Transformer for Defect Detection of Industrial Lace Surface","publication_year":2024,"publication_date":"2024-02-02","ids":{"openalex":"https://openalex.org/W4399418580","doi":"https://doi.org/10.1145/3651671.3651723"},"language":"en","primary_location":{"id":"doi:10.1145/3651671.3651723","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3651671.3651723","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 16th International Conference on Machine Learning and Computing","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":null,"display_name":"Ya Fei Wang","orcid":"https://orcid.org/0000-0002-1081-2788"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ya Fei Wang","raw_affiliation_strings":["Xi'an Polytechnic University, School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, Shaanxi Province, China"],"raw_orcid":"https://orcid.org/0000-0002-1081-2788","affiliations":[{"raw_affiliation_string":"Xi'an Polytechnic University, School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I27599042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066405237","display_name":"Junfeng Jing","orcid":"https://orcid.org/0000-0001-6646-3698"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Feng Jing","raw_affiliation_strings":["Xi'an Polytechnic University, School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, Shaanxi Province, China"],"raw_orcid":"https://orcid.org/0000-0001-6646-3698","affiliations":[{"raw_affiliation_string":"Xi'an Polytechnic University, School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I27599042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064247837","display_name":"Siyu Sheng","orcid":"https://orcid.org/0000-0002-4838-8986"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Si Yu Sheng","raw_affiliation_strings":["Xi'an Polytechnic University, School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, Shaanxi Province, China"],"raw_orcid":"https://orcid.org/0000-0002-4838-8986","affiliations":[{"raw_affiliation_string":"Xi'an Polytechnic University, School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I27599042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5099043638","display_name":"Xin Tian Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Tian Jiao","raw_affiliation_strings":["Xi'an Polytechnic University, School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, Shaanxi Province, China"],"raw_orcid":"https://orcid.org/0000-0002-0030-9927","affiliations":[{"raw_affiliation_string":"Xi'an Polytechnic University, School of Electronic Information, Xi 'an Polytechnic University, Xi 'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I27599042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27599042"],"apc_list":null,"apc_paid":null,"fwci":0.3331,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59642614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"251","last_page":"256"},"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":0.9994000196456909,"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":0.9994000196456909,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9735000133514404,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9584000110626221,"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":0.6183162331581116},{"id":"https://openalex.org/keywords/separable-space","display_name":"Separable space","score":0.5498908162117004},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5295418500900269},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5188289880752563},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48651546239852905},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4830220639705658},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.48107409477233887},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37342342734336853},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3566381335258484},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19725364446640015},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.152091383934021},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12218800187110901},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.10850423574447632},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.09601455926895142},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07542264461517334}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6183162331581116},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.5498908162117004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5295418500900269},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5188289880752563},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48651546239852905},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4830220639705658},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.48107409477233887},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37342342734336853},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3566381335258484},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19725364446640015},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.152091383934021},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12218800187110901},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.10850423574447632},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.09601455926895142},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07542264461517334},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3651671.3651723","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3651671.3651723","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 16th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2005164446","https://openalex.org/W2102605133","https://openalex.org/W2618530766","https://openalex.org/W2883780447","https://openalex.org/W2946948417","https://openalex.org/W2982083293","https://openalex.org/W3018757597","https://openalex.org/W3082759760","https://openalex.org/W3094502228","https://openalex.org/W3133954504","https://openalex.org/W3184439416","https://openalex.org/W4210860225","https://openalex.org/W4225113999","https://openalex.org/W4225321635","https://openalex.org/W4286233390","https://openalex.org/W4319993105","https://openalex.org/W4381511029","https://openalex.org/W6739901393","https://openalex.org/W6761108903","https://openalex.org/W6778485988","https://openalex.org/W6785652829","https://openalex.org/W6798838024","https://openalex.org/W6838784703"],"related_works":["https://openalex.org/W2009525028","https://openalex.org/W4321064619","https://openalex.org/W4375867731","https://openalex.org/W2530952058","https://openalex.org/W2582836483","https://openalex.org/W4299366318","https://openalex.org/W3158431807","https://openalex.org/W4297580547","https://openalex.org/W2951583185","https://openalex.org/W4308155352"],"abstract_inverted_index":{"Lace":[0],"surface":[1,69,149],"Defect":[2],"detection":[3,41,62,197],"has":[4,192],"always":[5],"been":[6],"a":[7,48,59,115,193],"crucial":[8],"step":[9],"in":[10,153],"the":[11,20,30,40,76,91,98,104,124,128,146,158,162,176,181],"industrial":[12,147],"production":[13,155],"of":[14,25,32,42,130,133,164,180],"lace":[15,45,68,148,154],"products.":[16],"However,":[17],"due":[18],"to":[19,66,83,97,113],"complex":[21],"texture":[22],"and":[23,111,118,137,157,175,178,185,199],"deformability":[24],"lace,":[26],"as":[27,29],"well":[28],"difficulty":[31],"distinguishing":[33],"minor":[34],"defects":[35,43],"from":[36],"normal":[37],"images.":[38],"Therefore,":[39],"on":[44,145],"surfaces":[46],"is":[47,167,170],"challenging":[49],"but":[50],"rarely":[51],"studied":[52],"task.":[53],"In":[54,81],"this":[55],"paper,":[56],"we":[57,89,102],"propose":[58],"new":[60,116],"lightweight":[61,77,92],"framework,":[63],"Light-Trans":[64],"YOLO,":[65],"detect":[67],"defects.":[70],"First,":[71],"our":[72,165,190],"backbone":[73,99],"network":[74,78],"uses":[75],"C3":[79],"GhostNet.":[80],"addition,":[82],"obtain":[84],"more":[85,139],"complete":[86],"global":[87],"information,":[88],"add":[90,119],"Mobile":[93],"Transformer":[94],"Block":[95],"(MTB)":[96],"network.":[100],"Then":[101],"use":[103],"proposed":[105],"standard":[106],"deep-wise":[107],"separable":[108,135],"convolution":[109,136],"(SDSConv)":[110],"SDSBottleneck":[112],"design":[114],"neck":[117],"Coordinate":[120],"Attention":[121],"(CA)":[122],"at":[123],"end,":[125],"which":[126,169,187],"overcomes":[127],"problem":[129],"information":[131],"loss":[132],"deep":[134],"extracts":[138],"effective":[140],"information.":[141],"We":[142],"conduct":[143],"experiments":[144,159],"defect":[150],"dataset":[151],"collected":[152],"sites,":[156],"prove":[160],"that":[161,189],"mAP":[163],"model":[166,182,191],"96.6%,":[168],"7.7%":[171],"higher":[172],"than":[173],"YOLOV5s,":[174],"FPS":[177],"F1-score":[179],"reaches":[183],"50.3":[184],"0.93,":[186],"indicates":[188],"great":[194],"trade-off":[195],"between":[196],"accuracy":[198],"speed.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}