{"id":"https://openalex.org/W4406673085","doi":"https://doi.org/10.2298/csis240911007r","title":"A lightweight defect classification method for latex gloves based on image enhancement","display_name":"A lightweight defect classification method for latex gloves based on image enhancement","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4406673085","doi":"https://doi.org/10.2298/csis240911007r"},"language":"en","primary_location":{"id":"doi:10.2298/csis240911007r","is_oa":true,"landing_page_url":"https://doi.org/10.2298/csis240911007r","pdf_url":"http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142500007R","source":{"id":"https://openalex.org/S206939107","display_name":"Computer Science and Information Systems","issn_l":"1820-0214","issn":["1820-0214","2406-1018"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321031","host_organization_name":"ComSIS Consortium","host_organization_lineage":["https://openalex.org/P4310321031"],"host_organization_lineage_names":["ComSIS Consortium"],"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":"Computer Science and Information Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142500007R","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025812546","display_name":"Yong Ren","orcid":"https://orcid.org/0000-0003-2217-0927"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Ren","raw_affiliation_strings":["Applied Technology College Of Soochow University, SuZhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Technology College Of Soochow University, SuZhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100407515","display_name":"Dong Liu","orcid":"https://orcid.org/0000-0003-2043-5346"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Liu","raw_affiliation_strings":["Applied Technology College Of Soochow University, SuZhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Technology College Of Soochow University, SuZhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112186303","display_name":"Shengfeng Gu","orcid":"https://orcid.org/0000-0003-0439-4978"},"institutions":[{"id":"https://openalex.org/I4210148536","display_name":"Suzhou Electrical Apparatus Science Academy","ror":"https://ror.org/056tm2d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210148536"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sanhong Gu","raw_affiliation_strings":["Suzhou Dechuang Measurement and Control Technology Co., Ltd., Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Suzhou Dechuang Measurement and Control Technology Co., Ltd., Suzhou, China","institution_ids":["https://openalex.org/I4210148536"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00966784,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":"1","first_page":"181","last_page":"197"},"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":0.9991000294685364,"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":0.9991000294685364,"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":0.9968000054359436,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9921000003814697,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.896145224571228},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8352882862091064},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7689348459243774},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.6789768934249878},{"id":"https://openalex.org/keywords/histogram-equalization","display_name":"Histogram equalization","score":0.6705294847488403},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5789099335670471},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5404847860336304},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5311607122421265},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4949108958244324},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4797286093235016},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4187428951263428},{"id":"https://openalex.org/keywords/image-enhancement","display_name":"Image enhancement","score":0.41000333428382874},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20391488075256348},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10135084390640259}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.896145224571228},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8352882862091064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7689348459243774},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.6789768934249878},{"id":"https://openalex.org/C136943445","wikidata":"https://www.wikidata.org/wiki/Q1970240","display_name":"Histogram equalization","level":4,"score":0.6705294847488403},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5789099335670471},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5404847860336304},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5311607122421265},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4949108958244324},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4797286093235016},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4187428951263428},{"id":"https://openalex.org/C3017601658","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image enhancement","level":3,"score":0.41000333428382874},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20391488075256348},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10135084390640259},{"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:10.2298/csis240911007r","is_oa":true,"landing_page_url":"https://doi.org/10.2298/csis240911007r","pdf_url":"http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142500007R","source":{"id":"https://openalex.org/S206939107","display_name":"Computer Science and Information Systems","issn_l":"1820-0214","issn":["1820-0214","2406-1018"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321031","host_organization_name":"ComSIS Consortium","host_organization_lineage":["https://openalex.org/P4310321031"],"host_organization_lineage_names":["ComSIS Consortium"],"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":"Computer Science and Information Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.2298/csis240911007r","is_oa":true,"landing_page_url":"https://doi.org/10.2298/csis240911007r","pdf_url":"http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142500007R","source":{"id":"https://openalex.org/S206939107","display_name":"Computer Science and Information Systems","issn_l":"1820-0214","issn":["1820-0214","2406-1018"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321031","host_organization_name":"ComSIS Consortium","host_organization_lineage":["https://openalex.org/P4310321031"],"host_organization_lineage_names":["ComSIS Consortium"],"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":"Computer Science and Information Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5299999713897705,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338249","display_name":"Air Force Civil Engineer Center","ror":"https://ror.org/006gmme17"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4406673085.pdf"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W2194775991","https://openalex.org/W2744245146","https://openalex.org/W2766547203","https://openalex.org/W2786406558","https://openalex.org/W2790621718","https://openalex.org/W2898232272","https://openalex.org/W2907998578","https://openalex.org/W2954996726","https://openalex.org/W2963163009","https://openalex.org/W3024054933","https://openalex.org/W3035682985","https://openalex.org/W3102633758","https://openalex.org/W3118545455","https://openalex.org/W3132135025","https://openalex.org/W3164104137","https://openalex.org/W3201615129","https://openalex.org/W3210704155","https://openalex.org/W4207007441","https://openalex.org/W4226380018","https://openalex.org/W4295122181","https://openalex.org/W4308824936","https://openalex.org/W4319597842","https://openalex.org/W4323781174","https://openalex.org/W4362575754","https://openalex.org/W4377013095","https://openalex.org/W4386902839","https://openalex.org/W4388840685","https://openalex.org/W4389264937","https://openalex.org/W4400410093"],"related_works":["https://openalex.org/W2057981026","https://openalex.org/W2256021896","https://openalex.org/W2355760056","https://openalex.org/W1994424557","https://openalex.org/W2024449420","https://openalex.org/W2903465195","https://openalex.org/W2181573213","https://openalex.org/W2398368608","https://openalex.org/W1555939286","https://openalex.org/W2122866860"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,14,51,133],"glove":[4,24,54],"defect":[5,25,55],"classification":[6,26,56],"method":[7],"that":[8,109],"integrates":[9],"image":[10,96],"enhancement":[11,59],"techniques":[12],"with":[13],"lightweight":[15,83],"model":[16,78,84,87,113],"to":[17,139],"enhance":[18],"the":[19,46,82,86,92,103,110,122,140],"efficiency":[20],"and":[21,48,66,75,101,124,131,142],"accuracy":[22,117],"of":[23,34,37,50,94,118],"in":[27],"industrial":[28],"manufacturing.":[29],"A":[30],"dataset":[31,73],"comprising":[32],"images":[33],"five":[35],"types":[36],"gloves":[38],"was":[39,88],"collected,":[40],"totaling":[41],"360":[42],"sample":[43],"images,":[44],"for":[45],"training":[47,136],"validation":[49],"deep":[52],"learning-based":[53],"model.":[57],"Image":[58],"techniques,":[60],"including":[61],"super-pixels,":[62],"exposure":[63],"adjustment,":[64],"blurring,":[65],"limited":[67],"contrast":[68],"adaptive":[69],"histogram":[70],"equalization,":[71],"increased":[72],"diversity":[74],"size,":[76],"improving":[77],"generalization.":[79],"Based":[80],"on":[81,120],"MobileNetV2,":[85],"improved":[89,111],"by":[90],"reducing":[91],"number":[93],"input":[95],"channels":[97],"through":[98],"grayscale":[99],"conversion":[100],"optimizing":[102],"loss":[104],"function.":[105],"Experimental":[106],"results":[107],"demonstrate":[108],"MobileNetV2":[112],"achieved":[114],"an":[115],"average":[116],"97.85%":[119],"both":[121],"original":[123],"enhanced":[125],"datasets,":[126],"effectively":[127],"mitigated":[128],"overfitting":[129],"phenomena,":[130],"exhibited":[132],"significantly":[134],"faster":[135],"speed":[137],"compared":[138],"ResNet34":[141],"ResNet50":[143],"models.":[144]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
