{"id":"https://openalex.org/W4408997089","doi":"https://doi.org/10.3390/jimaging11040105","title":"Inspection of Defective Glass Bottle Mouths Using Machine Learning","display_name":"Inspection of Defective Glass Bottle Mouths Using Machine Learning","publication_year":2025,"publication_date":"2025-03-29","ids":{"openalex":"https://openalex.org/W4408997089","doi":"https://doi.org/10.3390/jimaging11040105","pmid":"https://pubmed.ncbi.nlm.nih.gov/40278021"},"language":"en","primary_location":{"id":"doi:10.3390/jimaging11040105","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging11040105","pdf_url":"https://www.mdpi.com/2313-433X/11/4/105/pdf?version=1743240445","source":{"id":"https://openalex.org/S2736465063","display_name":"Journal of Imaging","issn_l":"2313-433X","issn":["2313-433X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2313-433X/11/4/105/pdf?version=1743240445","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008079092","display_name":"Daiki Tomita","orcid":null},"institutions":[{"id":"https://openalex.org/I185088104","display_name":"Tokyo City University","ror":"https://ror.org/04dt6bw53","country_code":"JP","type":"education","lineage":["https://openalex.org/I185088104"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Daiki Tomita","raw_affiliation_strings":["Setagaya Campas, Tokyo City University, Tokyo 158-8557, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Setagaya Campas, Tokyo City University, Tokyo 158-8557, Japan","institution_ids":["https://openalex.org/I185088104"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102674470","display_name":"Yue Bao","orcid":"https://orcid.org/0000-0002-5589-1598"},"institutions":[{"id":"https://openalex.org/I185088104","display_name":"Tokyo City University","ror":"https://ror.org/04dt6bw53","country_code":"JP","type":"education","lineage":["https://openalex.org/I185088104"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yue Bao","raw_affiliation_strings":["Setagaya Campas, Tokyo City University, Tokyo 158-8557, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Setagaya Campas, Tokyo City University, Tokyo 158-8557, Japan","institution_ids":["https://openalex.org/I185088104"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008079092"],"corresponding_institution_ids":["https://openalex.org/I185088104"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1732},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1732},"fwci":2.9488,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.90364882,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"11","issue":"4","first_page":"105","last_page":"105"},"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":0.9979000091552734,"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":0.9979000091552734,"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/bottle","display_name":"Bottle","score":0.902112603187561},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6130878925323486},{"id":"https://openalex.org/keywords/thread","display_name":"Thread (computing)","score":0.563956618309021},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.5600661039352417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5294575095176697},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.42666077613830566},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4163089990615845},{"id":"https://openalex.org/keywords/engineering-drawing","display_name":"Engineering drawing","score":0.3233058452606201},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.24450409412384033},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2175363302230835},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.123026043176651}],"concepts":[{"id":"https://openalex.org/C32236832","wikidata":"https://www.wikidata.org/wiki/Q80228","display_name":"Bottle","level":2,"score":0.902112603187561},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6130878925323486},{"id":"https://openalex.org/C138101251","wikidata":"https://www.wikidata.org/wiki/Q213092","display_name":"Thread (computing)","level":2,"score":0.563956618309021},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.5600661039352417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5294575095176697},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.42666077613830566},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4163089990615845},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.3233058452606201},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.24450409412384033},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2175363302230835},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.123026043176651},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/jimaging11040105","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging11040105","pdf_url":"https://www.mdpi.com/2313-433X/11/4/105/pdf?version=1743240445","source":{"id":"https://openalex.org/S2736465063","display_name":"Journal of Imaging","issn_l":"2313-433X","issn":["2313-433X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging","raw_type":"journal-article"},{"id":"pmid:40278021","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40278021","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of imaging","raw_type":null},{"id":"pmh:oai:doaj.org/article:09aa352f63f347d7a10938e0e603dc1f","is_oa":true,"landing_page_url":"https://doaj.org/article/09aa352f63f347d7a10938e0e603dc1f","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":"Journal of Imaging, Vol 11, Iss 4, p 105 (2025)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:12028236","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12028236","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Imaging","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/jimaging11040105","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging11040105","pdf_url":"https://www.mdpi.com/2313-433X/11/4/105/pdf?version=1743240445","source":{"id":"https://openalex.org/S2736465063","display_name":"Journal of Imaging","issn_l":"2313-433X","issn":["2313-433X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4408997089.pdf"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W2101892598","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2963163009","https://openalex.org/W2981035923","https://openalex.org/W4313597885","https://openalex.org/W4317425876","https://openalex.org/W4367664123","https://openalex.org/W4408126561","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2361630154","https://openalex.org/W2371085739","https://openalex.org/W4295815926","https://openalex.org/W2073257261","https://openalex.org/W3005230903","https://openalex.org/W2370457247","https://openalex.org/W2321188953","https://openalex.org/W1905048983","https://openalex.org/W745496718","https://openalex.org/W3021242879"],"abstract_inverted_index":{"In":[0,19,312],"this":[1,182,215],"study,":[2,183],"we":[3],"proposed":[4,217,247,305,353],"a":[5,186,218,232,313,320],"method":[6,219,306],"for":[7,29,130],"detecting":[8],"chips":[9,150,360],"in":[10,32,47,151,177,181,185,361],"the":[11,38,48,65,69,73,86,102,125,154,164,167,174,178,199,204,207,223,228,243,246,259,264,271,274,278,281,287,290,294,297,304,308,322,325,331,337,340,352],"mouth":[12,208,261,283,333],"of":[13,40,50,67,72,104,119,127,188,203,245,257,273,280,289,296,324,330,339,345],"glass":[14,24,51,74,131,152,156,179],"bottles":[15,25,57,132,157,180],"using":[16,133,231,255,277,293,316,328,343],"machine":[17,137,191,234],"learning.":[18],"recent":[20],"years,":[21],"Japanese":[22],"cosmetic":[23,56],"have":[26,84,93],"gained":[27],"attention":[28],"their":[30],"advancements":[31],"manufacturing":[33],"technology":[34],"and":[35,81,111,136,163,201,237,262],"eco-friendliness":[36],"through":[37],"use":[39],"recycled":[41],"glass,":[42],"leading":[43],"to":[44,60,109,198,220,358],"an":[45],"increase":[46],"volume":[49],"bottle":[52,75,229],"exports":[53],"overseas.":[54],"Although":[55],"are":[58,158,195],"subject":[59],"strict":[61],"quality":[62],"inspections":[63,79,83,89],"from":[64,107,166,209,227],"standpoint":[66],"safety,":[68],"complicated":[70],"shape":[71],"mouths":[76],"makes":[77],"automated":[78],"difficult,":[80],"visual":[82],"been":[85,140],"norm.":[87],"Visual":[88],"conducted":[90],"by":[91,173,252,310],"workers":[92],"become":[94,99],"problematic":[95],"because":[96],"it":[97],"has":[98,139],"clear":[100],"that":[101,112,270,303,351],"standard":[103],"judgment":[105],"differs":[106],"worker":[108,110],"inspection":[113,128,193],"accuracy":[114,249,272,288,309,323,338],"deteriorates":[115],"after":[116],"long":[117],"hours":[118],"work.":[120],"To":[121,211,241],"address":[122],"these":[123,213],"issues,":[124],"development":[126],"systems":[129],"image":[134,145,279,295],"processing":[135,146],"learning":[138,235],"actively":[141],"pursued.":[142],"While":[143],"conventional":[144],"methods":[147,194],"can":[148,355],"detect":[149,359],"bottles,":[153],"target":[155],"those":[159],"without":[160],"screw":[161,175,224,265,298,346],"threads,":[162],"light":[165,168],"source":[169],"is":[170],"diffusely":[171],"reflected":[172],"threads":[176,299,347],"resulting":[184],"loss":[187],"accuracy.":[189],"Additionally,":[190],"learning-based":[192],"generally":[196],"limited":[197],"body":[200],"bottom":[202],"bottle,":[205],"excluding":[206],"analysis.":[210],"overcome":[212],"challenges,":[214],"study":[216],"extract":[221],"only":[222],"thread":[225],"regions":[226],"image,":[230],"dedicated":[233],"model,":[236],"perform":[238],"defect":[239],"detection.":[240],"evaluate":[242],"effectiveness":[244],"approach,":[248],"was":[250,284,300,334,348],"assessed":[251],"training":[253],"models":[254],"images":[256,329,344],"both":[258],"entire":[260,282,332],"just":[263],"threads.":[266],"Experimental":[267],"results":[268],"showed":[269],"model":[275,291,326,341],"trained":[276,292,327,342],"98.0%,":[285],"while":[286],"99.7%,":[301,335],"indicating":[302,350],"improves":[307],"1.7%.":[311],"demonstration":[314],"experiment":[315],"data":[317],"obtained":[318],"at":[319],"factory,":[321],"whereas":[336],"100%,":[349],"system":[354],"be":[356],"used":[357],"factories.":[362]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}