{"id":"https://openalex.org/W4415974156","doi":"https://doi.org/10.1016/j.procs.2025.09.577","title":"Improving Automatic Check-Out Accuracy with Fine-tuned YOLOv10 and YOLOv12 Models","display_name":"Improving Automatic Check-Out Accuracy with Fine-tuned YOLOv10 and YOLOv12 Models","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415974156","doi":"https://doi.org/10.1016/j.procs.2025.09.577"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2025.09.577","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.577","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2025.09.577","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013685096","display_name":"Rafa\u0142 Skinderowicz","orcid":"https://orcid.org/0000-0002-1712-0688"},"institutions":[{"id":"https://openalex.org/I864159182","display_name":"University of Silesia in Katowice","ror":"https://ror.org/0104rcc94","country_code":"PL","type":"education","lineage":["https://openalex.org/I864159182"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Rafa\u0142 Skinderowicz","raw_affiliation_strings":["University of Silesia in Katowice, Faculty of Science and Technology, Institute of Computer Science, B\u0119dzi\u0144ska 39, 41-205 Sosnowiec, Poland"],"affiliations":[{"raw_affiliation_string":"University of Silesia in Katowice, Faculty of Science and Technology, Institute of Computer Science, B\u0119dzi\u0144ska 39, 41-205 Sosnowiec, Poland","institution_ids":["https://openalex.org/I864159182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5013685096"],"corresponding_institution_ids":["https://openalex.org/I864159182"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35455157,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"270","issue":null,"first_page":"4514","last_page":"4523"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5253000259399414,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5253000259399414,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.05990000069141388,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0478999987244606,"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/set","display_name":"Set (abstract data type)","score":0.5703999996185303},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5346999764442444},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4830999970436096},{"id":"https://openalex.org/keywords/receipt","display_name":"Receipt","score":0.4027000069618225},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.3596000075340271},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.34470000863075256}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.921500027179718},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5703999996185303},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5346999764442444},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4830999970436096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4690000116825104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43149998784065247},{"id":"https://openalex.org/C2778979077","wikidata":"https://www.wikidata.org/wiki/Q330190","display_name":"Receipt","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36800000071525574},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.34470000863075256},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.30809998512268066},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25949999690055847},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2025.09.577","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.577","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2025.09.577","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.577","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2886904239","https://openalex.org/W2921056832","https://openalex.org/W2963769056","https://openalex.org/W2964121718","https://openalex.org/W2971900262","https://openalex.org/W2981976713","https://openalex.org/W3106146634","https://openalex.org/W3194487089","https://openalex.org/W3199729485","https://openalex.org/W4320002812","https://openalex.org/W4384929636","https://openalex.org/W4391467409","https://openalex.org/W4403296739","https://openalex.org/W4403770406","https://openalex.org/W4404801524"],"related_works":[],"abstract_inverted_index":{"Improving":[0],"the":[1,7,31,46,72,110],"retail":[2],"customer":[3],"experience":[4],"includes":[5],"solving":[6],"Automatic":[8],"Check-Out":[9],"(ACO)":[10],"problem,":[11],"which":[12],"involves":[13],"predicting":[14],"a":[15,19,24,39,54,101],"receipt":[16],"based":[17],"on":[18,53,109],"photograph":[20],"of":[21,57,92,105],"products":[22],"in":[23],"checkout":[25,58,83,103],"area.":[26],"Recent":[27],"work":[28,61],"demonstrated":[29],"that":[30],"ACO":[32],"problem":[33],"can":[34,99],"be":[35],"addressed":[36],"by":[37,65,85],"fine-tuning":[38],"strong":[40],"pre-trained":[41],"object":[42],"detection":[43],"model":[44],"from":[45,74,88],"You":[47],"Only":[48],"Look":[49],"Once":[50],"(YOLO)":[51],"series":[52],"limited":[55],"set":[56],"photographs.":[59],"Our":[60],"extends":[62],"this":[63],"research":[64],"introducing":[66],"an":[67,89],"additional":[68],"filtering":[69],"step":[70],"for":[71],"results":[73],"YOLOv10":[75],"and":[76],"YOLOv12":[77],"models.":[78],"Furthermore,":[79],"we":[80],"investigate":[81],"improving":[82],"accuracy":[84,104],"merging":[86],"detections":[87],"ensemble":[90],"(pair)":[91],"different":[93],"YOLO":[94],"variants.":[95],"The":[96],"proposed":[97],"approach":[98],"achieve":[100],"competitive":[102],"up":[106],"to":[107],"96.89%":[108],"RPC":[111],"dataset.":[112]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-06T00:00:00"}