{"id":"https://openalex.org/W7155225957","doi":"https://doi.org/10.1109/access.2026.3686452","title":"Real-Time Detection Method for Cracked Eggs Based on Improved YOLOv8 and Dual Verification With Triple Classification Granularity","display_name":"Real-Time Detection Method for Cracked Eggs Based on Improved YOLOv8 and Dual Verification With Triple Classification Granularity","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7155225957","doi":"https://doi.org/10.1109/access.2026.3686452"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3686452","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3686452","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3686452","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101857555","display_name":"Sun Daxu","orcid":"https://orcid.org/0000-0002-9709-7210"},"institutions":[{"id":"https://openalex.org/I4210120302","display_name":"Shunde Polytechnic","ror":"https://ror.org/01w9d4603","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210120302"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daxu Sun","raw_affiliation_strings":["Foshan Polytechnic, Foshan, China"],"raw_orcid":"https://orcid.org/0000-0002-9709-7210","affiliations":[{"raw_affiliation_string":"Foshan Polytechnic, Foshan, China","institution_ids":["https://openalex.org/I4210120302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134217996","display_name":"Hongfei Wei","orcid":"https://orcid.org/0009-0009-1689-7223"},"institutions":[{"id":"https://openalex.org/I4210120302","display_name":"Shunde Polytechnic","ror":"https://ror.org/01w9d4603","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210120302"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongfei Wei","raw_affiliation_strings":["Foshan Polytechnic, Foshan, China"],"raw_orcid":"https://orcid.org/0009-0009-1689-7223","affiliations":[{"raw_affiliation_string":"Foshan Polytechnic, Foshan, China","institution_ids":["https://openalex.org/I4210120302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010993949","display_name":"Yangfan Luo","orcid":"https://orcid.org/0000-0001-8456-1304"},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangfan Luo","raw_affiliation_strings":["College of Engineering, South China Agricultural University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering, South China Agricultural University, Guangzhou, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134250751","display_name":"Weijian Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210120302","display_name":"Shunde Polytechnic","ror":"https://ror.org/01w9d4603","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210120302"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijian Li","raw_affiliation_strings":["Foshan Polytechnic, Foshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Foshan Polytechnic, Foshan, China","institution_ids":["https://openalex.org/I4210120302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134307507","display_name":"Yingshun Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210120302","display_name":"Shunde Polytechnic","ror":"https://ror.org/01w9d4603","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210120302"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingshun Yu","raw_affiliation_strings":["Foshan Polytechnic, Foshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Foshan Polytechnic, Foshan, China","institution_ids":["https://openalex.org/I4210120302"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.47876912,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"62322","last_page":"62333"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.1412000060081482,"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":0.1412000060081482,"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.03709999844431877,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T13567","display_name":"AI and Multimedia in Education","score":0.02449999935925007,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/granularity","display_name":"Granularity","score":0.7394000291824341},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6333000063896179},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5055000185966492},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.30809998512268066},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.30790001153945923}],"concepts":[{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.7394000291824341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.734000027179718},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6333000063896179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5241000056266785},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5055000185966492},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35740000009536743},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29190000891685486},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3686452","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3686452","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:322f7a494879481f8dc26d07e2e9d80f","is_oa":true,"landing_page_url":"https://doaj.org/article/322f7a494879481f8dc26d07e2e9d80f","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":"IEEE Access, Vol 14, Pp 62322-62333 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3686452","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3686452","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.6002369523048401,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Addressing":[0],"the":[1,71,74,133,145,153,167,197,212,218],"urgent":[2],"need":[3],"for":[4,114,192,201,220,232],"real-time":[5,18,171,222],"detection":[6,19,112,122,160,172],"of":[7,96,127,142,174,182,190,211],"cracked":[8,47,52],"eggs":[9,45,53,203],"on":[10,22,144],"production":[11,164],"lines,":[12],"this":[13],"study":[14,28,227],"proposes":[15],"a":[16,31,35,119,137,149,170,178,186],"high-precision":[17],"algorithm":[20],"based":[21],"an":[23,229],"improved":[24,134],"YOLOv8":[25],"framework.":[26],"This":[27,49,206,226],"innovatively":[29,76],"introduced":[30],"dual-detection":[32],"method,":[33],"incorporating":[34],"“crack":[36],"region”":[37],"classification":[38],"to":[39,83,93,110,124],"assist":[40],"in":[41,100],"distinguishing":[42],"between":[43],"intact":[44,193],"and":[46,67,117,156,185,215],"eggs.":[48],"method":[50],"identifies":[51],"by":[54],"recognizing":[55],"either":[56],"local":[57],"or":[58],"overall":[59],"crack":[60],"characteristics,":[61],"effectively":[62],"reducing":[63],"both":[64],"false":[65,68,179,187,198],"negatives":[66],"positives.":[69],"At":[70],"architectural":[72],"level,":[73],"model":[75,135,155,214],"integrates":[77],"Weighted":[78,102],"Bidirectional":[79],"Feature":[80],"Pyramid":[81],"(BiFPN)":[82],"optimize":[84],"multi-scale":[85],"feature":[86],"fusion,":[87],"Deformable":[88],"Convolutional":[89],"Network":[90],"v2":[91,106],"(DCNv2)":[92],"enhance":[94],"perception":[95],"irregular":[97],"geometric":[98],"deformations":[99],"cracks,":[101],"Intersection":[103],"over":[104,152],"Union":[105],"(WIoUv2)":[107],"loss":[108],"function":[109],"improve":[111],"accuracy":[113],"occluded":[115],"samples,":[116],"adds":[118],"small":[120],"object":[121],"layer":[123],"strengthen":[125],"recognition":[126],"minute":[128],"defects.":[129],"Experiments":[130],"demonstrate":[131],"that":[132,210],"achieves":[136],"mean":[138],"average":[139],"precision":[140],"(mAP)":[141],"92.9%":[143],"test":[146],"set,":[147],"representing":[148],"5.2%":[150],"improvement":[151],"original":[154,213],"outperforming":[157],"current":[158],"mainstream":[159],"models.":[161],"In":[162],"real-world":[163],"deployment":[165],"testing,":[166],"system":[168],"achieved":[169],"speed":[173],"45":[175],"FPS,":[176],"with":[177,236],"negative":[180],"rate":[181,189,200],"only":[183],"0.46%":[184],"positive":[188,199],"2.18%":[191],"eggs,":[194],"while":[195],"keeping":[196],"broken":[202],"below":[204],"2%.":[205],"performance":[207],"far":[208],"surpasses":[209],"fully":[216],"meets":[217],"requirements":[219],"industrial-grade":[221],"online":[223],"quality":[224,234],"inspection.":[225],"provides":[228],"efficient":[230],"solution":[231],"egg":[233],"inspection":[235],"significant":[237],"engineering":[238],"application":[239],"value.":[240]},"counts_by_year":[],"updated_date":"2026-05-04T06:00:05.808912","created_date":"2026-04-23T00:00:00"}