{"id":"https://openalex.org/W4404919497","doi":"https://doi.org/10.3390/jimaging10120309","title":"FastQAFPN-YOLOv8s-Based Method for Rapid and Lightweight Detection of Walnut Unseparated Material","display_name":"FastQAFPN-YOLOv8s-Based Method for Rapid and Lightweight Detection of Walnut Unseparated Material","publication_year":2024,"publication_date":"2024-12-02","ids":{"openalex":"https://openalex.org/W4404919497","doi":"https://doi.org/10.3390/jimaging10120309","pmid":"https://pubmed.ncbi.nlm.nih.gov/39728206"},"language":"en","primary_location":{"id":"doi:10.3390/jimaging10120309","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging10120309","pdf_url":"https://www.mdpi.com/2313-433X/10/12/309/pdf?version=1733146085","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/10/12/309/pdf?version=1733146085","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039939205","display_name":"Junqiu Li","orcid":"https://orcid.org/0000-0002-3751-6233"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junqiu Li","raw_affiliation_strings":["College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100449261","display_name":"Jiayi Wang","orcid":"https://orcid.org/0000-0002-5028-3470"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayi Wang","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109641760","display_name":"Dexiao Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dexiao Kong","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100724382","display_name":"Qinghui Zhang","orcid":"https://orcid.org/0000-0002-8407-6170"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qinghui Zhang","raw_affiliation_strings":["Key Laboratory of State Forestry and GrassIand Administration on Forestry Ecological Big Data, Southwest Forestry University, Kunming 650224, China"],"raw_orcid":"https://orcid.org/0000-0002-8407-6170","affiliations":[{"raw_affiliation_string":"Key Laboratory of State Forestry and GrassIand Administration on Forestry Ecological Big Data, Southwest Forestry University, Kunming 650224, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012527861","display_name":"Zhenping Qiang","orcid":"https://orcid.org/0000-0002-5630-9408"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenping Qiang","raw_affiliation_strings":["College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China"],"raw_orcid":"https://orcid.org/0000-0002-5630-9408","affiliations":[{"raw_affiliation_string":"College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China","institution_ids":["https://openalex.org/I25399270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100724382"],"corresponding_institution_ids":["https://openalex.org/I25399270"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1732},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1732},"fwci":0.2798,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58807398,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"10","issue":"12","first_page":"309","last_page":"309"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12314","display_name":"Nuts composition and effects","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12314","display_name":"Nuts composition and effects","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12388","display_name":"Identification and Quantification in Food","score":0.968999981880188,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7538204193115234},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.6153788566589355},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.569019079208374},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5512410998344421},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5391998291015625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5310015678405762},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5233939290046692},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.4891686737537384},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14014029502868652}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7538204193115234},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.6153788566589355},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.569019079208374},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5512410998344421},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5391998291015625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5310015678405762},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5233939290046692},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.4891686737537384},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14014029502868652},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/jimaging10120309","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging10120309","pdf_url":"https://www.mdpi.com/2313-433X/10/12/309/pdf?version=1733146085","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:39728206","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39728206","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:pubmedcentral.nih.gov:11679546","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11679546","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11679546/pdf/jimaging-10-00309.pdf","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"},{"id":"pmh:oai:doaj.org/article:c6f18d7bc7164214998157bdffdd1b9a","is_oa":true,"landing_page_url":"https://doaj.org/article/c6f18d7bc7164214998157bdffdd1b9a","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 10, Iss 12, p 309 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/jimaging10120309","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging10120309","pdf_url":"https://www.mdpi.com/2313-433X/10/12/309/pdf?version=1733146085","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":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.7300000190734863}],"awards":[{"id":"https://openalex.org/G8054204917","display_name":null,"funder_award_id":"12163004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324721","display_name":"National Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4404919497.pdf"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W2015779785","https://openalex.org/W2067043910","https://openalex.org/W2963037989","https://openalex.org/W2963125010","https://openalex.org/W2979365173","https://openalex.org/W3003072343","https://openalex.org/W3122173535","https://openalex.org/W3126734216","https://openalex.org/W3149571784","https://openalex.org/W3156982815","https://openalex.org/W3168123399","https://openalex.org/W3210586215","https://openalex.org/W3215087236","https://openalex.org/W4238732474","https://openalex.org/W4298130778","https://openalex.org/W4304128474","https://openalex.org/W4308527059","https://openalex.org/W4310187935","https://openalex.org/W4318816569","https://openalex.org/W4321252951","https://openalex.org/W4379161265","https://openalex.org/W4386047745","https://openalex.org/W6804238019"],"related_works":["https://openalex.org/W4249847449","https://openalex.org/W44395729","https://openalex.org/W2765338038","https://openalex.org/W1496225612","https://openalex.org/W4206776094","https://openalex.org/W3154920669","https://openalex.org/W3121197456","https://openalex.org/W2977517636","https://openalex.org/W1969179582","https://openalex.org/W2810129309"],"abstract_inverted_index":{"Walnuts":[0],"possess":[1],"significant":[2],"nutritional":[3],"and":[4,8,13,33,72,83,134,148,191,222,254,260,263,271,278,286,290],"economic":[5],"value.":[6],"Fast":[7],"accurate":[9],"sorting":[10,146],"of":[11,19,36,79,91,152,155,175,181,189,195,205],"shells":[12],"kernels":[14],"will":[15],"enhance":[16],"the":[17,49,55,60,77,80,84,88,92,94,104,119,139,166,200,203,216,235,248,265],"efficiency":[18],"automated":[20],"production.":[21],"Therefore,":[22],"we":[23,141],"propose":[24],"a":[25,114,129,143,150,172,178,192,240],"FastQAFPN-YOLOv8s":[26,297],"object":[27],"detection":[28,35,298],"network":[29,85,99,132,168,246],"to":[30,47,102,127,234,258,284,293],"achieve":[31],"rapid":[32],"precise":[34],"unsorted":[37],"materials.":[38],"The":[39,65,162,245,275,296],"method":[40,299],"uses":[41],"lightweight":[42,236],"Pconv":[43],"(Partial":[44],"Convolution)":[45],"operators":[46],"build":[48],"FasterNextBlock":[50],"structure,":[51],"which":[52],"serves":[53],"as":[54],"backbone":[56],"feature":[57,62,96,124],"extractor":[58],"for":[59,123,160],"Fasternet":[61],"extraction":[63,98,126],"network.":[64],"ECIoU":[66],"loss":[67],"function,":[68],"combining":[69],"EIoU":[70],"(Efficient-IoU)":[71],"CIoU":[73],"(Complete-IoU),":[74],"speeds":[75],"up":[76],"adjustment":[78],"prediction":[81],"frame":[82,193,223,266],"regression.":[86],"In":[87],"Neck":[89],"section":[90],"network,":[93],"QAFPN":[95],"fusion":[97,125],"is":[100,231],"proposed":[101],"replace":[103],"PAN-FPN":[105],"(Path":[106],"Aggregation":[107],"Network-Feature":[108],"Pyramid":[109],"Network)":[110],"in":[111,239,243],"YOLOv8s":[112],"with":[113,199,210],"Rep-PAN":[115],"structure":[116],"based":[117],"on":[118],"QARepNext":[120],"reparameterization":[121],"framework":[122],"strike":[128],"balance":[130],"between":[131],"performance":[133],"inference":[135],"speed.":[136],"To":[137],"validate":[138],"method,":[140],"built":[142],"three-axis":[144],"mobile":[145],"device":[147],"created":[149],"dataset":[151],"3000":[153],"images":[154],"walnuts":[156],"after":[157],"shell":[158],"removal":[159],"experiments.":[161],"results":[163],"show":[164,280],"that":[165],"improved":[167,225,287],"contains":[169],"6071008":[170],"parameters,":[171],"training":[173,211],"time":[174,212],"2.49":[176],"h,":[177],"model":[179,217,249,302],"size":[180,218,250,303],"12.3":[182],"MB,":[183],"an":[184],"mAP":[185,279],"(Mean":[186],"Average":[187],"Precision)":[188],"94.5%,":[190],"rate":[194,224,267],"52.1":[196],"FPS.":[197],"Compared":[198],"original":[201],"model,":[202],"number":[204],"parameters":[206],"decreased":[207],"by":[208,214,220,226,251,268,288],"45.5%,":[209],"reduced":[213],"32.7%,":[215],"shrunk":[219],"45.3%,":[221],"40.8%.":[227],"However,":[228],"some":[229],"accuracy":[230],"sacrificed":[232],"due":[233],"design,":[237],"resulting":[238],"1.2%":[241],"decrease":[242],"mAP.":[244],"reduces":[247,301],"59.7":[252],"MB":[253,256],"23.9":[255],"compared":[257,283,292],"YOLOv7":[259,285],"YOLOv6,":[261,294],"respectively,":[262],"improves":[264],"15.67":[269],"fps":[270],"22.55":[272],"fps,":[273],"respectively.":[274,295],"average":[276],"confidence":[277],"minimal":[281],"changes":[282],"4.2%":[289],"2.4%":[291],"effectively":[300],"while":[304],"maintaining":[305],"recognition":[306],"accuracy.":[307]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
