{"id":"https://openalex.org/W4410568382","doi":"https://doi.org/10.32604/cmc.2025.064629","title":"An Improved Aluminum Surface Defect Detection Algorithm Based on YOLOv8n","display_name":"An Improved Aluminum Surface Defect Detection Algorithm Based on YOLOv8n","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410568382","doi":"https://doi.org/10.32604/cmc.2025.064629"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.064629","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.064629","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.064629","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070125095","display_name":"Hao Qiu","orcid":"https://orcid.org/0000-0002-5137-0306"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hao Qiu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016261600","display_name":"Shoudong Ni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shoudong Ni","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5070125095"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.6319,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.92731801,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"84","issue":"2","first_page":"2677","last_page":"2697"},"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":0.8913999795913696,"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":0.8913999795913696,"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/aluminium","display_name":"Aluminium","score":0.5893964767456055},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.5015685558319092},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4846532940864563},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4496498703956604},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.41418713331222534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3534155488014221},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23190850019454956},{"id":"https://openalex.org/keywords/metallurgy","display_name":"Metallurgy","score":0.15184926986694336},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.14336469769477844}],"concepts":[{"id":"https://openalex.org/C513153333","wikidata":"https://www.wikidata.org/wiki/Q663","display_name":"Aluminium","level":2,"score":0.5893964767456055},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.5015685558319092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4846532940864563},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4496498703956604},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.41418713331222534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3534155488014221},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23190850019454956},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.15184926986694336},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.14336469769477844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.064629","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.064629","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.064629","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.064629","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2193145675","https://openalex.org/W2982770724","https://openalex.org/W3021494530","https://openalex.org/W4220707259","https://openalex.org/W4367368303","https://openalex.org/W4390629764","https://openalex.org/W4399358334","https://openalex.org/W4401422788","https://openalex.org/W4402775306","https://openalex.org/W4411939329"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W4404995717","https://openalex.org/W2016187641","https://openalex.org/W4404725684","https://openalex.org/W4246450666","https://openalex.org/W4388998267","https://openalex.org/W4409278740","https://openalex.org/W2051487156","https://openalex.org/W2898370298","https://openalex.org/W4410118003"],"abstract_inverted_index":{"In":[0],"response":[1],"to":[2,58,105,118,134,168],"the":[3,13,60,64,68,72,78,93,107,113,116,136,141,145,149,154,157,164,170,177,187,191,194,201,216,229],"missed":[4],"and":[5,17,52,76,100,140,151,173,205,212,215,239],"false":[6],"detections":[7],"that":[8,182,185],"are":[9,208],"easily":[10],"caused":[11],"by":[12,45,199,210,220],"large":[14],"variety":[15],"of":[16,67,115,138,144,153,176,186,193,203,236],"significant":[18],"differences":[19],"among":[20],"aluminum":[21],"surface":[22,244],"defects,":[23],"a":[24,40,47,53,125],"detection":[25,122,129,227],"algorithm":[26,196,231],"based":[27],"on":[28],"an":[29,84,101],"improved":[30,195,230],"You":[31],"Only":[32],"Look":[33],"Once":[34],"(YOLO)v8n":[35],"network":[36,117],"is":[37,43,90,103,132,197,218],"proposed.":[38],"First,":[39],"C2f_DWR_DRB":[41],"module":[42,51,62,146],"constructed":[44],"introducing":[46],"dilation-wise":[48],"residual":[49],"(DWR)":[50],"dilated":[54],"reparameterization":[55],"block":[56],"(DRB)":[57],"replace":[59,106],"C2f":[61],"at":[63],"high":[65],"level":[66],"backbone":[69],"network,":[70],"enriching":[71],"gradient":[73],"flow":[74],"information":[75],"increasing":[77],"effective":[79],"receptive":[80],"field":[81],"(ERF).":[82],"Second,":[83],"efficient":[85],"local":[86],"attention":[87],"(ELA)":[88],"mechanism":[89],"fused":[91],"with":[92,120,184,223],"high-level":[94],"screening-feature":[95],"pyramid":[96],"networks":[97],"(HS-FPN)":[98],"module,":[99,111],"ELA_HSFPN":[102],"designed":[104],"original":[108,165,188],"feature":[109],"fusion":[110],"enhancing":[112,148],"ability":[114],"cope":[119],"multiscale":[121],"tasks.":[123],"Moreover,":[124],"lightweight":[126],"shared":[127],"convolutional":[128],"head":[130],"(SCDH)":[131],"introduced":[133],"reduce":[135],"number":[137,202],"parameters":[139,204],"computational":[142,206],"complexity":[143,207],"while":[147],"performance":[150],"generalizability":[152,242],"model.":[155,178],"Finally,":[156],"soft":[158],"intersection":[159],"over":[160],"union":[161],"(SIoU)":[162],"replaces":[163],"loss":[166],"function":[167],"improve":[169],"convergence":[171],"speed":[172],"prediction":[174],"accuracy":[175],"Experimental":[179],"results":[180],"show":[181],"compared":[183],"YOLOv8n":[189],"model,":[190],"mAP@0.5":[192],"increased":[198,219],"5.1%,":[200],"reduced":[209],"33.3%":[211],"32.1%,":[213],"respectively,":[214],"FPS":[217],"4.9%.":[221],"Compared":[222],"other":[224,248],"mainstream":[225],"object":[226],"algorithms,":[228],"still":[232],"leads":[233],"in":[234,247],"terms":[235],"core":[237],"indicators":[238],"has":[240],"good":[241],"for":[243],"defects":[245],"encountered":[246],"industrial":[249],"scenarios.":[250]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-04T08:04:53.788161","created_date":"2025-10-10T00:00:00"}