{"id":"https://openalex.org/W4410070844","doi":"https://doi.org/10.3390/sym17050701","title":"Fusion YOLOv8s and Dynamic Convolution Algorithm for Steel Surface Defect Detection","display_name":"Fusion YOLOv8s and Dynamic Convolution Algorithm for Steel Surface Defect Detection","publication_year":2025,"publication_date":"2025-05-04","ids":{"openalex":"https://openalex.org/W4410070844","doi":"https://doi.org/10.3390/sym17050701"},"language":"en","primary_location":{"id":"doi:10.3390/sym17050701","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17050701","pdf_url":"https://www.mdpi.com/2073-8994/17/5/701/pdf?version=1746348872","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"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":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/17/5/701/pdf?version=1746348872","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070699970","display_name":"Chunyan Huang","orcid":"https://orcid.org/0009-0007-4512-9499"},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunyan Huang","raw_affiliation_strings":["School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jingnan Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingnan Cui","raw_affiliation_strings":["School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100363309","display_name":"Yanling Li","orcid":"https://orcid.org/0000-0002-3540-8706"},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanling Li","raw_affiliation_strings":["School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019136600","display_name":"Yao Lu","orcid":"https://orcid.org/0000-0002-0261-0713"},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yao Lu","raw_affiliation_strings":["School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071752299","display_name":"Chunyu Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunyu Yang","raw_affiliation_strings":["School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"],"raw_orcid":"https://orcid.org/0009-0005-9200-6948","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China","institution_ids":["https://openalex.org/I198645480"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5019136600","https://openalex.org/A5100363309"],"corresponding_institution_ids":["https://openalex.org/I198645480"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.9829,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.76604917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"17","issue":"5","first_page":"701","last_page":"701"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9961000084877014,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9833999872207642,"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/convolution","display_name":"Convolution (computer science)","score":0.5810033679008484},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5804767608642578},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5679237246513367},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5625323057174683},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.43615058064460754},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.32798081636428833},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27961021661758423},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27624472975730896},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.1352885365486145},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07720926403999329}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5810033679008484},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5804767608642578},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5679237246513367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5625323057174683},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.43615058064460754},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.32798081636428833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27961021661758423},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27624472975730896},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.1352885365486145},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07720926403999329},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/sym17050701","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17050701","pdf_url":"https://www.mdpi.com/2073-8994/17/5/701/pdf?version=1746348872","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"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":"Symmetry","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/sym17050701","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17050701","pdf_url":"https://www.mdpi.com/2073-8994/17/5/701/pdf?version=1746348872","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"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":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410070844.pdf","grobid_xml":"https://content.openalex.org/works/W4410070844.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2102605133","https://openalex.org/W2163605009","https://openalex.org/W2570343428","https://openalex.org/W2752782242","https://openalex.org/W2884585870","https://openalex.org/W2944303778","https://openalex.org/W2954875452","https://openalex.org/W2962766617","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2982101047","https://openalex.org/W2997747012","https://openalex.org/W3034421924","https://openalex.org/W3122173535","https://openalex.org/W3137333070","https://openalex.org/W3177052299","https://openalex.org/W3194790201","https://openalex.org/W3206309978","https://openalex.org/W4205999200","https://openalex.org/W4225321635","https://openalex.org/W4280553134","https://openalex.org/W4378574635","https://openalex.org/W4382699034","https://openalex.org/W4386076325","https://openalex.org/W4387119704","https://openalex.org/W4408703525","https://openalex.org/W6620707391","https://openalex.org/W6684191040","https://openalex.org/W6767312599","https://openalex.org/W6802790961"],"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/W2137437058"],"abstract_inverted_index":{"The":[0,174],"detection":[1,53,61,197,202],"of":[2,23,34,41,71,112,122,172,188,199],"surface":[3,16,51,181],"defects":[4,17],"in":[5,18,48,74,179,214],"steel":[6,12,50,180],"is":[7],"a":[8,142,160,185,195],"prerequisite":[9],"for":[10],"improving":[11],"quality.":[13],"When":[14],"detecting":[15],"steel,":[19],"the":[20,31,39,69,78,87,94,104,108,113,119,131,150,155,166,192,210],"texture":[21],"features":[22],"defective":[24],"areas":[25,73],"often":[26],"show":[27],"significant":[28],"differences":[29],"from":[30],"symmetry":[32],"patterns":[33],"normal":[35],"areas.":[36],"To":[37,66],"address":[38],"issues":[40],"low":[42],"accuracy":[43,162,203],"and":[44,76,125],"slow":[45],"recognition":[46],"speed":[47,121,198],"existing":[49],"defect":[52,60,182],"methods,":[54],"this":[55],"study":[56],"proposes":[57],"an":[58,170],"improved":[59,156],"method":[62],"based":[63],"on":[64,68,149],"YOLOv8s.":[65],"focus":[67],"information":[70],"asymmetric":[72],"images":[75],"amplify":[77],"model\u2019s":[79],"capacity":[80,111],"to":[81,117,165],"learn":[82],"target":[83,123],"defects,":[84],"we":[85,129],"integrate":[86],"ODConv":[88],"(Omni-Dimensional":[89],"Dynamic":[90],"Convolution)":[91],"module":[92,100],"into":[93],"backbone":[95,114],"feature":[96,109],"extraction":[97,110],"network.":[98,115],"This":[99,208],"infuses":[101],"attention":[102],"within":[103],"convolution":[105],"process,":[106],"augmenting":[107],"Furthermore,":[116],"refine":[118],"regression":[120],"boxes":[124],"enhance":[126],"positioning":[127],"accuracy,":[128],"adopt":[130],"WIoU":[132],"(Wise":[133],"Intersection":[134],"over":[135],"Union)":[136],"bounding":[137],"box":[138],"loss":[139],"function,":[140],"featuring":[141],"dynamic":[143],"non-monotonic":[144],"focusing":[145],"mechanism.":[146],"Experimental":[147],"results":[148],"NEU-DET":[151],"dataset":[152],"reveal":[153],"that":[154],"YOLOv8s-OD":[157],"model":[158,175,193,211],"achieves":[159],"4.5%":[161],"improvement":[163],"compared":[164],"original":[167],"YOLOv8s,":[168],"with":[169],"mAP":[171],"78.9%.":[173],"demonstrates":[176],"robust":[177],"performance":[178],"detection.":[183],"With":[184],"modest":[186],"size":[187],"only":[189],"21.5":[190],"MB,":[191],"sustains":[194],"high":[196],"89FPS,":[200],"elevating":[201],"while":[204],"preserving":[205],"real-time":[206],"performance.":[207],"renders":[209],"highly":[212],"applicable":[213],"real-world":[215],"industrial":[216],"scenarios.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
