{"id":"https://openalex.org/W4404739765","doi":"https://doi.org/10.1109/icbase63199.2024.10762415","title":"A Traffic Sign Detection Model Based on Improved YOLOv8","display_name":"A Traffic Sign Detection Model Based on Improved YOLOv8","publication_year":2024,"publication_date":"2024-09-20","ids":{"openalex":"https://openalex.org/W4404739765","doi":"https://doi.org/10.1109/icbase63199.2024.10762415"},"language":"en","primary_location":{"id":"doi:10.1109/icbase63199.2024.10762415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbase63199.2024.10762415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101757449","display_name":"Zilong Chen","orcid":"https://orcid.org/0000-0002-2977-1824"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zilong Chen","raw_affiliation_strings":["Xiamen University of Technology,School of Computer and Information Engineering,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University of Technology,School of Computer and Information Engineering,Xiamen,China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418783","display_name":"Li Liu","orcid":"https://orcid.org/0000-0002-2011-2873"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Liu","raw_affiliation_strings":["Xiamen University of Technology,School of Computer and Information Engineering,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University of Technology,School of Computer and Information Engineering,Xiamen,China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047774844","display_name":"Zemin Chen","orcid":"https://orcid.org/0009-0000-5653-4296"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zemin Chen","raw_affiliation_strings":["Xiamen University of Technology,School of Computer and Information Engineering,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University of Technology,School of Computer and Information Engineering,Xiamen,China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000921410","display_name":"Xiaodong Jin","orcid":"https://orcid.org/0000-0002-4416-1055"},"institutions":[{"id":"https://openalex.org/I158809036","display_name":"Shenzhen Institute of Information Technology","ror":"https://ror.org/03wrf9427","country_code":"CN","type":"education","lineage":["https://openalex.org/I158809036"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Jin","raw_affiliation_strings":["Shenzhen SD Information Technology Co.. Ltd,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Shenzhen SD Information Technology Co.. Ltd,Shenzhen,China","institution_ids":["https://openalex.org/I158809036"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101757449"],"corresponding_institution_ids":["https://openalex.org/I75867142"],"apc_list":null,"apc_paid":null,"fwci":0.3383,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6622877,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"672","last_page":"676"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.9451000094413757,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9363999962806702,"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/computer-science","display_name":"Computer science","score":0.6224786043167114},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.5069639682769775},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.47354429960250854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32663464546203613},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06501650810241699}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6224786043167114},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.5069639682769775},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.47354429960250854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32663464546203613},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06501650810241699},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icbase63199.2024.10762415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbase63199.2024.10762415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2193145675","https://openalex.org/W2479866714","https://openalex.org/W2565639579","https://openalex.org/W2601564443","https://openalex.org/W2884367402","https://openalex.org/W2884561390","https://openalex.org/W2963849369","https://openalex.org/W2966926453","https://openalex.org/W3034971973","https://openalex.org/W4246399668","https://openalex.org/W4375953024","https://openalex.org/W6729983426"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W746432122","https://openalex.org/W643638081","https://openalex.org/W1582770384","https://openalex.org/W2369781462","https://openalex.org/W564764569"],"abstract_inverted_index":{"To":[0,30],"address":[1],"the":[2,34,41,48,64,82,90,110,114,148,151],"challenges":[3],"of":[4,43,99,130,150,157],"insufficient":[5],"small":[6,44],"object":[7,13,45],"detection":[8,25],"capability":[9],"and":[10,74,120,159],"inadequate":[11],"multi-scale":[12],"processing":[14],"in":[15,60,122,155],"complex":[16,61,123],"traffic":[17,23],"environments,":[18,124],"this":[19],"study":[20],"proposes":[21],"a":[22,94,103,127],"sign":[24],"model":[26,84,116],"based":[27],"on":[28,89],"YOLOv8.":[29],"overcome":[31],"these":[32],"challenges,":[33],"SPD-Conv":[35],"module":[36,52,66],"is":[37,53,67],"introduced":[38],"to":[39,55,69,109],"enhance":[40],"retention":[42],"features.":[46],"Additionally,":[47],"Deformable":[49],"Convolution":[50],"(Denv3)":[51],"integrated":[54],"improve":[56],"feature":[57,72],"extraction":[58],"accuracy":[59],"backgrounds.":[62],"Finally,":[63],"ASF-SDNet":[65],"utilized":[68],"optimize":[70],"cross-scale":[71],"fusion":[73],"channel":[75],"purification":[76],"capabilities.":[77],"Experimental":[78],"results":[79],"show":[80],"that":[81],"improved":[83,118],"achieves":[85],"significant":[86],"performance":[87],"improvement":[88],"TT100K":[91],"dataset,":[92],"with":[93,139],"mean":[95],"average":[96],"precision":[97],"(mAP@0.5)":[98],"$88.8":[100],"\\%$,":[101],"representing":[102],"4.7":[104],"percentage":[105],"point":[106],"increase":[107],"compared":[108],"original":[111],"model.":[112],"Moreover,":[113],"enhanced":[115],"demonstrates":[117],"robustness":[119],"adaptability":[121],"while":[125],"maintaining":[126],"parameter":[128],"count":[129],"13.3":[131],"M,":[132],"indicating":[133],"high":[134],"computational":[135],"efficiency.":[136],"Comparative":[137],"experiments":[138],"other":[140],"mainstream":[141],"models":[142],"(e.g.,":[143],"Faster-RCNN,":[144],"YOLOv5s)":[145],"also":[146],"highlight":[147],"superiority":[149],"proposed":[152],"model,":[153],"particularly":[154],"terms":[156],"recall":[158],"precision.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}