{"id":"https://openalex.org/W7147374952","doi":"https://doi.org/10.1109/cnml68938.2026.11452438","title":"An Enhanced YOLOv12 Algorithm Based on Multi-strategy Fusion for Autonomous Driving Object Detection","display_name":"An Enhanced YOLOv12 Algorithm Based on Multi-strategy Fusion for Autonomous Driving Object Detection","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147374952","doi":"https://doi.org/10.1109/cnml68938.2026.11452438"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452438","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452438","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5132596127","display_name":"Kaitong Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I1315279114","display_name":"Zhejiang Wanli University","ror":"https://ror.org/00rjdhd62","country_code":"CN","type":"education","lineage":["https://openalex.org/I1315279114"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaitong Yu","raw_affiliation_strings":["Zhejiang Wanli University,College of Big Data and Software Engineering,Ningbo,China"],"affiliations":[{"raw_affiliation_string":"Zhejiang Wanli University,College of Big Data and Software Engineering,Ningbo,China","institution_ids":["https://openalex.org/I1315279114"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5132596127"],"corresponding_institution_ids":["https://openalex.org/I1315279114"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.91342252,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"610","last_page":"618"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9444000124931335,"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":0.9444000124931335,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.019899999722838402,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.003700000001117587,"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/upsampling","display_name":"Upsampling","score":0.5860999822616577},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5266000032424927},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5052000284194946},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.49970000982284546},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.46059998869895935},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4226999878883362},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.4124000072479248},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4108000099658966},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.40610000491142273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6536999940872192},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.5860999822616577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5690000057220459},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5266000032424927},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5249000191688538},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5052000284194946},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.49970000982284546},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4607999920845032},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.40610000491142273},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36329999566078186},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.33869999647140503},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.33230000734329224},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452438","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452438","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W3097779785","https://openalex.org/W3195218187","https://openalex.org/W4206300761","https://openalex.org/W4214700196","https://openalex.org/W4220832258","https://openalex.org/W4327953388","https://openalex.org/W4390873058","https://openalex.org/W4402454178","https://openalex.org/W4403101145","https://openalex.org/W4407742893","https://openalex.org/W4409173118","https://openalex.org/W4409460869","https://openalex.org/W4410214984","https://openalex.org/W4410780685","https://openalex.org/W4410812862","https://openalex.org/W4411134618"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"limitations":[3],"in":[4,91,151,155],"You":[5],"Only":[6],"Look":[7],"Once":[8],"(YOLO)":[9],"algorithms,":[10],"including":[11],"insufficient":[12],"small-object":[13,67],"detection":[14],"accuracy,":[15],"low":[16],"feature":[17],"utilization,":[18],"imprecise":[19],"bounding":[20,92],"box":[21,93],"regression,":[22],"and":[23,37,95,116,138,153,162,169],"slow":[24],"training":[25],"convergence":[26],"under":[27,165],"complex":[28],"autonomous":[29,109],"driving":[30,110],"scenarios.":[31],"To":[32],"meet":[33],"stringent":[34],"real-time,":[35],"accurate,":[36],"robust":[38],"perception":[39,160],"requirements,":[40],"we":[41],"propose":[42],"YOLOv12-YT,":[43],"a":[44,52,105],"multi-strategy":[45],"enhanced":[46,135],"model.":[47],"Key":[48],"innovations":[49],"include":[50],"integrating":[51],"SimAM":[53],"attention":[54],"module":[55],"after":[56],"the":[57,97],"backbone's":[58],"Area-Attention":[59],"Enhanced":[60],"Cross-Feature":[61],"(A2C2f)":[62],"block":[63],"to":[64,87,100,149],"amplify":[65],"critical":[66],"features.":[68],"replacing":[69],"fixed":[70],"upsampling":[71,75],"with":[72],"DySample":[73],"dynamic":[74],"for":[76],"adaptive":[77],"multi-scale":[78,170],"fusion.":[79],"adopting":[80],"Position":[81],"Intersection":[82],"over":[83],"Union":[84],"(PIoU)":[85],"loss":[86],"enhance":[88],"positional":[89],"sensitivity":[90],"regression.":[94],"introducing":[96],"Nadam":[98],"optimizer":[99],"accelerate":[101],"convergence.":[102],"Evaluated":[103],"on":[104,122],"fused":[106],"PASCAL":[107],"VOC2007+2012":[108],"dataset,":[111],"YOLOv12-YT":[112],"achieves":[113],"81.2%":[114],"precision":[115,152],"77.8%":[117],"mean":[118],"Average":[119],"Precision":[120],"(mAP@50)":[121],"an":[123],"independent":[124],"test":[125],"set.":[126],"Comparative":[127],"experiments":[128],"against":[129],"eight":[130],"mainstream":[131],"models":[132,146],"(including":[133],"five":[134],"YOLOv12":[136],"variants":[137],"three":[139],"baseline":[140],"YOLO":[141],"versions)":[142],"demonstrate":[143],"superiority-surpassing":[144],"sub-optimal":[145],"by":[147],"up":[148],"5.5%":[150],"3.3%":[154],"mAP@50.":[156],"The":[157],"algorithm":[158],"enhances":[159],"reliability":[161],"real-time":[163],"capability":[164],"challenging":[166],"illumination,":[167],"occlusion,":[168],"conditions.":[171]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}
