{"id":"https://openalex.org/W4402353352","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650645","title":"DMAC-YOLO: A High-Precision YOLO v5s Object Detection Model with a Novel Optimizer","display_name":"DMAC-YOLO: A High-Precision YOLO v5s Object Detection Model with a Novel Optimizer","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402353352","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650645"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5103213221","display_name":"Chao Meng","orcid":"https://orcid.org/0000-0001-7435-4774"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Meng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Laboratory for Intelligent Cyber-Physical System,Beijing,China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Laboratory for Intelligent Cyber-Physical System,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100648903","display_name":"Shaohua Liu","orcid":"https://orcid.org/0000-0002-9374-0192"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohua Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Laboratory for Intelligent Cyber-Physical System,Beijing,China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Laboratory for Intelligent Cyber-Physical System,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115602184","display_name":"Yang Yu","orcid":"https://orcid.org/0000-0001-6584-4050"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Yang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Laboratory for Intelligent Cyber-Physical System,Beijing,China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Laboratory for Intelligent Cyber-Physical System,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.2629,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53633694,"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":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7296442985534668},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6846779584884644},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5750339031219482},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5531651377677917},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4631499648094177},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4454197585582733},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.20403045415878296},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0473155677318573}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7296442985534668},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6846779584884644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5750339031219482},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5531651377677917},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4631499648094177},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4454197585582733},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.20403045415878296},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0473155677318573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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":24,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1955857676","https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2565639579","https://openalex.org/W2884585870","https://openalex.org/W2963037989","https://openalex.org/W2963698657","https://openalex.org/W2964159641","https://openalex.org/W2988916019","https://openalex.org/W2990230185","https://openalex.org/W3001083904","https://openalex.org/W3042011474","https://openalex.org/W3106250896","https://openalex.org/W3132064345","https://openalex.org/W3164031824","https://openalex.org/W3168954534","https://openalex.org/W3177326532","https://openalex.org/W3183827394","https://openalex.org/W3212386989","https://openalex.org/W4246399668","https://openalex.org/W4320002812","https://openalex.org/W4400315380","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"YOLO":[0,38,98,119],"v5s":[1,39,99,120],"is":[2,25,150],"one":[3],"of":[4,49,95],"the":[5,44,50,78,92,96,102,106,118,122,138,143,146,167,171],"commonly":[6],"used":[7],"one-stage":[8],"object":[9],"detection":[10],"algorithms":[11],"currently,":[12],"known":[13],"for":[14,53],"its":[15,22,26],"small":[16],"size":[17],"and":[18,56,76,86,100,131,156,182],"fast":[19],"speed.":[20,183],"However,":[21],"main":[23],"limitation":[24],"lower":[27],"accuracy.":[28],"To":[29],"address":[30],"this":[31,33],"issue,":[32],"paper":[34],"proposes":[35],"an":[36,47],"improved":[37,151],"model,":[40,121],"DMAC-YOLO,":[41],"which":[42],"utilizes":[43],"AdamPlus":[45],"optimizer,":[46,52],"enhancement":[48],"Adam":[51],"higher":[54],"accuracy":[55,181],"faster":[57],"convergence":[58],"compared":[59,116],"to":[60,70,82,117,129,135,154,160,173],"traditional":[61],"optimizers.":[62],"The":[63],"model":[64,124,172],"adopts":[65],"a":[66,178],"Decoupled":[67],"Head":[68],"approach":[69],"improve":[71],"gradient":[72],"propagation":[73],"during":[74],"training":[75],"introduces":[77],"SIoU":[79],"Loss":[80],"function":[81],"reduce":[83],"false":[84],"positives":[85],"missed":[87],"detections.":[88],"Additionally,":[89],"by":[90,127,133,152,158],"improving":[91],"network":[93],"structure":[94],"original":[97],"incorporating":[101],"CBAM":[103],"attention":[104],"mechanism,":[105],"model's":[107,148],"feature":[108],"extraction":[109],"capabilities":[110],"are":[111],"enhanced.":[112],"Experiments":[113],"show":[114],"that":[115,166],"DMAC-YOLO":[123,147],"increases":[125],"mAP@0.5":[126,149],"6.0%":[128],"84.3%":[130],"mAP@0.95":[132,157],"13.2%":[134],"64.2%":[136],"on":[137],"PASCAL":[139],"VOC":[140],"dataset.":[141],"On":[142],"COCO":[144],"dataset,":[145],"4.0%":[153],"56.7%,":[155],"4.2%":[159],"37.4%.":[161],"Ablation":[162],"experiments":[163],"also":[164],"demonstrate":[165],"proposed":[168],"improvements":[169],"enable":[170],"converge":[174],"quickly":[175],"while":[176],"maintaining":[177],"balance":[179],"between":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
