{"id":"https://openalex.org/W3092127093","doi":"https://doi.org/10.1109/case48305.2020.9216756","title":"Asymmetric Convolution Networks Based on Multi-feature Fusion for Object Detection","display_name":"Asymmetric Convolution Networks Based on Multi-feature Fusion for Object Detection","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W3092127093","doi":"https://doi.org/10.1109/case48305.2020.9216756","mag":"3092127093"},"language":"en","primary_location":{"id":"doi:10.1109/case48305.2020.9216756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case48305.2020.9216756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","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/A5089143504","display_name":"Zhenkun Yang","orcid":"https://orcid.org/0000-0002-6377-6629"},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenkun Yang","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China","institution_ids":["https://openalex.org/I67001856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110453313","display_name":"Xianghua Ma","orcid":"https://orcid.org/0000-0002-6377-6629"},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianghua Ma","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China","institution_ids":["https://openalex.org/I67001856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068803477","display_name":"Jing An","orcid":"https://orcid.org/0000-0002-3946-3526"},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing An","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China","institution_ids":["https://openalex.org/I67001856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67001856"],"apc_list":null,"apc_paid":null,"fwci":0.2937,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57584683,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1355","last_page":"1360"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9973999857902527,"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":0.8185733556747437},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7184038162231445},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.677005410194397},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6211403608322144},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.5984547138214111},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5468929409980774},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5441932678222656},{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.5372227430343628},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5233111381530762},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4926608204841614},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46875429153442383},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.4600429832935333},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45972129702568054},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3986421227455139},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24007761478424072},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13603618741035461}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8185733556747437},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7184038162231445},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.677005410194397},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6211403608322144},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.5984547138214111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5468929409980774},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5441932678222656},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.5372227430343628},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5233111381530762},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4926608204841614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46875429153442383},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.4600429832935333},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45972129702568054},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3986421227455139},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24007761478424072},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13603618741035461},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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:10.1109/case48305.2020.9216756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case48305.2020.9216756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","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":25,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1650736245","https://openalex.org/W1836465849","https://openalex.org/W1996901117","https://openalex.org/W2031489346","https://openalex.org/W2183341477","https://openalex.org/W2407521645","https://openalex.org/W2547954583","https://openalex.org/W2579985080","https://openalex.org/W2883780447","https://openalex.org/W2907868778","https://openalex.org/W2922005503","https://openalex.org/W2950800384","https://openalex.org/W2950967261","https://openalex.org/W2963037989","https://openalex.org/W2973307622","https://openalex.org/W2999972254","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W6637078681","https://openalex.org/W6638667902","https://openalex.org/W6732243160","https://openalex.org/W6750227808","https://openalex.org/W6754921332","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2369061952","https://openalex.org/W2367122702","https://openalex.org/W1601492201","https://openalex.org/W4231036715","https://openalex.org/W2132989621","https://openalex.org/W2383495548","https://openalex.org/W2370645350","https://openalex.org/W2015447694","https://openalex.org/W1969590113","https://openalex.org/W3177249605"],"abstract_inverted_index":{"Real-time":[0],"object":[1,23,50,66],"detection":[2,28,31,63,72],"on":[3,176,197],"mobile":[4],"platforms":[5],"is":[6,16,33,59],"a":[7,26,45,70,109,139,162,198],"crucial":[8],"but":[9],"challenging":[10],"computer":[11],"vision":[12],"task.":[13],"However,":[14],"it":[15],"widely":[17],"recognized":[18],"that":[19,114],"although":[20],"the":[21,30,62,75,101,120,143,150,154,168,185],"lightweight":[22,47],"detectors":[24,67],"have":[25],"high":[27,71],"speed,":[29],"accuracy":[32,64],"relatively":[34],"low.":[35],"To":[36],"address":[37],"this":[38,41,158],"problem,":[39],"in":[40,105,128],"paper,":[42],"we":[43,78,137,160],"propose":[44],"new":[46],"one-stage":[48],"generic":[49],"detector,":[51],"named":[52],"ACFNet.":[53],"The":[54],"design":[55,79,104,138],"goal":[56],"of":[57,65,97,171,187],"ACFNet":[58],"to":[60,93,118,130,167],"improve":[61],"while":[68],"maintaining":[69],"speed.":[73],"In":[74,142],"backbone":[76,102,110],"part,":[77],"an":[80],"asymmetric":[81,90],"convolution":[82,91],"unit,":[83],"namely":[84],"AC":[85,116],"block,":[86,145],"which":[87],"employs":[88],"three":[89],"branches":[92],"enhance":[94],"multi-feature":[95],"representation":[96],"CNNs.":[98],"We":[99],"follow":[100],"part":[103],"ShuffleNetV2,":[106],"and":[107,180,191],"present":[108],"network":[111,151],"called":[112],"ACNet":[113],"exploits":[115],"blocks":[117],"replace":[119],"standard":[121],"convolutional":[122],"layers,":[123],"e.g.,":[124],"3\u00d73":[125],"layers.":[126],"Besides,":[127],"order":[129],"make":[131,149],"all":[132],"feature":[133,155],"maps":[134],"stronger":[135],"semantically,":[136],"deconvolution":[140],"block.":[141],"prediction":[144,173],"spatial":[146,163],"attention":[147,164],"can":[148,192],"better":[152],"locate":[153],"distribution.":[156],"Following":[157],"principle,":[159],"add":[161],"building":[165],"block":[166,170],"residual":[169],"each":[172],"layer.":[174],"Experiments":[175],"PASCAL":[177],"VOC":[178,181],"2007":[179],"2012":[182],"datasets":[183],"demonstrate":[184],"effectiveness":[186],"our":[188],"proposed":[189],"method":[190],"run":[193],"at":[194],"19":[195],"FPS":[196],"single":[199],"GPU.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}