{"id":"https://openalex.org/W4281297332","doi":"https://doi.org/10.1109/cscwd54268.2022.9776033","title":"EfficientFCOS: An Efficient One-stage Object Detection Model based on FCOS","display_name":"EfficientFCOS: An Efficient One-stage Object Detection Model based on FCOS","publication_year":2022,"publication_date":"2022-05-04","ids":{"openalex":"https://openalex.org/W4281297332","doi":"https://doi.org/10.1109/cscwd54268.2022.9776033"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd54268.2022.9776033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd54268.2022.9776033","pdf_url":null,"source":{"id":"https://openalex.org/S4363607909","display_name":"2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","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/A5103033745","display_name":"Yan Chu","orcid":"https://orcid.org/0000-0001-7396-8387"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Chu","raw_affiliation_strings":["Harbin Engineering University,Harbin,China","Harbin Engineering University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,Harbin,China","institution_ids":["https://openalex.org/I151727225"]},{"raw_affiliation_string":"Harbin Engineering University, Harbin, China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038048526","display_name":"Jianpeng Guo","orcid":"https://orcid.org/0000-0003-1707-2716"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianpeng Guo","raw_affiliation_strings":["Harbin Engineering University,Harbin,China","Harbin Engineering University, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,Harbin,China","institution_ids":["https://openalex.org/I151727225"]},{"raw_affiliation_string":"Harbin Engineering University, Harbin, China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101576537","display_name":"Wen Shan","orcid":"https://orcid.org/0000-0002-7377-8943"},"institutions":[{"id":"https://openalex.org/I8696757","display_name":"Singapore University of Social Sciences","ror":"https://ror.org/01s57k749","country_code":"SG","type":"education","lineage":["https://openalex.org/I8696757"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wen Shan","raw_affiliation_strings":["Singapore University of Social Sciences,Singapore,Singapore","Singapore University of Social Sciences, Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore University of Social Sciences,Singapore,Singapore","institution_ids":["https://openalex.org/I8696757"]},{"raw_affiliation_string":"Singapore University of Social Sciences, Singapore, Singapore","institution_ids":["https://openalex.org/I8696757"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020485549","display_name":"Zhengkui Wang","orcid":"https://orcid.org/0000-0003-4554-0791"},"institutions":[{"id":"https://openalex.org/I168639165","display_name":"Singapore Institute of Technology","ror":"https://ror.org/01v2c2791","country_code":"SG","type":"education","lineage":["https://openalex.org/I168639165"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhengkui Wang","raw_affiliation_strings":["Singapore Institute of Technology,Singapore,Singapore","Singapore Institute of Technology, Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore Institute of Technology,Singapore,Singapore","institution_ids":["https://openalex.org/I168639165"]},{"raw_affiliation_string":"Singapore Institute of Technology, Singapore, Singapore","institution_ids":["https://openalex.org/I168639165"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0925,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.32407174,"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":"617","last_page":"622"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9980000257492065,"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":0.9916999936103821,"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":0.7126278281211853},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.5895949602127075},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4666905999183655},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.420732319355011},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3521798253059387},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.16547664999961853},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.06187167763710022}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7126278281211853},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.5895949602127075},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4666905999183655},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.420732319355011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3521798253059387},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.16547664999961853},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.06187167763710022},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cscwd54268.2022.9776033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd54268.2022.9776033","pdf_url":null,"source":{"id":"https://openalex.org/S4363607909","display_name":"2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.46000000834465027}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2037227137","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2129987527","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2490270993","https://openalex.org/W2549139847","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2626685773","https://openalex.org/W2804935296","https://openalex.org/W2886904239","https://openalex.org/W2955425717","https://openalex.org/W2962721361","https://openalex.org/W2962766617","https://openalex.org/W2962861284","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963542991","https://openalex.org/W2963857746","https://openalex.org/W2963918968","https://openalex.org/W2982770724","https://openalex.org/W2990792015","https://openalex.org/W2997747012","https://openalex.org/W3012573144","https://openalex.org/W3018757597","https://openalex.org/W3034971973","https://openalex.org/W3040266635","https://openalex.org/W3106250896","https://openalex.org/W3135550350","https://openalex.org/W4293584584","https://openalex.org/W6620707391","https://openalex.org/W6629368666","https://openalex.org/W6639102338","https://openalex.org/W6679461745","https://openalex.org/W6684191040","https://openalex.org/W6750227808","https://openalex.org/W6762718338","https://openalex.org/W6777046832","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2883677709","https://openalex.org/W4312842780","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"Object":[0],"detection":[1,18,61,79],"is":[2,40,188,217],"playing":[3],"an":[4,57],"important":[5],"role":[6],"in":[7],"computer":[8],"vision.":[9],"With":[10],"the":[11,47,71,90,106,116,129,132,137,153,164,172,175,192,196,205,225],"rapid":[12],"development":[13],"of":[14,35,75,113,115,131,167,174],"deep":[15,29],"learning,":[16],"object":[17,60,78],"algorithms":[19],"based":[20,64],"on":[21,65],"convolutional":[22],"neural":[23],"networks":[24],"have":[25],"been":[26],"successful.":[27],"However,":[28],"learning":[30],"requires":[31],"a":[32],"large":[33],"amount":[34],"data":[36,199],"to":[37,42,88,109,128,143,163],"train,":[38],"it":[39,104,135,151,223],"crucial":[41],"improve":[43,70],"model":[44],"efficiency":[45,74],"with":[46],"same":[48],"required":[49],"hardware":[50],"resources.":[51],"In":[52,82],"this":[53],"paper,":[54],"we":[55],"propose":[56],"efficient":[58,190],"one-stage":[59,77],"model,":[62],"EfficientFCOS":[63,84,187,201],"pixel-level":[66],"prediction,":[67,169],"which":[68,94,208,216],"can":[69],"accuracy":[72],"and":[73,98,123,146,161,228,233],"existing":[76,193],"network":[80,122,126,142,214],"model.":[81],"particular,":[83],"first":[85],"uses":[86],"EfficientNet":[87],"extract":[89],"input":[91],"image":[92],"features,":[93],"has":[95,212],"fewer":[96],"parameters":[97],"better":[99],"performance":[100],"than":[101,191,220],"ResNet.":[102],"Next,":[103],"employs":[105],"scaling":[107],"approaches":[108],"uniformly":[110],"scale":[111],"number":[112],"channels":[114],"model\u2019s":[117],"backbone":[118],"network,":[119],"feature":[120,140,148],"fusion":[121],"shared":[124],"head":[125],"according":[127],"resolution":[130],"image.":[133],"Furthermore,":[134],"adopts":[136],"weighted":[138],"bidirectional":[139],"pyramid":[141],"achieve":[144],"fast":[145],"multi-scale":[147],"fusion.":[149],"Meanwhile,":[150,222],"integrates":[152],"geometric":[154],"factors":[155],"(center":[156],"point":[157],"distance,":[158],"overlap":[159],"rate":[160],"scale)":[162],"regression":[165,173],"loss":[166],"target":[168,176],"such":[170],"that":[171,186],"prediction":[177],"box":[178],"becomes":[179],"more":[180,189],"stable.":[181],"Our":[182],"experimental":[183],"results":[184],"indicate":[185],"FCOS.":[194,221],"On":[195],"Pascal":[197],"Voc":[198],"set,":[200],"achieves":[202],"82.1":[203],"for":[204],"mAP":[206],"value,":[207],"increases":[209],"2.8%.":[210],"It":[211],"15M":[213],"parameters,":[215],"4.3x":[218],"smaller":[219],"improves":[224],"GPU":[226],"LAT":[227,230],"CPU":[229],"by":[231],"12.2%":[232],"20.0%":[234],"respectively.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
