{"id":"https://openalex.org/W4312619503","doi":"https://doi.org/10.1109/icpr56361.2022.9956221","title":"Ghost-YOLOX: A Lightweight and Efficient Implementation of Object Detection Model","display_name":"Ghost-YOLOX: A Lightweight and Efficient Implementation of Object Detection Model","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312619503","doi":"https://doi.org/10.1109/icpr56361.2022.9956221"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956221","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956221","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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 26th International Conference on Pattern Recognition (ICPR)","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/A5103162139","display_name":"Chunzhi Wang","orcid":"https://orcid.org/0000-0002-9620-3421"},"institutions":[{"id":"https://openalex.org/I74525822","display_name":"Hubei University of Technology","ror":"https://ror.org/02d3fj342","country_code":"CN","type":"education","lineage":["https://openalex.org/I74525822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chun-Zhi Wang","raw_affiliation_strings":["Hubei University of Technology,School of Computing,Wuhan,China","School of Computing, Hubei University of Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University of Technology,School of Computing,Wuhan,China","institution_ids":["https://openalex.org/I74525822"]},{"raw_affiliation_string":"School of Computing, Hubei University of Technology, Wuhan, China","institution_ids":["https://openalex.org/I74525822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103137306","display_name":"Xin Tong","orcid":"https://orcid.org/0000-0002-0280-8391"},"institutions":[{"id":"https://openalex.org/I74525822","display_name":"Hubei University of Technology","ror":"https://ror.org/02d3fj342","country_code":"CN","type":"education","lineage":["https://openalex.org/I74525822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Tong","raw_affiliation_strings":["Hubei University of Technology,School of Computing,Wuhan,China","School of Computing, Hubei University of Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University of Technology,School of Computing,Wuhan,China","institution_ids":["https://openalex.org/I74525822"]},{"raw_affiliation_string":"School of Computing, Hubei University of Technology, Wuhan, China","institution_ids":["https://openalex.org/I74525822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101816065","display_name":"Jiahui Zhu","orcid":"https://orcid.org/0000-0002-4552-134X"},"institutions":[{"id":"https://openalex.org/I74525822","display_name":"Hubei University of Technology","ror":"https://ror.org/02d3fj342","country_code":"CN","type":"education","lineage":["https://openalex.org/I74525822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia-Hui Zhu","raw_affiliation_strings":["Hubei University of Technology,School of Computing,Wuhan,China","School of Computing, Hubei University of Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University of Technology,School of Computing,Wuhan,China","institution_ids":["https://openalex.org/I74525822"]},{"raw_affiliation_string":"School of Computing, Hubei University of Technology, Wuhan, China","institution_ids":["https://openalex.org/I74525822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052869008","display_name":"Rong Gao","orcid":"https://orcid.org/0000-0001-7935-7173"},"institutions":[{"id":"https://openalex.org/I74525822","display_name":"Hubei University of Technology","ror":"https://ror.org/02d3fj342","country_code":"CN","type":"education","lineage":["https://openalex.org/I74525822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Gao","raw_affiliation_strings":["Hubei University of Technology,School of Computing,Wuhan,China","School of Computing, Hubei University of Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University of Technology,School of Computing,Wuhan,China","institution_ids":["https://openalex.org/I74525822"]},{"raw_affiliation_string":"School of Computing, Hubei University of Technology, Wuhan, China","institution_ids":["https://openalex.org/I74525822"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74525822"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4552","last_page":"4558"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9887999892234802,"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":0.987500011920929,"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/backbone-network","display_name":"Backbone network","score":0.8084577322006226},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7406368255615234},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6890710592269897},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6501243710517883},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6137593388557434},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.5939351916313171},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5788899660110474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5452670454978943},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.510277509689331},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.48298561573028564},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4779239296913147},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4669816195964813},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4591689705848694},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4331273138523102},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4154522716999054},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39164018630981445},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3436940312385559},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2469702959060669},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12182465195655823}],"concepts":[{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.8084577322006226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7406368255615234},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6890710592269897},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6501243710517883},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6137593388557434},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.5939351916313171},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5788899660110474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5452670454978943},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.510277509689331},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.48298561573028564},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4779239296913147},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4669816195964813},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4591689705848694},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4331273138523102},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4154522716999054},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39164018630981445},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3436940312385559},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2469702959060669},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12182465195655823},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956221","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956221","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"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":10,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2963163009","https://openalex.org/W2982083293","https://openalex.org/W3010250471","https://openalex.org/W3035414587","https://openalex.org/W3084484668","https://openalex.org/W3115750425","https://openalex.org/W3126438387","https://openalex.org/W3162760148","https://openalex.org/W3170981797"],"related_works":["https://openalex.org/W3205445068","https://openalex.org/W3161591591","https://openalex.org/W3177249605","https://openalex.org/W4376620596","https://openalex.org/W2534152068","https://openalex.org/W4299545679","https://openalex.org/W1972515067","https://openalex.org/W1689909837","https://openalex.org/W4293054914","https://openalex.org/W3134004915"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,68,86,92,110,114],"solve":[3],"the":[4,38,45,50,55,62,66,75,88,111,117,121,126,133,135,140,163,167],"problems":[5],"of":[6,9,47,54,65,70,125,153],"large":[7],"number":[8,46,152],"parameters":[10,154],"and":[11,43,80,155,157],"high":[12],"computational":[13],"complexity":[14],"in":[15,49,84,120,147,166],"current":[16],"object":[17,24,143],"detection":[18,25,144],"models,":[19],"we":[20,31,73],"propose":[21],"a":[22,33],"lightweight":[23,34,142],"model":[26,67,145],"based":[27],"on":[28],"YOLOX.":[29],"First,":[30],"use":[32],"network":[35,57],"GhostNet":[36,85],"as":[37,132],"backbone":[39,56,89,127],"feature":[40,52,123],"extraction":[41],"network,":[42],"adjust":[44],"channels":[48],"output":[51,122],"layers":[53],"using":[58],"depth-separable":[59],"convolution,":[60],"reducing":[61],"parameter":[63],"quantity":[64],"one-third":[69],"YOLOX-l.":[71],"Then,":[72],"introduce":[74],"pyramid":[76],"attention":[77],"segmentation":[78],"module":[79],"FReLU":[81],"activation":[82],"function":[83],"improve":[87],"network\u2019s":[90],"ability":[91],"capture":[93],"contextual":[94],"information":[95,119],"at":[96],"different":[97],"scales;":[98],"The":[99],"Py-PAFPN":[100],"structure":[101,113],"is":[102,162],"also":[103],"proposed":[104,146],"by":[105],"adding":[106],"multi-scale":[107],"pyramidal":[108],"convolution":[109],"FPN":[112],"efficiently":[115],"fuse":[116],"image":[118],"layer":[124],"network.":[128],"Using":[129],"Pascal":[130],"VOC":[131],"dataset,":[134],"experimental":[136],"results":[137],"show":[138],"that":[139],"Ghost-YOLOX":[141],"this":[148],"paper":[149],"has":[150],"less":[151],"computation,":[156],"achieves":[158],"89.44%":[159],"accuracy,":[160],"which":[161],"best":[164],"result":[165],"comparison":[168],"experiments.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
