{"id":"https://openalex.org/W4385881961","doi":"https://doi.org/10.1109/icis57766.2023.10210237","title":"Real-time Lightweight Hand Detection Model Combined with Network Pruning","display_name":"Real-time Lightweight Hand Detection Model Combined with Network Pruning","publication_year":2023,"publication_date":"2023-06-23","ids":{"openalex":"https://openalex.org/W4385881961","doi":"https://doi.org/10.1109/icis57766.2023.10210237"},"language":"en","primary_location":{"id":"doi:10.1109/icis57766.2023.10210237","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icis57766.2023.10210237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACIS 23rd International Conference on Computer and Information Science (ICIS)","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/A5021346645","display_name":"Xiangxian Zhu","orcid":"https://orcid.org/0000-0001-8534-1557"},"institutions":[{"id":"https://openalex.org/I4210154884","display_name":"Joyson Electronics (China)","ror":"https://ror.org/0505b0847","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210154884"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangxian Zhu","raw_affiliation_strings":["Ningbo Preh Joyson Automotive Electronics Co., Ltd,Zhejiang Key Laboratory of Automotive Electronics Intelligence,Ningbo,China","Zhejiang Key Laboratory of Automotive Electronics Intelligence, Ningbo Preh Joyson Automotive Electronics Co., Ltd, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Ningbo Preh Joyson Automotive Electronics Co., Ltd,Zhejiang Key Laboratory of Automotive Electronics Intelligence,Ningbo,China","institution_ids":["https://openalex.org/I4210154884"]},{"raw_affiliation_string":"Zhejiang Key Laboratory of Automotive Electronics Intelligence, Ningbo Preh Joyson Automotive Electronics Co., Ltd, Ningbo, China","institution_ids":["https://openalex.org/I4210154884"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089171218","display_name":"Zhao Jiang","orcid":"https://orcid.org/0000-0002-6617-8165"},"institutions":[{"id":"https://openalex.org/I4210154884","display_name":"Joyson Electronics (China)","ror":"https://ror.org/0505b0847","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210154884"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Jiang","raw_affiliation_strings":["Ningbo Preh Joyson Automotive Electronics Co., Ltd,Zhejiang Key Laboratory of Automotive Electronics Intelligence,Ningbo,China","Zhejiang Key Laboratory of Automotive Electronics Intelligence, Ningbo Preh Joyson Automotive Electronics Co., Ltd, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Ningbo Preh Joyson Automotive Electronics Co., Ltd,Zhejiang Key Laboratory of Automotive Electronics Intelligence,Ningbo,China","institution_ids":["https://openalex.org/I4210154884"]},{"raw_affiliation_string":"Zhejiang Key Laboratory of Automotive Electronics Intelligence, Ningbo Preh Joyson Automotive Electronics Co., Ltd, Ningbo, China","institution_ids":["https://openalex.org/I4210154884"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060727408","display_name":"Yilun Lou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210154884","display_name":"Joyson Electronics (China)","ror":"https://ror.org/0505b0847","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210154884"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yilun Lou","raw_affiliation_strings":["Ningbo Preh Joyson Automotive Electronics Co., Ltd,Zhejiang Key Laboratory of Automotive Electronics Intelligence,Ningbo,China","Zhejiang Key Laboratory of Automotive Electronics Intelligence, Ningbo Preh Joyson Automotive Electronics Co., Ltd, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Ningbo Preh Joyson Automotive Electronics Co., Ltd,Zhejiang Key Laboratory of Automotive Electronics Intelligence,Ningbo,China","institution_ids":["https://openalex.org/I4210154884"]},{"raw_affiliation_string":"Zhejiang Key Laboratory of Automotive Electronics Intelligence, Ningbo Preh Joyson Automotive Electronics Co., Ltd, Ningbo, China","institution_ids":["https://openalex.org/I4210154884"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021346645"],"corresponding_institution_ids":["https://openalex.org/I4210154884"],"apc_list":null,"apc_paid":null,"fwci":0.1725,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47673504,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11398","display_name":"Hand Gesture Recognition Systems","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9983000159263611,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9797999858856201,"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":0.8210411667823792},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5999487042427063},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5971174836158752},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5747418403625488},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.48796528577804565},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.45762762427330017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45503565669059753},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4515402317047119},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4246410131454468},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3316439986228943},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13783106207847595},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1087777316570282}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8210411667823792},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5999487042427063},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5971174836158752},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5747418403625488},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.48796528577804565},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.45762762427330017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45503565669059753},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4515402317047119},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4246410131454468},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3316439986228943},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13783106207847595},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1087777316570282},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icis57766.2023.10210237","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icis57766.2023.10210237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACIS 23rd International Conference on Computer and Information Science (ICIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1977995219","https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2204609240","https://openalex.org/W2486323017","https://openalex.org/W2570343428","https://openalex.org/W2804032941","https://openalex.org/W2962134292","https://openalex.org/W2963037989","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963488642","https://openalex.org/W2963787550","https://openalex.org/W2964233199","https://openalex.org/W2966256598","https://openalex.org/W2982083293","https://openalex.org/W3034891989","https://openalex.org/W3035396860","https://openalex.org/W3036954260","https://openalex.org/W3043944662","https://openalex.org/W3106250896","https://openalex.org/W3172087149","https://openalex.org/W3177656211","https://openalex.org/W4293584584","https://openalex.org/W4297775537","https://openalex.org/W6751349269","https://openalex.org/W6797972850"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W4287880334","https://openalex.org/W4366700029","https://openalex.org/W2949601986","https://openalex.org/W3175363083","https://openalex.org/W4285230481","https://openalex.org/W2788972299","https://openalex.org/W2373300491","https://openalex.org/W4385769873","https://openalex.org/W4313645560"],"abstract_inverted_index":{"Hand":[0],"detection,":[1],"often":[2],"as":[3,93],"a":[4,15,36,58,149,205],"pre-processing":[5],"step":[6],"for":[7,31,152],"hand":[8,12,23,40,45,61],"keypoint":[9],"localization":[10],"and":[11,76,89,107,144,183],"segmentation,":[13],"is":[14,25,47,179],"critical":[16],"task":[17],"in":[18,39],"gesture":[19,41],"interaction.":[20,42],"A":[21,80],"fast":[22,44],"detector":[24],"demanding":[26],"because":[27],"it":[28],"saves":[29],"time":[30],"downstream":[32],"tasks,":[33],"leading":[34],"to":[35,132,136],"real-time":[37,60],"performance":[38],"Achieving":[43],"detection":[46,62],"challenging,":[48],"especially":[49],"on":[50,212],"resource-limited":[51],"devices.":[52,214],"In":[53],"this":[54],"paper,":[55],"we":[56,147],"propose":[57],"lightweight":[59],"model":[63,99,113,174,178,182,191],"called":[64],"BlazeYOLO.":[65],"We":[66],"reduce":[67],"the":[68,94,98,109,122,125,137,153,157,160,173,180],"model's":[69],"computational":[70,202],"cost":[71],"by":[72,141],"utilizing":[73],"light":[74],"modules":[75],"pruning":[77,150,158],"redundancy":[78],"channels.":[79],"BlazeBlock,":[81],"which":[82],"consists":[83],"of":[84,97,201,209],"depthwise":[85,142],"convolution,":[86,88],"pointwise":[87],"residual":[90,145],"structure,":[91,146],"serves":[92],"building":[95],"block":[96],"backbone.":[100],"The":[101,112,176],"Only":[102],"Train":[103],"Once":[104],"method":[105],"trains":[106],"prunes":[108],"entire":[110],"model.":[111,155],"parameters":[114,123,163],"are":[115,128,164],"grouped":[116],"into":[117],"zero-invariant":[118],"groups.":[119],"During":[120],"training,":[121],"within":[124],"same":[126],"group":[127],"updated":[129],"or":[130],"set":[131],"zero":[133,166],"simultaneously.":[134],"Due":[135],"channel":[138],"dependency":[139],"induced":[140],"convolution":[143],"design":[148],"scheme":[151],"BlazeYOLO":[154],"Following":[156],"scheme,":[159],"groups":[161],"whose":[162],"all":[165],"can":[167],"be":[168],"directly":[169],"pruned":[170,177],"without":[171],"affecting":[172],"output.":[175],"final":[181],"does":[184],"not":[185],"require":[186],"additional":[187],"fine-tuning.":[188],"Our":[189],"proposed":[190],"achieves":[192],"95.5%":[193],"average":[194],"precision":[195],"(AP)":[196],"with":[197],"only":[198],"0.11":[199],"GFLOPS":[200],"cost,":[203],"reaching":[204],"high":[206],"inference":[207],"speed":[208],"78.1":[210],"FPS":[211],"mobile":[213]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}
