{"id":"https://openalex.org/W4412570281","doi":"https://doi.org/10.1002/itl2.70091","title":"Faster\u2010ActionNet: Deep Partial Convolutional Neural Networks for Volleyball Action Detection on Edge Devices","display_name":"Faster\u2010ActionNet: Deep Partial Convolutional Neural Networks for Volleyball Action Detection on Edge Devices","publication_year":2025,"publication_date":"2025-07-22","ids":{"openalex":"https://openalex.org/W4412570281","doi":"https://doi.org/10.1002/itl2.70091"},"language":"en","primary_location":{"id":"doi:10.1002/itl2.70091","is_oa":false,"landing_page_url":"https://doi.org/10.1002/itl2.70091","pdf_url":null,"source":{"id":"https://openalex.org/S4210238311","display_name":"Internet Technology Letters","issn_l":"2476-1508","issn":["2476-1508"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Internet Technology Letters","raw_type":"journal-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":null,"display_name":"Shaohua Wang","orcid":"https://orcid.org/0009-0009-0176-4663"},"institutions":[{"id":"https://openalex.org/I4210102701","display_name":"Jilin Vocational College of Industry and Technology","ror":"https://ror.org/017s6v868","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210102701"]},{"id":"https://openalex.org/I4210125838","display_name":"Jilin University of Chemical Technology","ror":"https://ror.org/03bvz5p76","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125838"]},{"id":"https://openalex.org/I4210141933","display_name":"Jilin Agricultural Science and Technology University","ror":"https://ror.org/04w5zb891","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210141933"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shaohua Wang","raw_affiliation_strings":["Jilin Technology College of Electronic Information  Jilin China","Jilin Technology College of Electronic Information, Jilin, China"],"raw_orcid":"https://orcid.org/0009-0009-0176-4663","affiliations":[{"raw_affiliation_string":"Jilin Technology College of Electronic Information  Jilin China","institution_ids":["https://openalex.org/I4210102701","https://openalex.org/I4210125838","https://openalex.org/I4210141933"]},{"raw_affiliation_string":"Jilin Technology College of Electronic Information, Jilin, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210102701","https://openalex.org/I4210125838","https://openalex.org/I4210141933"],"apc_list":{"value":2630,"currency":"USD","value_usd":2630},"apc_paid":null,"fwci":2.5399,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.90411311,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"8","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9976000189781189,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9697999954223633,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8323066234588623},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6802842020988464},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6480414271354675},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.6405574679374695},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5035375952720642},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41556739807128906},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3223009705543518},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09346833825111389}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8323066234588623},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6802842020988464},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6480414271354675},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.6405574679374695},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5035375952720642},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41556739807128906},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3223009705543518},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09346833825111389},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/itl2.70091","is_oa":false,"landing_page_url":"https://doi.org/10.1002/itl2.70091","pdf_url":null,"source":{"id":"https://openalex.org/S4210238311","display_name":"Internet Technology Letters","issn_l":"2476-1508","issn":["2476-1508"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Internet Technology Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1972696612","https://openalex.org/W2143612262","https://openalex.org/W2259801182","https://openalex.org/W2593390416","https://openalex.org/W3028117577","https://openalex.org/W4312591085","https://openalex.org/W4312919330","https://openalex.org/W4319266066","https://openalex.org/W4366085748","https://openalex.org/W4386047745","https://openalex.org/W4388110929","https://openalex.org/W4403281654","https://openalex.org/W4408378578","https://openalex.org/W4409014921"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4391621807","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391621790","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"ABSTRACT":[0],"To":[1],"address":[2],"the":[3,25,38,56,59,86,95,103,116],"challenges":[4],"of":[5,27,89,97,118],"low":[6],"accuracy":[7,122],"in":[8,17,36,129,144],"volleyball":[9,18],"individual":[10,104,130],"action":[11,105,131,154],"recognition":[12,106,155],"caused":[13],"by":[14],"complex":[15],"scenarios":[16],"sports,":[19],"Faster\u2010ActionNet":[20],"was":[21],"proposed":[22],"based":[23],"on":[24,108],"backbone":[26,39,57],"YOLOv11.":[28],"In":[29,55],"this":[30],"network,":[31,58],"partial":[32],"convolutions":[33],"are":[34],"adopted":[35],"both":[37],"and":[40,52,62,73,93,123,156],"neck":[41],"modules":[42],"to":[43,76],"amplify":[44],"critical":[45,98],"feature":[46,78,99],"representations":[47],"while":[48],"minimizing":[49],"redundant":[50],"computational":[51],"memory":[53],"overhead.":[54],"Feature":[60],"Refinement":[61],"Fusion":[63],"Network":[64],"(FRFN)":[65],"attention":[66],"mechanism":[67],"is":[68],"integrated,":[69],"which":[70],"employs":[71],"optimized":[72],"streamlined":[74],"operations":[75],"reduce":[77],"redundancy":[79],"across":[80],"channels.":[81],"This":[82,139],"enhancement":[83],"significantly":[84],"boosts":[85],"reconstruction":[87],"quality":[88],"latent":[90],"sharp":[91],"images":[92],"alleviates":[94],"risk":[96],"degradation.":[100],"Experiments":[101],"evaluating":[102],"model":[107,117,140],"volleyball\u2010specific":[109],"tasks":[110],"have":[111,135],"revealed":[112],"superior":[113],"performance,":[114],"with":[115],"mAP":[119],"attaining":[120],"88.2%":[121],"75.6":[124],"frames":[125],"per":[126],"second":[127],"(FPS)":[128],"recognition.":[132],"These":[133],"results":[134],"surpassed":[136],"state\u2010of\u2010the\u2010art":[137],"benchmarks.":[138],"demonstrates":[141],"outstanding":[142],"performance":[143],"real\u2010world":[145],"applications,":[146],"providing":[147],"valuable":[148],"technical":[149],"insights":[150],"for":[151],"improving":[152],"sports":[153],"advancing":[157],"computer":[158],"vision":[159],"technologies.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}