{"id":"https://openalex.org/W4400811111","doi":"https://doi.org/10.1109/aicas59952.2024.10595873","title":"An Efficient and Fast Filter Pruning Method for Object Detection in Embedded Systems","display_name":"An Efficient and Fast Filter Pruning Method for Object Detection in Embedded Systems","publication_year":2024,"publication_date":"2024-04-22","ids":{"openalex":"https://openalex.org/W4400811111","doi":"https://doi.org/10.1109/aicas59952.2024.10595873"},"language":"en","primary_location":{"id":"doi:10.1109/aicas59952.2024.10595873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aicas59952.2024.10595873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS)","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/A5113296026","display_name":"Hyunjun Ko","orcid":null},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunjun Ko","raw_affiliation_strings":["Inha University,Department of Electrical and Computer Engineering,Incheon,Republic of Korea,22212"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inha University,Department of Electrical and Computer Engineering,Incheon,Republic of Korea,22212","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035110848","display_name":"Jin-Ku Kang","orcid":"https://orcid.org/0000-0002-3752-3740"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jin-Ku Kang","raw_affiliation_strings":["Inha University,Department of Electrical and Computer Engineering,Incheon,Republic of Korea,22212"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inha University,Department of Electrical and Computer Engineering,Incheon,Republic of Korea,22212","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019873685","display_name":"Yongwoo Kim","orcid":"https://orcid.org/0000-0002-1011-2319"},"institutions":[{"id":"https://openalex.org/I141074810","display_name":"Korea National University of Education","ror":"https://ror.org/03c9fpk55","country_code":"KR","type":"education","lineage":["https://openalex.org/I141074810"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yongwoo Kim","raw_affiliation_strings":["Korea National University of Education,Department of Technology Education,Cheongju,Republic of Korea,28173"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea National University of Education,Department of Technology Education,Cheongju,Republic of Korea,28173","institution_ids":["https://openalex.org/I141074810"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1856,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48156137,"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":"204","last_page":"207"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12222","display_name":"IoT-based Smart Home Systems","score":0.6988000273704529,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12222","display_name":"IoT-based Smart Home Systems","score":0.6988000273704529,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.6653000116348267,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13717","display_name":"Advanced Algorithms and Applications","score":0.6528000235557556,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7512019276618958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7031911015510559},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5740641355514526},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5574308037757874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5317080616950989},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5184028148651123},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4911200702190399},{"id":"https://openalex.org/keywords/filtering-theory","display_name":"Filtering theory","score":0.4427884817123413},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2938835620880127}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7512019276618958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7031911015510559},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5740641355514526},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5574308037757874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5317080616950989},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5184028148651123},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4911200702190399},{"id":"https://openalex.org/C2988922011","wikidata":"https://www.wikidata.org/wiki/Q5449244","display_name":"Filtering theory","level":2,"score":0.4427884817123413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2938835620880127},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aicas59952.2024.10595873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aicas59952.2024.10595873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2037227137","https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2896409484","https://openalex.org/W3034513523","https://openalex.org/W3106250896","https://openalex.org/W3109212549","https://openalex.org/W3177656211","https://openalex.org/W4285140289","https://openalex.org/W4312796067","https://openalex.org/W4319873770","https://openalex.org/W4386083031","https://openalex.org/W6637373629","https://openalex.org/W6755843862","https://openalex.org/W6797972850"],"related_works":["https://openalex.org/W2373300491","https://openalex.org/W2378744544","https://openalex.org/W2594301978","https://openalex.org/W2379704676","https://openalex.org/W1998810860","https://openalex.org/W4292830139","https://openalex.org/W4319309705","https://openalex.org/W1500438404","https://openalex.org/W2112857436","https://openalex.org/W2166470059"],"abstract_inverted_index":{"Recently,":[0],"CNN-based":[1],"networks":[2,17],"have":[3],"exhibited":[4],"high":[5],"performance":[6],"in":[7,29],"computer":[8],"vision.":[9],"On":[10],"the":[11,16,27,35,87,112,123,127,136,160,187,200],"other":[12,104,196,231],"hand,":[13],"due":[14],"to":[15,25,119,121,145,163,168,174,230],"becoming":[18],"deeper":[19],"and":[20,63,78,91,133,189,193,220],"wider,":[21],"it":[22,58],"is":[23,143,207],"hard":[24],"implement":[26],"model":[28,150],"real-time":[30],"embedded":[31],"environments.":[32],"To":[33],"overcome":[34],"drawback,":[36],"filter":[37,80],"pruning":[38,48,81,105,113,152,171,197,232],"has":[39],"been":[40],"widely":[41],"studied":[42],"for":[43],"neural":[44],"network":[45],"compression.":[46],"Filter":[47],"does":[49],"not":[50],"need":[51],"any":[52,67],"special":[53,68],"hardware":[54],"or":[55,70],"software":[56,69],"because":[57],"removes":[59],"filters":[60,118,154,167,173],"of":[61,138,226],"CNN":[62],"accelerates":[64],"inference":[65,205,221],"without":[66],"hardware.":[71],"In":[72,111],"this":[73],"paper,":[74],"we":[75,115,158,217],"proposed":[76],"efficient":[77],"fast":[79],"(EFFP),":[82],"which":[83],"focuses":[84],"on":[85,199,209],"reducing":[86],"training":[88,109],"computation":[89],"resources":[90],"searching":[92],"optimal":[93,148,177],"pruned":[94,149],"networks.":[95],"The":[96,204],"success":[97],"stems":[98],"from":[99],"two":[100],"significant":[101],"improvements":[102],"upon":[103],"methods.":[106,233],"(1)":[107],"Short":[108],"time:":[110],"stage,":[114],"make":[116,122],"redundant":[117,153],"zero":[120],"output":[124],"feature":[125],"map":[126],"same":[128],"as":[129],"a":[130,215,224],"lightweight":[131,178],"model,":[132],"(2)":[134],"adjust":[135],"change":[137],"redundancy":[139],"using":[140],"regrowing:":[141],"It":[142],"difficult":[144],"get":[146,175],"an":[147,176],"by":[151,223],"at":[155],"once.":[156],"Therefore,":[157],"use":[159],"pruning/regrowing":[161],"method":[162],"gradually":[164],"remove":[165],"unimportant":[166],"avoid":[169],"permanently":[170],"important":[172],"model.":[179,203],"Experimental":[180],"results":[181],"indicate":[182],"that":[183],"EFFP":[184],"can":[185],"reduce":[186],"FLOPs":[188],"parameters":[190],"more":[191],"efficiently":[192],"faster":[194],"than":[195],"methods":[198],"object":[201],"detection":[202],"time":[206,222],"measured":[208],"NVIDIA":[210],"Jetson":[211],"Xavier":[212],"NX.":[213],"As":[214],"result,":[216],"improve":[218],"mAP":[219],"maximum":[225],"45":[227],"%":[228],"compared":[229]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}