{"id":"https://openalex.org/W2790167166","doi":"https://doi.org/10.1145/3174243.3174266","title":"A Lightweight YOLOv2","display_name":"A Lightweight YOLOv2","publication_year":2018,"publication_date":"2018-02-15","ids":{"openalex":"https://openalex.org/W2790167166","doi":"https://doi.org/10.1145/3174243.3174266","mag":"2790167166"},"language":"en","primary_location":{"id":"doi:10.1145/3174243.3174266","doi":"https://doi.org/10.1145/3174243.3174266","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3174243.3174266","pdf_url":null,"source":{"id":"https://openalex.org/S7407087960","display_name":"ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5070734898","display_name":"Hiroki Nakahara","orcid":"https://orcid.org/0000-0002-5701-7466","observed_orcids":["https://orcid.org/0000-0002-5701-7466"]},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698","https://openalex.org/I4400009020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Nakahara","raw_affiliation_strings":["Tokyo Institute of Technology, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008074659","display_name":"Haruyoshi Yonekawa","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698","https://openalex.org/I4400009020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Haruyoshi Yonekawa","raw_affiliation_strings":["Tokyo Institute of Technology, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108645900","display_name":"Tomoya Fujii","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698","https://openalex.org/I4400009020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoya Fujii","raw_affiliation_strings":["Tokyo Institute of Technology, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101424734","display_name":"Shimpei Sato","orcid":"https://orcid.org/0000-0003-0292-1391","observed_orcids":["https://orcid.org/0000-0003-0292-1391"]},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698","https://openalex.org/I4400009020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shimpei Sato","raw_affiliation_strings":["Tokyo Institute of Technology, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":null,"apc_paid":null,"fwci":5.8596,"has_fulltext":false,"cited_by_count":141,"citation_normalized_percentile":{"value":0.96963629,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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"}},{"id":"https://openalex.org/T10904","display_name":"Embedded Systems Design Techniques","score":0.005100000184029341,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T13292","display_name":"Embedded Systems and FPGA Applications","score":0.0027000000700354576,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/yolov2","display_name":"YOLOv2","score":0.9800000190734863},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.7409999966621399},{"id":"https://openalex.org/keywords/fpga-implementation","display_name":"FPGA implementation","score":0.9409999847412109},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":0.8600000143051147},{"id":"https://openalex.org/keywords/lightweight-neural-networks","display_name":"lightweight neural networks","score":0.6299999952316284},{"id":"https://openalex.org/keywords/embedded-vision","display_name":"embedded vision","score":0.7839999794960022},{"id":"https://openalex.org/keywords/performance-per-watt","display_name":"performance per watt","score":0.6349999904632568},{"id":"https://openalex.org/keywords/bounding-box-regression","display_name":"bounding box regression","score":0.7739999890327454},{"id":"https://openalex.org/keywords/real-time-inference","display_name":"real-time inference","score":0.5479999780654907}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7852751016616821},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7724614143371582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7228345274925232},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7125377655029297},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.7055193781852722},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.693283200263977},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6367738246917725},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.6123977303504944},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5170479416847229},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5156911611557007},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47505852580070496},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4558209180831909},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4541771113872528},{"id":"https://openalex.org/C2777187653","wikidata":"https://www.wikidata.org/wiki/Q975106","display_name":"MPSoC","level":3,"score":0.433494508266449},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4308801293373108},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4274987280368805},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42280298471450806},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.410093754529953},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.212469220161438},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1621837317943573},{"id":"https://openalex.org/C118021083","wikidata":"https://www.wikidata.org/wiki/Q610398","display_name":"System on a chip","level":2,"score":0.13751697540283203},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3174243.3174266","doi":"https://doi.org/10.1145/3174243.3174266","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3174243.3174266","pdf_url":null,"source":{"id":"https://openalex.org/S7407087960","display_name":"ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1525954826","https://openalex.org/W1536680647","https://openalex.org/W1580215688","https://openalex.org/W1594098193","https://openalex.org/W1665214252","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W2068730032","https://openalex.org/W2102605133","https://openalex.org/W2135252045","https://openalex.org/W2137226992","https://openalex.org/W2156135142","https://openalex.org/W2168356304","https://openalex.org/W2179352600","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2276486856","https://openalex.org/W2300242332","https://openalex.org/W2319920447","https://openalex.org/W2469490737","https://openalex.org/W2565125333","https://openalex.org/W2570343428","https://openalex.org/W2585560244","https://openalex.org/W2591922920","https://openalex.org/W2761085955","https://openalex.org/W2761262947","https://openalex.org/W2762597430","https://openalex.org/W2949117887","https://openalex.org/W3106250896","https://openalex.org/W4239510810","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2905251143","https://openalex.org/W2787550496","https://openalex.org/W4205661600","https://openalex.org/W2755268668","https://openalex.org/W2982003654","https://openalex.org/W7155468713","https://openalex.org/W6940227633","https://openalex.org/W4285065868","https://openalex.org/W4313496670","https://openalex.org/W3138495922"],"abstract_inverted_index":{"A":[0],"frame":[1,30,278],"object":[2,66,103,180,208,279],"detection":[3],"problem":[4,13],"consists":[5,109,142],"of":[6,25,50,110,143],"two":[7],"problems:":[8],"one":[9],"is":[10,21,33,72,80,91,169,274],"a":[11,76,92,111,117,138,148,160,163,184],"regression":[12,156],"to":[14,128],"spatially":[15],"separated":[16],"bounding":[17,112],"boxes,":[18],"the":[19,22,26,37,60,83,96,99,102,107,122,134,144,152,170,189,193,200,218,245,277],"second":[20,214],"associated":[23],"classification":[24,161],"objects":[27],"within":[28],"realtime":[29],"rate.":[31],"It":[32,79],"widely":[34],"used":[35,178],"in":[36,130,179],"embedded":[38,248,283],"systems,":[39],"such":[40],"as":[41],"robotics,":[42],"autonomous":[43],"driving,":[44],"security,":[45],"and":[46,54,74,89,98,116,151,162,232,261],"drones":[47],"-":[48],"all":[49,124],"which":[51,71,141,198],"require":[52],"high-performance":[53],"low-power":[55],"consumption.":[56],"This":[57],"paper":[58],"implements":[59],"YOLO":[61],"(You":[62],"only":[63],"look":[64],"once)":[65],"detector":[67,104,209,280],"on":[68,82,106,192],"an":[69,282],"FPGA,":[70],"faster":[73],"has":[75,199],"higher":[77],"accuracy.":[78],"based":[81,105,186],"convolutional":[84],"deep":[85],"neural":[86],"network":[87],"(CNN),":[88],"it":[90,221,226,250,255],"dominant":[93],"part":[94],"both":[95,159],"performance":[97,234,263],"area.":[100],"However,":[101],"CNN":[108,126,146],"box":[113],"prediction":[114],"(regression)":[115],"class":[118],"estimation":[119],"(classification).":[120],"Thus,":[121,271],"conventional":[123],"binarized":[125,145,173],"fails":[127],"recognize":[129],"most":[131],"cases.":[132],"In":[133],"paper,":[135],"we":[136],"propose":[137],"lightweight":[139,190],"YOLOv2,":[140],"for":[147,158,188,276,281],"feature":[149],"extraction":[150],"parallel":[153],"support":[154],"vector":[155],"(SVR)":[157],"localization.":[164],"To":[165],"our":[166,272],"knowledge,":[167],"this":[168],"first":[171],"time":[172],"CNN\u00bbs":[174],"have":[175],"been":[176],"successfully":[177],"detection.":[181],"We":[182],"implement":[183],"pipelined":[185],"architecture":[187],"YOLOv2":[191],"Xilinx":[194,201],"Inc.":[195,202],"zcu102":[196],"board,":[197],"Zynq":[203],"Ultrascale+":[204],"MPSoC.":[205],"The":[206],"implemented":[207],"archived":[210],"40.81":[211],"frames":[212],"per":[213,235,264],"(FPS).":[215],"Compared":[216],"with":[217,244],"ARM":[219],"Cortex-A57,":[220],"was":[222,238,251,267],"177.4":[223],"times":[224,229,240,253,258,269],"faster,":[225,254],"dissipated":[227,256],"1.1":[228],"more":[230],"power,":[231,260],"its":[233,262],"power":[236,265],"efficiency":[237,266],"158.9":[239],"better.":[241,270],"Also,":[242],"compared":[243],"nVidia":[246],"Pascall":[247],"GPU,":[249],"27.5":[252],"1.5":[257],"lower":[259],"42.9":[268],"method":[273],"suitable":[275],"vision":[284],"system.":[285]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":32},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":11}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2018-03-29T00:00:00"}