{"id":"https://openalex.org/W4200071014","doi":"https://doi.org/10.1145/3487075.3487088","title":"Improved Pedestrian Detection Algorithm of Yolov4 Network Structure","display_name":"Improved Pedestrian Detection Algorithm of Yolov4 Network Structure","publication_year":2021,"publication_date":"2021-10-19","ids":{"openalex":"https://openalex.org/W4200071014","doi":"https://doi.org/10.1145/3487075.3487088"},"language":"en","primary_location":{"id":"doi:10.1145/3487075.3487088","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3487075.3487088","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","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/A5061978729","display_name":"Xiujun Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiujun Zhu","raw_affiliation_strings":["School of Information Science &amp; Engineering, Yunnan University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science &amp; Engineering, Yunnan University, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101959750","display_name":"Yujie Bai","orcid":"https://orcid.org/0009-0005-3273-4580"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujie Bai","raw_affiliation_strings":["School of Information Science &amp; Engineering, Yunnan University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science &amp; Engineering, Yunnan University, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010722514","display_name":"Yijian Pei","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yijian Pei","raw_affiliation_strings":["School of Information Science &amp; Engineering, Yunnan University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science &amp; Engineering, Yunnan University, China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061978729"],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16351307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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":0.998199999332428,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.7647178173065186},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.584088146686554},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5389465093612671},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5349370837211609},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49300381541252136},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.48815709352493286},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4785485863685608},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.46303021907806396},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45895418524742126},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4371379613876343},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42448610067367554},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38396787643432617},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35136672854423523},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.10534688830375671}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7647178173065186},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.584088146686554},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5389465093612671},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5349370837211609},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49300381541252136},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.48815709352493286},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4785485863685608},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.46303021907806396},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45895418524742126},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4371379613876343},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42448610067367554},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38396787643432617},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35136672854423523},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.10534688830375671},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3487075.3487088","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3487075.3487088","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1483870316","https://openalex.org/W1866173756","https://openalex.org/W2097324787","https://openalex.org/W2102605133","https://openalex.org/W2161969291","https://openalex.org/W2923837612","https://openalex.org/W2997592810","https://openalex.org/W3009938199","https://openalex.org/W3010993759","https://openalex.org/W3019909811","https://openalex.org/W3026714776","https://openalex.org/W3091802947","https://openalex.org/W3095453497","https://openalex.org/W6631782140","https://openalex.org/W6777046832","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581"],"abstract_inverted_index":{"When":[0],"the":[1,7,16,29,37,42,54,61,83,98,110,118,121,132,141,145,173,183],"YOLOV4":[2,30,62],"network":[3,31,43,64,100,174],"detects":[4],"pedestrians":[5,10],"alone,":[6],"small":[8,45],"target":[9],"will":[11],"be":[12],"missed,":[13],"resulting":[14],"in":[15],"reduction":[17],"of":[18,41,60,97,117],"P":[19,115,142],"(Precision)":[20],"and":[21,93,131,140,182,185],"AP":[22,133,162],"(Average":[23],"Precision)":[24],"values.":[25],"This":[26],"paper":[27],"improves":[28],"structure.":[32],"In":[33],"order":[34],"to":[35,53,65,81,108,129,138,160,167],"improve":[36],"feature":[38,49,58],"extraction":[39],"capability":[40],"for":[44],"targets,":[46],"a":[47,176],"shallower":[48],"layer":[50],"is":[51,91,106],"added":[52,80,92],"original":[55,99],"three":[56],"output":[57],"layers":[59,96],"backbone":[63],"build":[66],"PANet":[67],"(Path":[68],"Aggregation":[69],"Network)":[70],"together.":[71],"And":[72],"two":[73],"SPP":[74],"(Spatial":[75],"Pyramid":[76],"Pooling)":[77],"structures":[78],"are":[79,101,188],"expand":[82],"receptive":[84],"field.":[85],"The":[86,114],"channel":[87],"attention":[88],"mechanism":[89],"module":[90],"some":[94],"convolutional":[95],"deleted.":[102],"Finally,":[103],"transfer":[104],"learning":[105],"used":[107,147],"make":[109],"detection":[111,149],"effect":[112,178],"better.":[113],"value":[116,134,143,163],"pedestrian":[119,148,180],"on":[120,144,179],"PASCAL":[122],"VOC":[123],"data":[124,150],"set":[125,151],"increased":[126,135,156,159,164],"from":[127,136,157,165],"84.43%":[128],"91.37%,":[130],"74.78%":[137],"87.39%,":[139],"commonly":[146],"INRIA":[152],"(INRIA":[153],"Person":[154],"Dataset)":[155],"93.20%":[158],"98.02%,":[161],"91.08%":[166],"94.02%.":[168],"Experimental":[169],"results":[170],"show":[171],"that":[172],"has":[175],"better":[177],"detection,":[181],"accuracy":[184],"average":[186],"precision":[187],"improved.":[189]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
