{"id":"https://openalex.org/W4391550297","doi":"https://doi.org/10.1145/3635118.3635124","title":"Efficient Real-Time Dense Pedestrian Detector Based on Improved YOLOv7-tiny","display_name":"Efficient Real-Time Dense Pedestrian Detector Based on Improved YOLOv7-tiny","publication_year":2023,"publication_date":"2023-11-17","ids":{"openalex":"https://openalex.org/W4391550297","doi":"https://doi.org/10.1145/3635118.3635124"},"language":"en","primary_location":{"id":"doi:10.1145/3635118.3635124","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3635118.3635124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Advances in Image Processing","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/A5075654029","display_name":"Xiner Li","orcid":"https://orcid.org/0009-0009-0908-7978"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuezhi Li","raw_affiliation_strings":["Shanghai University Of Engineering Science, China"],"raw_orcid":"https://orcid.org/0009-0009-0908-7978","affiliations":[{"raw_affiliation_string":"Shanghai University Of Engineering Science, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037265525","display_name":"Yiping Shi","orcid":"https://orcid.org/0009-0009-6966-8993"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiping Shi","raw_affiliation_strings":["Shanghai University Of Engineering Science, China"],"raw_orcid":"https://orcid.org/0009-0009-6966-8993","affiliations":[{"raw_affiliation_string":"Shanghai University Of Engineering Science, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021942997","display_name":"Ziyun Zhao","orcid":"https://orcid.org/0009-0003-8721-8626"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyun Zhao","raw_affiliation_strings":["Shanghai University Of Engineering Science, China"],"raw_orcid":"https://orcid.org/0009-0003-8721-8626","affiliations":[{"raw_affiliation_string":"Shanghai University Of Engineering Science, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027743711","display_name":"Shaojie Shi","orcid":"https://orcid.org/0009-0004-5781-0941"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaojie Shi","raw_affiliation_strings":["Shanghai University Of Engineering Science, China"],"raw_orcid":"https://orcid.org/0009-0004-5781-0941","affiliations":[{"raw_affiliation_string":"Shanghai University Of Engineering Science, China","institution_ids":["https://openalex.org/I141962983"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1123,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45147168,"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":"42","last_page":"48"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9961000084877014,"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":0.9961000084877014,"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":0.9947999715805054,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9891999959945679,"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/pedestrian","display_name":"Pedestrian","score":0.6912763714790344},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6720049381256104},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6460245847702026},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6380784511566162},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3716908097267151},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3309163749217987},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1375793218612671},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13234323263168335},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.0958317220211029}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6912763714790344},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6720049381256104},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6460245847702026},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6380784511566162},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3716908097267151},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3309163749217987},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1375793218612671},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13234323263168335},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0958317220211029}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3635118.3635124","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3635118.3635124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Advances in Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5099999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W2883780447","https://openalex.org/W2910345484","https://openalex.org/W2910522659","https://openalex.org/W2962766617","https://openalex.org/W2963857746","https://openalex.org/W2997747012","https://openalex.org/W3020130933","https://openalex.org/W3022645783","https://openalex.org/W3034552520","https://openalex.org/W3034971973","https://openalex.org/W3080079484","https://openalex.org/W3156866268","https://openalex.org/W3190695173","https://openalex.org/W3199736468","https://openalex.org/W3210586215","https://openalex.org/W3215216361","https://openalex.org/W4205275628","https://openalex.org/W4206709332","https://openalex.org/W4256080149","https://openalex.org/W4284968638","https://openalex.org/W4285178232","https://openalex.org/W4289535494","https://openalex.org/W4379279809","https://openalex.org/W4386076325","https://openalex.org/W6600020652","https://openalex.org/W6600662924","https://openalex.org/W6603884005","https://openalex.org/W6604103938","https://openalex.org/W6604344240","https://openalex.org/W6604381581","https://openalex.org/W6696711281"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W1976827262","https://openalex.org/W49697837","https://openalex.org/W3122828758","https://openalex.org/W2170799233","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"The":[0],"state-of-the-art":[1],"YOLO":[2,92],"framework":[3],"strikes":[4],"an":[5],"excellent":[6],"balance":[7],"between":[8],"speed":[9,218],"and":[10,12,38,82,119,157,214],"accuracy":[11,184],"has":[13],"become":[14],"one":[15],"of":[16,122,148,191,199,207],"the":[17,30,67,72,79,84,91,98,138,146,149,153,158,167,172,177,183,189,197,205,208,216],"most":[18],"effective":[19],"object":[20],"detection":[21,28,59,217],"algorithms.":[22],"However,":[23],"when":[24],"performing":[25],"dense":[26],"pedestrian":[27],"tasks,":[29],"network":[31,93,99,107,168,192,209],"is":[32,36,40],"computationally":[33],"intensive,":[34],"real-time":[35],"insufficient,":[37],"it":[39],"difficult":[41],"to":[42,45,77,87,96,109,136,141,176,221],"be":[43,88],"applied":[44],"edge":[46],"devices":[47],"with":[48,90,113],"weak":[49],"computational":[50,100],"power":[51],"in":[52,94],"practical":[53],"use.":[54],"Therefore,":[55],"we":[56,70],"propose":[57,83,103],"a":[58,104,114,132],"algorithm":[60],"based":[61],"on":[62,66,152],"YOLOv7-tiny":[63,178],"improvement.":[64],"Based":[65],"GhostNetv2":[68],"network,":[69],"use":[71,129],"parameter-free":[73],"attention":[74,140],"mechanism":[75],"SimAM":[76],"construct":[78],"GhostNet-tiny":[80],"structure":[81,108,118],"C3Ghost-tiny":[85],"module":[86],"combined":[89],"order":[95],"reduce":[97,166],"parameters.":[101],"We":[102,127,144],"new":[105],"neck":[106],"extract":[110],"richer":[111],"information":[112],"bi-directional":[115],"weighted":[116,120],"pyramid":[117],"connectivity":[121],"features":[123],"at":[124],"different":[125],"scales.":[126],"also":[128],"WIoU":[130],"as":[131],"coordinate":[133],"loss":[134],"function":[135],"improve":[137],"network's":[139],"small":[142],"targets.":[143],"tested":[145],"effectiveness":[147],"proposed":[150],"method":[151,163],"public":[154],"dataset":[155],"CrowdHuman,":[156],"results":[159],"show":[160],"that":[161],"our":[162,180],"can":[164],"effectively":[165],"parameters":[169,193],"while":[170],"improving":[171],"accuracy.":[173],"Notably,":[174],"compared":[175],"model,":[179],"model":[181,210],"improves":[182,215],"value":[185],"by":[186,194,202,212,219],"2.13%,":[187],"reduces":[188,196,204],"amount":[190,198],"5.9%,":[195],"floating-point":[200],"calculations":[201],"60.3%,":[203],"size":[206],"file":[211],"27.7%,":[213],"up":[220],"46.64%.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}