{"id":"https://openalex.org/W4382450226","doi":"https://doi.org/10.3390/s23135912","title":"An Improved YOLOv7 Lightweight Detection Algorithm for Obscured Pedestrians","display_name":"An Improved YOLOv7 Lightweight Detection Algorithm for Obscured Pedestrians","publication_year":2023,"publication_date":"2023-06-26","ids":{"openalex":"https://openalex.org/W4382450226","doi":"https://doi.org/10.3390/s23135912","pmid":"https://pubmed.ncbi.nlm.nih.gov/37447762"},"language":"en","primary_location":{"id":"doi:10.3390/s23135912","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23135912","pdf_url":"https://www.mdpi.com/1424-8220/23/13/5912/pdf?version=1687768398","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/23/13/5912/pdf?version=1687768398","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100429517","display_name":"Chang Li","orcid":"https://orcid.org/0000-0002-1705-0149"},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Li","raw_affiliation_strings":["College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China","institution_ids":["https://openalex.org/I1456306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100607418","display_name":"Yiding Wang","orcid":"https://orcid.org/0000-0002-6596-2337"},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiding Wang","raw_affiliation_strings":["College of Information Science and Technology, North China University of Technology, Beijing 100144, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, North China University of Technology, Beijing 100144, China","institution_ids":["https://openalex.org/I1456306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100409027","display_name":"Xiaoming Liu","orcid":"https://orcid.org/0000-0002-0424-629X"},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Liu","raw_affiliation_strings":["College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China","institution_ids":["https://openalex.org/I1456306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100607418"],"corresponding_institution_ids":["https://openalex.org/I1456306"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":3.8703,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.95023267,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"23","issue":"13","first_page":"5912","last_page":"5912"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9984999895095825,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9853000044822693,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7963947057723999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7245047688484192},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.6862133741378784},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6378978490829468},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6238482594490051},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5425444841384888},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5398008823394775},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5315373539924622},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5148626565933228},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47391045093536377},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.4239223599433899},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.41601842641830444},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3397129774093628},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1305469274520874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11196273565292358}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7963947057723999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245047688484192},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.6862133741378784},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6378978490829468},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6238482594490051},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5425444841384888},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5398008823394775},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5315373539924622},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5148626565933228},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47391045093536377},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.4239223599433899},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.41601842641830444},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3397129774093628},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1305469274520874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11196273565292358},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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":[{"descriptor_ui":"D000069636","descriptor_name":"Pedestrians","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.3390/s23135912","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23135912","pdf_url":"https://www.mdpi.com/1424-8220/23/13/5912/pdf?version=1687768398","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:37447762","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37447762","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10347272","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10347272","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10347272/pdf/sensors-23-05912.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:11bb638f66494180a04aa4ba5bd041a9","is_oa":true,"landing_page_url":"https://doaj.org/article/11bb638f66494180a04aa4ba5bd041a9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 23, Iss 13, p 5912 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/23/13/5912/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s23135912","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 23; Issue 13; Pages: 5912","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s23135912","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23135912","pdf_url":"https://www.mdpi.com/1424-8220/23/13/5912/pdf?version=1687768398","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4382450226.pdf"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2097324787","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2144506857","https://openalex.org/W2169671170","https://openalex.org/W2193145675","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2612445135","https://openalex.org/W2743706233","https://openalex.org/W2752782242","https://openalex.org/W2884585870","https://openalex.org/W2916994131","https://openalex.org/W2929259582","https://openalex.org/W2963037989","https://openalex.org/W2963163009","https://openalex.org/W2963351448","https://openalex.org/W2963857746","https://openalex.org/W2982083293","https://openalex.org/W3033789093","https://openalex.org/W3034552520","https://openalex.org/W3106250896","https://openalex.org/W3122308411","https://openalex.org/W3180134609","https://openalex.org/W3210586215","https://openalex.org/W4297676427","https://openalex.org/W4386076325","https://openalex.org/W6750697433"],"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/W2348780717","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W4223892596"],"abstract_inverted_index":{"The":[0,29,208,238],"detection":[1,50,73,105,176,200,228,248,252],"algorithm":[2,34,60,74,239],"commonly":[3],"misses":[4],"obscured":[5,256],"pedestrians":[6,19,154,157,257,260],"in":[7,80,158,214,241,261],"traffic":[8],"scenes":[9],"with":[10,63,164],"a":[11,85,93,175],"high":[12],"pedestrian":[13,72],"density":[14],"because":[15],"mutual":[16],"occlusion":[17],"among":[18],"reduces":[20],"the":[21,26,32,36,40,46,49,54,58,64,71,90,101,111,116,121,124,135,142,149,170,188,196,204,211,226,234,247,251,262,266],"prediction":[22],"box":[23],"score":[24],"of":[25,57,89,103,106,123,152,191,198,210,250],"concealed":[27],"pedestrians.":[28],"paper":[30,243],"uses":[31],"YOLOv7":[33,59,227],"as":[35],"baseline":[37],"and":[38,128,155,169,202,258],"makes":[39],"following":[41,205],"three":[42],"improvements":[43],"by":[44],"investigating":[45],"variables":[47],"influencing":[48],"method's":[51],"performance:":[52],"First,":[53],"backbone":[55],"network":[56,68],"is":[61,98,172,184,220],"replaced":[62],"lightweight":[65],"feature":[66,95,112,117,126,131],"extraction":[67],"Mobilenetv3":[69],"since":[70],"frequently":[75],"needs":[76],"to":[77,119,133,140,145,160,173,186,231,265],"be":[78],"deployed":[79],"driverless":[81],"mobile,":[82],"which":[83,109,219],"requires":[84],"fast":[86],"operating":[87],"speed":[88],"algorithm;":[91],"second,":[92],"high-resolution":[94],"pyramid":[96,118],"structure":[97],"suggested":[99,212],"for":[100,148,167,255],"issue":[102],"missed":[104],"hidden":[107,153],"pedestrians,":[108],"upscales":[110],"maps":[113,127,132],"generated":[114],"from":[115],"increase":[120,246],"resolution":[122],"output":[125],"introduces":[129],"shallow":[130],"strengthen":[134],"distinctions":[136],"between":[137],"adjacent":[138],"sub-features":[139],"enhance":[141],"network's":[143],"ability":[144],"extract":[146],"features":[147,163],"visible":[150],"area":[151],"small-sized":[156,259],"order":[159],"produce":[161],"deeper":[162],"greater":[165],"differentiation":[166],"pedestrians;":[168],"third":[171],"suggest":[174],"head":[177],"based":[178],"on":[179,233],"an":[180],"attention":[181],"mechanism":[182],"that":[183],"employed":[185],"lower":[187,195,203],"confidence":[189],"level":[190],"target":[192],"neighboring":[193],"sub-features,":[194],"quantity":[197],"redundant":[199],"boxes,":[201],"NMS":[206],"computation.":[207],"mAP":[209],"approach":[213],"this":[215,242],"work":[216],"achieves":[217],"89.75%,":[218],"9.5":[221],"percentage":[222],"points":[223],"better":[224],"than":[225],"algorithm,":[229,253],"according":[230,264],"experiments":[232],"CrowdHuman":[235],"pedestrian-intensive":[236],"dataset.":[237],"proposed":[240],"can":[244],"considerably":[245],"performance":[249],"particularly":[254],"dataset,":[263],"experimental":[267],"effect":[268],"plots.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}