{"id":"https://openalex.org/W4415748257","doi":"https://doi.org/10.1109/tnnls.2025.3624356","title":"LEPD-Net: A Lightweight and Efficient Network for Pedestrian Detection","display_name":"LEPD-Net: A Lightweight and Efficient Network for Pedestrian Detection","publication_year":2025,"publication_date":"2025-10-31","ids":{"openalex":"https://openalex.org/W4415748257","doi":"https://doi.org/10.1109/tnnls.2025.3624356","pmid":"https://pubmed.ncbi.nlm.nih.gov/41171681"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3624356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3624356","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5068022227","display_name":"Wenliang Ge","orcid":"https://orcid.org/0009-0008-0722-4919"},"institutions":[{"id":"https://openalex.org/I4210096899","display_name":"Jiangsu University of Science and Technology","ror":"https://ror.org/00tyjp878","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210096899"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenliang Ge","raw_affiliation_strings":["Jiangsu University of Science and Technology, Zhenjiang, China"],"raw_orcid":"https://orcid.org/0009-0008-0722-4919","affiliations":[{"raw_affiliation_string":"Jiangsu University of Science and Technology, Zhenjiang, China","institution_ids":["https://openalex.org/I4210096899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083070890","display_name":"Shucheng Huang","orcid":"https://orcid.org/0000-0002-5435-5961"},"institutions":[{"id":"https://openalex.org/I4210096899","display_name":"Jiangsu University of Science and Technology","ror":"https://ror.org/00tyjp878","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210096899"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shucheng Huang","raw_affiliation_strings":["Jiangsu University of Science and Technology, Zhenjiang, China"],"raw_orcid":"https://orcid.org/0000-0002-5435-5961","affiliations":[{"raw_affiliation_string":"Jiangsu University of Science and Technology, Zhenjiang, China","institution_ids":["https://openalex.org/I4210096899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100732084","display_name":"Mingxing Li","orcid":"https://orcid.org/0009-0004-7417-5265"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingxing Li","raw_affiliation_strings":["Jingjiang College, Jiangsu University, Zhenjiang, China"],"raw_orcid":"https://orcid.org/0009-0004-7417-5265","affiliations":[{"raw_affiliation_string":"Jingjiang College, Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031056278","display_name":"Yifan Jiao","orcid":"https://orcid.org/0000-0002-8923-6997"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Jiao","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-8923-6997","affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5068022227"],"corresponding_institution_ids":["https://openalex.org/I4210096899"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30578003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"37","issue":"4","first_page":"1717","last_page":"1725"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8734999895095825,"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":0.8734999895095825,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.04320000112056732,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.017000000923871994,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.8355000019073486},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.8154000043869019},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7044000029563904},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6029999852180481},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5928000211715698},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5604000091552734},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.519599974155426},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5116000175476074}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.8355000019073486},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8154000043869019},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7504000067710876},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7044000029563904},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6029999852180481},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5928000211715698},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5604000091552734},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.519599974155426},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5116000175476074},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4726000130176544},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.45239999890327454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4424000084400177},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3856000006198883},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3822000026702881},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36739999055862427},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.36250001192092896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3587000072002411},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C80519477","wikidata":"https://www.wikidata.org/wiki/Q3532236","display_name":"Scenario testing","level":3,"score":0.2921000123023987},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.25}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2025.3624356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3624356","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:41171681","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41171681","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":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6396449109","display_name":null,"funder_award_id":"62276118","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,155],"pedestrian":[1,46,61,140,150],"detection":[2,23,62,70],"is":[3,142],"crucial":[4],"in":[5,89],"practical":[6,43],"applications,":[7],"such":[8],"as":[9],"autonomous":[10],"driving":[11],"and":[12,33,59,78,109,120,130,138,153],"video":[13],"surveillance.":[14],"However,":[15],"the":[16,42,75,85,111,133,146,162],"existing":[17],"research":[18],"mainly":[19],"focuses":[20],"on":[21,148],"improving":[22],"accuracy,":[24],"with":[25,38],"relatively":[26],"little":[27],"attention":[28,98],"paid":[29],"to":[30,73,82],"model":[31,76,147,160,170],"complexity":[32],"operational":[34],"efficiency.":[35],"In":[36,167],"scenarios":[37],"high":[39],"real-time":[40],"requirements,":[41],"deployment":[44],"of":[45,87,107],"detectors":[47],"still":[48],"faces":[49],"many":[50],"difficulties.":[51],"To":[52],"this":[53],"end,":[54],"we":[55,66,93],"propose":[56],"a":[57,68,95,104,136],"lightweight":[58,137],"efficient":[60,139],"network":[63],"(LEPD-Net).":[64],"First,":[65],"design":[67,94],"PoolFormer-based":[69],"head":[71],"(PDH)":[72],"reduce":[74],"computation":[77],"inference":[79,172],"time.":[80],"Second,":[81],"compensate":[83],"for":[84],"deficiency":[86],"PDH":[88,129],"global":[90,121],"context":[91],"modeling,":[92],"triple-branch":[96],"joint":[97],"module":[99],"(TJAM).":[100],"TJAM":[101,131],"uses":[102],"only":[103],"small":[105],"number":[106],"parameters":[108],"strengthens":[110],"model's":[112],"contextual":[113],"representation":[114],"by":[115,174],"capturing":[116],"spatial":[117],"location":[118],"dependencies":[119],"semantic":[122],"information":[123],"between":[124],"channels.":[125],"Finally,":[126],"after":[127],"incorporating":[128],"into":[132],"backbone":[134],"network,":[135],"detector":[141],"constructed.":[143],"We":[144],"benchmarked":[145],"mainstream":[149],"datasets":[151],"Caltech":[152],"CityPersons.":[154],"results":[156],"show":[157],"that":[158],"our":[159,169],"achieves":[161],"current":[163],"state-of-the-art":[164],"performance":[165],"level.":[166],"addition,":[168],"reduces":[171],"time":[173],"25%":[175],"while":[176],"maintaining":[177],"accuracy.":[178]},"counts_by_year":[],"updated_date":"2026-04-07T06:01:17.266235","created_date":"2025-10-31T00:00:00"}