{"id":"https://openalex.org/W4416250769","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227737","title":"GCGP-YOLO: Global-Local and Channel Grouping Perception Network for Small Object Detection based on YOLOv8","display_name":"GCGP-YOLO: Global-Local and Channel Grouping Perception Network for Small Object Detection based on YOLOv8","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250769","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227737"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227737","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227737","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5081848302","display_name":"Jinsheng Liu","orcid":"https://orcid.org/0000-0002-7035-0052"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinsheng Liu","raw_affiliation_strings":["Jiangnan University,Wuxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangnan University,Wuxi,China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013165643","display_name":"Tao Yan","orcid":"https://orcid.org/0000-0002-4494-9598"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Yan","raw_affiliation_strings":["Jiangnan University,Wuxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangnan University,Wuxi,China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xianglong Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianglong Chen","raw_affiliation_strings":["Jiangnan University,Wuxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangnan University,Wuxi,China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113409529","display_name":"Zhineng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhineng Zhang","raw_affiliation_strings":["Jiangnan University,Wuxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangnan University,Wuxi,China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022480641","display_name":"Chenglong Wang","orcid":"https://orcid.org/0000-0002-7103-7850"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglong Wang","raw_affiliation_strings":["Jiangnan University,Wuxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangnan University,Wuxi,China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110544324","display_name":"Weilong Huang","orcid":"https://orcid.org/0009-0002-0974-9766"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weilong Huang","raw_affiliation_strings":["Jiangnan University,Wuxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangnan University,Wuxi,China","institution_ids":["https://openalex.org/I111599522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30262911,"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":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9650999903678894,"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":0.9650999903678894,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.003700000001117587,"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"}},{"id":"https://openalex.org/T11133","display_name":"UAV Applications and Optimization","score":0.0034000000450760126,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/pooling","display_name":"Pooling","score":0.6891999840736389},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.659500002861023},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.607699990272522},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5454999804496765},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5443999767303467},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5440999865531921},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.516700029373169},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5098999738693237},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5027999877929688},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4977000057697296}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7477999925613403},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6891999840736389},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.659500002861023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6365000009536743},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.607699990272522},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5454999804496765},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5443999767303467},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5440999865531921},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.516700029373169},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5098999738693237},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5027999877929688},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4977000057697296},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4731999933719635},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4339999854564667},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.37770000100135803},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3628000020980835},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3619999885559082},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.3458999991416931},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25929999351501465},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227737","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227737","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2193145675","https://openalex.org/W2571149404","https://openalex.org/W2592939477","https://openalex.org/W2601564443","https://openalex.org/W2924574544","https://openalex.org/W2924873663","https://openalex.org/W2963091558","https://openalex.org/W2963150697","https://openalex.org/W2963163009","https://openalex.org/W2963351448","https://openalex.org/W2964241181","https://openalex.org/W2988452521","https://openalex.org/W2989611864","https://openalex.org/W2993756598","https://openalex.org/W3009396058","https://openalex.org/W3096609285","https://openalex.org/W3175515048","https://openalex.org/W3205100603","https://openalex.org/W4205954621","https://openalex.org/W4221040873","https://openalex.org/W4226334005","https://openalex.org/W4313003861","https://openalex.org/W4386076325","https://openalex.org/W4386160491","https://openalex.org/W4386319016","https://openalex.org/W4400859202","https://openalex.org/W4401415788","https://openalex.org/W4402351959","https://openalex.org/W4402754006","https://openalex.org/W4402754177"],"related_works":[],"abstract_inverted_index":{"Due":[0],"to":[1,26,38,83,107,111,164,175],"the":[2,31,85,94,101,109,113,148,153,166,173,186,190,200],"wide":[3],"field":[4],"of":[5,33,98,115,169,210],"view,":[6],"small":[7,34,44,61,116,170,179],"object":[8,12,21,62],"sizes,":[9],"and":[10,29,40,70,128,192,212,221],"high":[11],"densities":[13],"in":[14,100,214],"Unmanned":[15],"Aerial":[16],"Vehicle":[17],"(UAV)":[18],"imagery,":[19],"conventional":[20],"detection":[22,63],"networks":[23],"often":[24],"struggle":[25],"effectively":[27],"extract":[28],"perceive":[30],"features":[32],"objects.":[35,45,117],"This":[36],"leads":[37],"misclassification":[39],"localization":[41],"errors":[42],"for":[43,65],"To":[46],"address":[47],"these":[48],"challenges,":[49],"we":[50,75,125,157],"propose":[51,76,126],"GCGP-YOLO,":[52],"a":[53,77,133],"novel":[54],"network":[55,110,174,198],"based":[56],"on":[57,178,189],"YOLOv8n":[58],"that":[59,135,196],"improves":[60],"performance":[64],"aerial":[66],"image":[67],"through":[68],"Global-Local":[69],"Channel":[71,78],"Grouping":[72,79],"Perception.":[73],"Specifically,":[74],"Perception":[80],"Module":[81],"(CGPM)":[82],"model":[84],"global":[86,102],"context":[87],"information":[88,104,150],"while":[89],"extracting":[90],"local":[91],"features.":[92],"Subsequently,":[93],"implicit":[95],"semantic":[96],"relations":[97],"objects":[99,180],"contextual":[103],"are":[105,223],"employed":[106],"guide":[108],"enhance":[112],"perception":[114],"Meanwhile,":[118],"by":[119,152],"combining":[120],"large":[121],"separable":[122],"kernel":[123],"convolutions,":[124],"Long":[127],"Short":[129],"Dependency":[130],"Pooling":[131],"(LSDP),":[132],"method":[134],"captures":[136],"long-range":[137],"dependencies":[138],"without":[139],"introducing":[140],"additional":[141],"computational":[142,205],"cost":[143],"or":[144],"parameters,":[145],"thereby":[146],"enriching":[147],"fine-grained":[149],"extracted":[151],"network.":[154],"In":[155],"addition,":[156],"construct":[158],"Linear":[159],"Deformable":[160],"Convolution":[161],"Block":[162],"(LDCB)":[163],"fit":[165],"diverse":[167],"shapes":[168],"objects,":[171],"enabling":[172],"focus":[176],"more":[177],"during":[181],"multi-scale":[182],"feature":[183],"fusion.":[184],"Finally,":[185],"experimental":[187],"results":[188],"VisDrone2019":[191],"TinyPerson":[193],"datasets":[194],"demonstrate":[195],"our":[197],"outperforms":[199],"baseline":[201],"models":[202,222],"with":[203],"lower":[204],"cost,":[206],"achieving":[207],"significant":[208],"improvements":[209],"8.30%":[211],"5.68%":[213],"mAP<inf":[215],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[216],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">50</inf>,":[217],"respectively.":[218],"The":[219],"code":[220],"available":[224],"at":[225],"https://github.com/YT3DVision/GCGP-YOLO.":[226]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-14T00:00:00"}