{"id":"https://openalex.org/W7151032290","doi":"https://doi.org/10.48550/arxiv.2604.03176","title":"SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection","display_name":"SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7151032290","doi":"https://doi.org/10.48550/arxiv.2604.03176"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03176","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.03176","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133046391","display_name":"Wenfeng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wenfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133053090","display_name":"Jun Ni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133031418","display_name":"Yue Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133029200","display_name":"Xiaodong Pei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pei, Xiaodong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133053789","display_name":"Wei Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133024345","display_name":"Qibing Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Qibing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133043196","display_name":"Lei Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8982999920845032,"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":0.8982999920845032,"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/T11019","display_name":"Image Enhancement Techniques","score":0.01769999973475933,"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":0.013199999928474426,"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/object-detection","display_name":"Object detection","score":0.6984999775886536},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5694000124931335},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5059999823570251},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5040000081062317},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46399998664855957},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.4372999966144562},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41600000858306885},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.40630000829696655},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.40630000829696655}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7511000037193298},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7145000100135803},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6984999775886536},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6826000213623047},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5694000124931335},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5059999823570251},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.4372999966144562},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41600000858306885},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.40630000829696655},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.3806999921798706},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.34929999709129333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.3082999885082245},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2904999852180481},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2773999869823456},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2648000121116638},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03176","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.03176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03176","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Object":[0],"detection":[1,71,213],"in":[2,72,95,123],"unmanned":[3],"aerial":[4,186],"vehicle":[5],"(UAV)":[6],"images":[7],"remains":[8],"a":[9,57,88,131,210],"highly":[10],"challenging":[11,185],"task,":[12],"primarily":[13],"caused":[14],"by":[15],"the":[16,22,43,76,97,116,120,158,168,192],"complexity":[17],"of":[18,24,105,119,125,176,195],"background":[19,110],"noise":[20],"and":[21,38,99,128,150,189,200,215],"imbalance":[23],"target":[25],"scales.":[26],"Traditional":[27],"methods":[28],"easily":[29],"struggle":[30],"to":[31,40,113,144,165,167],"effectively":[32],"separate":[33],"objects":[34],"from":[35,109],"intricate":[36],"backgrounds":[37],"fail":[39],"fully":[41],"leverage":[42],"rich":[44],"multi-scale":[45,77,106],"information":[46],"contained":[47],"within":[48],"images.":[49,74],"To":[50],"address":[51],"these":[52],"issues,":[53],"we":[54],"have":[55],"developed":[56],"synergistic":[58,132],"feature":[59,133],"fusion":[60],"network":[61,135],"(SFFNet)":[62],"with":[63],"dual-domain":[64,79],"edge":[65,90],"enhancement":[66],"specifically":[67],"tailored":[68],"for":[69],"object":[70,107,148],"UAV":[73],"Firstly,":[75],"dynamic":[78],"coupling":[80],"(MDDC)":[81],"module":[82,162],"is":[83,137],"designed.":[84,181],"This":[85],"component":[86],"introduces":[87],"dual-driven":[89],"extraction":[91],"architecture":[92],"that":[93],"operates":[94],"both":[96,126],"frequency":[98],"spatial":[100],"domains,":[101],"enabling":[102],"effective":[103],"decoupling":[104],"edges":[108],"noise.":[111],"Secondly,":[112],"further":[114],"enhance":[115],"representation":[117],"capability":[118],"model's":[121],"neck":[122],"terms":[124],"geometric":[127],"semantic":[129],"information,":[130],"pyramid":[134],"(SFPN)":[136],"proposed.":[138],"SFPN":[139],"leverages":[140],"linear":[141],"deformable":[142],"convolutions":[143],"adaptively":[145],"capture":[146],"irregular":[147],"shapes":[149],"establishes":[151],"long-range":[152],"contextual":[153],"associations":[154],"around":[155],"targets":[156],"through":[157],"designed":[159],"wide-area":[160],"perception":[161],"(WPM).":[163],"Moreover,":[164],"adapt":[166],"various":[169],"applications":[170],"or":[171],"resource-constrained":[172],"scenarios,":[173],"six":[174],"detectors":[175],"different":[177],"scales":[178],"(N/S/M/B/L/X)":[179],"are":[180],"Experiments":[182],"on":[183],"two":[184],"datasets":[187],"(VisDrone":[188],"UAVDT)":[190],"demonstrate":[191],"outstanding":[193],"performance":[194],"SFFNet-X,":[196],"achieving":[197],"36.8":[198],"AP":[199],"20.6":[201],"AP,":[202],"respectively.":[203],"The":[204,218],"lightweight":[205],"models":[206],"(N/S)":[207],"also":[208],"maintain":[209],"balance":[211],"between":[212],"accuracy":[214],"parameter":[216],"efficiency.":[217],"code":[219],"will":[220],"be":[221],"available":[222],"at":[223],"https://github.com/CQNU-ZhangLab/SFFNet.":[224]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-07T00:00:00"}