{"id":"https://openalex.org/W7162291175","doi":"https://doi.org/10.1109/jiot.2026.3696742","title":"LMFF-Net: Lightweight Multiscale Feature Fusion Object Detection for Underwater IoT Networks","display_name":"LMFF-Net: Lightweight Multiscale Feature Fusion Object Detection for Underwater IoT Networks","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162291175","doi":"https://doi.org/10.1109/jiot.2026.3696742"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2026.3696742","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3696742","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-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/A5136928043","display_name":"Xiushuai Xu","orcid":null},"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":"Xiushuai Xu","raw_affiliation_strings":["Jiangsu University of Science and Technology"],"raw_orcid":"https://orcid.org/0009-0002-7599-8963","affiliations":[{"raw_affiliation_string":"Jiangsu University of Science and Technology","institution_ids":["https://openalex.org/I4210096899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136902441","display_name":"Zhibin Xie","orcid":"https://orcid.org/0000-0003-4440-0076"},"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":"Zhibin Xie","raw_affiliation_strings":["Jiangsu University of Science and Technology"],"raw_orcid":"https://orcid.org/0000-0003-4440-0076","affiliations":[{"raw_affiliation_string":"Jiangsu University of Science and Technology","institution_ids":["https://openalex.org/I4210096899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136909171","display_name":"Qian Li","orcid":null},"institutions":[{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]},{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Li","raw_affiliation_strings":["Qilu University of Technology (Shandong Academy of Sciences)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qilu University of Technology (Shandong Academy of Sciences)","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136927856","display_name":"Xin Shu","orcid":"https://orcid.org/0000-0003-1079-8434"},"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":"Xin Shu","raw_affiliation_strings":["Jiangsu University of Science and Technology"],"raw_orcid":"https://orcid.org/0000-0003-1079-8434","affiliations":[{"raw_affiliation_string":"Jiangsu University of Science and Technology","institution_ids":["https://openalex.org/I4210096899"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136941970","display_name":"Changbin Shao","orcid":null},"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":"Changbin Shao","raw_affiliation_strings":["Jiangsu University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu University of Science and Technology","institution_ids":["https://openalex.org/I4210096899"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":0.47485586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"15","first_page":"34942","last_page":"34951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.47760000824928284,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.47760000824928284,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11698","display_name":"Underwater Acoustics Research","score":0.12150000035762787,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.10719999670982361,"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/object-detection","display_name":"Object detection","score":0.6391000151634216},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5078999996185303},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.46369999647140503},{"id":"https://openalex.org/keywords/underwater","display_name":"Underwater","score":0.44749999046325684},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4207000136375427},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.3846000134944916}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8108999729156494},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6391000151634216},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5078999996185303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4887000024318695},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.46369999647140503},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4530999958515167},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4207000136375427},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38190001249313354},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32280001044273376},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.267300009727478}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2026.3696742","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3696742","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2102605133","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2193145675","https://openalex.org/W2565639579","https://openalex.org/W2765793020","https://openalex.org/W2963037989","https://openalex.org/W3096609285","https://openalex.org/W3159459366","https://openalex.org/W3176831374","https://openalex.org/W4307900253","https://openalex.org/W4327652243","https://openalex.org/W4385222086","https://openalex.org/W4390276929","https://openalex.org/W4391074841","https://openalex.org/W4391614230","https://openalex.org/W4393147880","https://openalex.org/W4395453945","https://openalex.org/W4399384347","https://openalex.org/W4402513951","https://openalex.org/W4402586294","https://openalex.org/W4404489005","https://openalex.org/W4405989482","https://openalex.org/W4408126639","https://openalex.org/W4411688622","https://openalex.org/W4412031026","https://openalex.org/W4412434909","https://openalex.org/W4412456618","https://openalex.org/W4413915493","https://openalex.org/W4414285894","https://openalex.org/W4416743863","https://openalex.org/W7104556055","https://openalex.org/W7130586201","https://openalex.org/W7134187809","https://openalex.org/W7140181845","https://openalex.org/W7141395015"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,35,90,102,107,119,168,174,186,213,220,225],"advancement":[2],"of":[3,6,39,109,163,176,195,224],"Underwater":[4],"Internet":[5],"Things":[7],"(U-IoT)":[8],"networks,":[9],"underwater":[10,23],"object":[11,27,124,148,206],"detection":[12,17,116,207],"has":[13],"become":[14],"crucial.":[15],"However,":[16],"performance":[18],"is":[19,85,128,157],"severely":[20],"hindered":[21],"by":[22,166],"image":[24],"degradation,":[25],"small":[26,68,147],"scales":[28],"and":[29,49,70,94,111,145,215],"weak":[30],"textures,":[31],"as":[32,34,89],"well":[33],"stringent":[36],"resource":[37],"limitations":[38],"deployment":[40],"platforms.":[41],"Meanwhile,":[42],"existing":[43],"detectors":[44],"rely":[45],"on":[46,185,212],"heavy":[47],"backbones":[48],"standard":[50],"downsampling,":[51],"which":[52,130],"not":[53],"only":[54,198],"increases":[55],"computation":[56],"but":[57],"also":[58,218],"discards":[59],"fine-grained":[60,177],"spatial":[61,133,153,178],"information,":[62],"resulting":[63],"in":[64,180],"low":[65],"precision":[66],"for":[67],"objects":[69],"suboptimal":[71],"deployability.":[72],"To":[73],"address":[74],"these":[75],"challenges,":[76],"a":[77,122,151],"lightweight":[78],"multi-scale":[79,123,134],"feature":[80,91,99,120,143,164],"fusion":[81,154],"network":[82,93],"named":[83],"LMFF-Net":[84,104,191],"proposed.":[86],"Utilizing":[87],"RepGhostNet":[88],"extraction":[92],"incorporating":[95],"an":[96,193],"enhanced":[97],"bidirectional":[98],"pyramid":[100],"structure,":[101],"proposed":[103,226],"effectively":[105],"reduces":[106],"number":[108],"parameters":[110],"computational":[112],"cost":[113],"while":[114],"maintaining":[115],"accuracy.":[117],"In":[118],"pyramid,":[121],"enhancement":[125],"(MSOE)":[126],"module":[127,156],"designed,":[129],"synergistically":[131],"combines":[132],"convolutions":[135],"with":[136,197],"spatial-frequency":[137],"attention":[138],"mechanisms.":[139],"This":[140],"synergy":[141],"enhances":[142],"discriminability":[144],"improves":[146],"representation.":[149],"Furthermore,":[150],"channelized":[152],"(CSF)":[155],"constructed":[158],"to":[159],"achieve":[160],"lossless":[161],"downsampling":[162],"maps":[165],"leveraging":[167],"spatial-to-depth":[169],"transformation":[170],"principle,":[171],"thereby":[172],"maximizing":[173],"retention":[175],"information":[179],"deep":[181],"networks.":[182],"Experimental":[183],"results":[184],"DUO":[187],"dataset":[188],"show":[189],"that":[190],"achieves":[192],"mAP@0.5":[194],"84.7%":[196],"1.73":[199],"M":[200],"parameters,":[201],"significantly":[202],"outperforming":[203],"other":[204],"classic":[205],"models.":[208],"Additionally,":[209],"generalization":[210,222],"experiments":[211],"URPC2020":[214],"RUOD":[216],"datasets":[217],"demonstrate":[219],"excellent":[221],"ability":[223],"model.":[227]},"counts_by_year":[],"updated_date":"2026-08-13T07:04:57.449891","created_date":"2026-05-26T00:00:00"}