{"id":"https://openalex.org/W7152543612","doi":"https://doi.org/10.1145/3807778","title":"A Multimodal Transformer Approach for UAV Detection and Aerial Object Recognition Using Radar, Audio, and Video Data","display_name":"A Multimodal Transformer Approach for UAV Detection and Aerial Object Recognition Using Radar, Audio, and Video Data","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7152543612","doi":"https://doi.org/10.1145/3807778"},"language":"en","primary_location":{"id":"doi:10.1145/3807778","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807778","pdf_url":null,"source":{"id":"https://openalex.org/S2506189754","display_name":"ACM Transactions on Cyber-Physical Systems","issn_l":"2378-962X","issn":["2378-962X","2378-9638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Cyber-Physical Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3807778","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115647018","display_name":"Mauro Larrat","orcid":null},"institutions":[{"id":"https://openalex.org/I59606676","display_name":"Universidade Federal do Par\u00e1","ror":"https://ror.org/03q9sr818","country_code":"BR","type":"education","lineage":["https://openalex.org/I59606676"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Mauro Larrat","raw_affiliation_strings":["Institute of Exact and Natural Sciences, Federal University of Para, Bel\u00e9m, Brazil"],"raw_orcid":"https://orcid.org/0009-0008-4963-4625","affiliations":[{"raw_affiliation_string":"Institute of Exact and Natural Sciences, Federal University of Para, Bel\u00e9m, Brazil","institution_ids":["https://openalex.org/I59606676"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012669201","display_name":"Claudomiro Sales","orcid":"https://orcid.org/0000-0002-2735-1383"},"institutions":[{"id":"https://openalex.org/I59606676","display_name":"Universidade Federal do Par\u00e1","ror":"https://ror.org/03q9sr818","country_code":"BR","type":"education","lineage":["https://openalex.org/I59606676"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Claudomiro Sales","raw_affiliation_strings":["Institute of Exact and Natural Sciences, Federal University of Para, Bel\u00e9m, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-2735-1383","affiliations":[{"raw_affiliation_string":"Institute of Exact and Natural Sciences, Federal University of Para, Bel\u00e9m, Brazil","institution_ids":["https://openalex.org/I59606676"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5115647018"],"corresponding_institution_ids":["https://openalex.org/I59606676"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.59463329,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":"3","first_page":"1","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.23800000548362732,"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":0.23800000548362732,"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/T11133","display_name":"UAV Applications and Optimization","score":0.1745000034570694,"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"}},{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.12380000203847885,"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/robustness","display_name":"Robustness (evolution)","score":0.6186000108718872},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5080000162124634},{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.4837000072002411},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4733999967575073},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.460999995470047},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4593000113964081},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.37610000371932983},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.3634999990463257}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6840000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6335999965667725},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6186000108718872},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5698000192642212},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5080000162124634},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4733999967575073},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.460999995470047},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4593000113964081},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.34389999508857727},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2842999994754791},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3807778","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807778","pdf_url":null,"source":{"id":"https://openalex.org/S2506189754","display_name":"ACM Transactions on Cyber-Physical Systems","issn_l":"2378-962X","issn":["2378-962X","2378-9638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Cyber-Physical Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3807778","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3807778","pdf_url":null,"source":{"id":"https://openalex.org/S2506189754","display_name":"ACM Transactions on Cyber-Physical Systems","issn_l":"2378-962X","issn":["2378-962X","2378-9638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Cyber-Physical Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6472973227500916,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"},{"score":0.4014102816581726,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2289146454","https://openalex.org/W3210343736","https://openalex.org/W4225660138","https://openalex.org/W4285056112","https://openalex.org/W4285303111","https://openalex.org/W4316813569","https://openalex.org/W4317927920","https://openalex.org/W4320060004","https://openalex.org/W4320891764","https://openalex.org/W4327927547","https://openalex.org/W4377103096","https://openalex.org/W4381163413","https://openalex.org/W4381163582","https://openalex.org/W4384932895","https://openalex.org/W4385416665","https://openalex.org/W4386647017","https://openalex.org/W4388004997","https://openalex.org/W4388116106","https://openalex.org/W4388228562","https://openalex.org/W4389366414","https://openalex.org/W4391145681","https://openalex.org/W4392713038","https://openalex.org/W4396628298","https://openalex.org/W4396852331","https://openalex.org/W4398131482","https://openalex.org/W4398758453","https://openalex.org/W4402350770","https://openalex.org/W4402675216"],"related_works":[],"abstract_inverted_index":{"The":[0,44,123,148],"newly":[1],"proposed":[2,124],"multimodal":[3,66,184],"transformer":[4,67],"architecture":[5,38],"offers":[6],"a":[7,159],"new":[8],"paradigm":[9],"for":[10,39,68],"UAV":[11,162],"detection":[12,114,188],"and":[13,34,52,101,119,145,176],"aerial":[14,95],"object":[15],"recognition.":[16],"It":[17],"introduces":[18],"an":[19,170],"innovative":[20],"way":[21],"of":[22,47,86,169,173,183],"feeding":[23],"multiple":[24],"data":[25,185],"streams,":[26],"such":[27,97,139],"as":[28,98,140,158],"audio,":[29],"infrared":[30],"video,":[31,33],"RGB":[32],"radar,":[35],"into":[36],"the":[37,56,59,65,78,83,181],"processing,":[40],"using":[41],"independent":[42],"modalities.":[43],"unique":[45],"features":[46,60],"each":[48],"modality":[49],"are":[50,61,105],"attached":[51],"processed":[53],"together":[54],"in":[55,161,186],"architecture,":[57],"where":[58],"then":[62],"exposed":[63],"to":[64,81,107,133,156],"classification.":[69],"Thus,":[70,164],"all":[71],"complementary":[72],"information":[73],"can":[74],"be":[75],"pooled":[76],"within":[77],"integration":[79],"framework":[80],"allow":[82],"model":[84,125],"discrimination":[85],"any":[87],"drone":[88],"target":[89],"under":[90,137],"outdoor":[91],"conditions":[92,138],"from":[93],"other":[94],"objects":[96],"birds,":[99],"helicopters,":[100],"airplanes.":[102],"These":[103],"methodologies":[104],"expected":[106],"outperform":[108],"traditional":[109],"single-modality":[110],"systems":[111],"by":[112],"improving":[113],"accuracy":[115],"through":[116,130],"class":[117],"balancing":[118],"addressing":[120],"modality-specific":[121],"limitations.":[122],"has":[126,153],"been":[127],"further":[128],"tested":[129],"various":[131],"experiments":[132],"evaluate":[134],"its":[135],"robustness":[136],"missing":[141],"entries,":[142],"corrupted":[143],"data,":[144],"synthetic":[146],"inputs.":[147],"results":[149],"suggest":[150],"that":[151],"it":[152],"strong":[154],"potential":[155,182],"serve":[157],"benchmark":[160],"detection.":[163],"this":[165],"work":[166],"takes":[167],"part":[168],"emerging":[171],"body":[172],"sensor":[174],"fusion":[175],"deep":[177],"learning-related":[178],"research,":[179],"demonstrating":[180],"real-world":[187],"problems.":[189]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-04-10T00:00:00"}
