{"id":"https://openalex.org/W3216226093","doi":"https://doi.org/10.1145/3460268.3460276","title":"A Novel UAV Aerial Vehicle Detection Method Based on Attention Mechanism and Multi-scale Feature Cross Fusion","display_name":"A Novel UAV Aerial Vehicle Detection Method Based on Attention Mechanism and Multi-scale Feature Cross Fusion","publication_year":2021,"publication_date":"2021-01-15","ids":{"openalex":"https://openalex.org/W3216226093","doi":"https://doi.org/10.1145/3460268.3460276","mag":"3216226093"},"language":"en","primary_location":{"id":"doi:10.1145/3460268.3460276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3460268.3460276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3460268.3460276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 2nd International Conference on Artificial Intelligence in Electronics Engineering","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3460268.3460276","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110119576","display_name":"Zhigang Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhigang Hou","raw_affiliation_strings":["Beijing University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026229683","display_name":"Yan Jin","orcid":"https://orcid.org/0000-0001-8956-7684"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128818","display_name":"Institute of Software","ror":"https://ror.org/033dfsn42","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128818"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Yan","raw_affiliation_strings":["Institute of Software Chinese Academy of Sciences Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Software Chinese Academy of Sciences Beijing, China","institution_ids":["https://openalex.org/I4210128818","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028766722","display_name":"Bowen Yang","orcid":"https://orcid.org/0000-0002-6305-5565"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Yang","raw_affiliation_strings":["Beijing University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057841485","display_name":"Zhiming Ding","orcid":"https://orcid.org/0000-0002-2057-5325"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128818","display_name":"Institute of Software","ror":"https://ror.org/033dfsn42","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128818"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiming Ding","raw_affiliation_strings":["Institute of Software Chinese Academy of Sciences Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Software Chinese Academy of Sciences Beijing, China","institution_ids":["https://openalex.org/I4210128818","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"51","last_page":"59"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9969000220298767,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9944999814033508,"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/computer-science","display_name":"Computer science","score":0.7861835956573486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7649394273757935},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.7339463829994202},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7138690948486328},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6624184846878052},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.6440613269805908},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5657481551170349},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5116549134254456},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.48445820808410645},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4671240448951721},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.44969022274017334},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4435086250305176},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4253571927547455},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.41687291860580444},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3289092779159546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7861835956573486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7649394273757935},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7339463829994202},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7138690948486328},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6624184846878052},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.6440613269805908},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5657481551170349},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5116549134254456},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.48445820808410645},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4671240448951721},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.44969022274017334},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4435086250305176},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4253571927547455},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.41687291860580444},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3289092779159546},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460268.3460276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3460268.3460276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3460268.3460276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 2nd International Conference on Artificial Intelligence in Electronics Engineering","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3460268.3460276","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3460268.3460276","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3460268.3460276","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 2nd International Conference on Artificial Intelligence in Electronics Engineering","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3216226093.pdf","grobid_xml":"https://content.openalex.org/works/W3216226093.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2133564696","https://openalex.org/W2194775991","https://openalex.org/W2288122362","https://openalex.org/W2570343428","https://openalex.org/W2613886079","https://openalex.org/W2797527871","https://openalex.org/W2963037989","https://openalex.org/W2991359031","https://openalex.org/W4293584584","https://openalex.org/W6730903564","https://openalex.org/W6739901393","https://openalex.org/W6750963839","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4396860960","https://openalex.org/W4390482660","https://openalex.org/W2972256598","https://openalex.org/W2610408157","https://openalex.org/W4388813151","https://openalex.org/W2612465689","https://openalex.org/W4284972948","https://openalex.org/W4237245474","https://openalex.org/W2099047584","https://openalex.org/W4327521163"],"abstract_inverted_index":{"With":[0],"the":[1,44,48,68,74,83,109,148,152,166,171,177,185],"rapid":[2],"development":[3],"of":[4,39,47,85,155],"artificial":[5],"intelligence":[6],"science,":[7],"more":[8,10],"and":[9,21,53,64,71,118,134,190],"researchers":[11],"try":[12],"to":[13,17,43,60,137],"use":[14],"deep":[15],"learning":[16],"train":[18],"neural":[19],"networks":[20],"have":[22],"achieved":[23],"great":[24],"success":[25],"in":[26,76,87,179],"object":[27,40,75],"detection.":[28,41],"Vehicle":[29],"detection":[30,153],"based":[31,164],"on":[32,165],"UAV":[33],"image":[34,55],"is":[35,58,145,158],"a":[36,92,114],"special":[37],"field":[38],"Due":[42],"low":[45],"resolution":[46],"vehicle":[49,178],"object,":[50],"complex":[51,77],"background,":[52],"less":[54],"information,":[56],"it":[57],"challenging":[59],"extract":[61,138],"robust":[62],"visual":[63],"spatial":[65,133],"features":[66],"from":[67],"depth":[69],"network":[70,96,103],"accurately":[72],"locate":[73],"scenes.":[78],"In":[79,112],"this":[80],"paper,":[81],"combining":[82],"characteristics":[84],"vehicles":[86],"aerial":[88],"images,":[89],"we":[90],"design":[91],"novel":[93],"feature":[94,101,119,126],"pyramid":[95,102],"called":[97],"channel-spatial":[98],"attention":[99,116],"fused":[100],"(CSF-FPN)":[104],"with":[105,131,184],"Faster":[106,149],"R-CNN":[107,150],"as":[108],"basic":[110],"framework.":[111],"CSF-FPN,":[113],"hybrid":[115],"mechanism":[117],"cross-fusion":[120],"module":[121],"are":[122,192],"introduced,":[123],"so":[124],"that":[125,170],"maps":[127],"can":[128],"be":[129],"generated":[130],"enhanced":[132],"channel":[135],"interdependence":[136],"richer":[139],"semantic":[140],"information.":[141],"After":[142],"our":[143],"CSF-FPN":[144],"integrated":[146],"into":[147],"network,":[151],"performance":[154],"small":[156],"objects":[157],"greatly":[159],"improved.":[160,193],"The":[161],"experimental":[162],"results":[163],"VEDIA":[167],"Dataset":[168],"showed":[169],"proposed":[172],"framework":[173],"could":[174],"effectively":[175],"detect":[176],"large":[180],"scene":[181],"azimuth.":[182],"Compared":[183],"existing":[186],"advanced":[187],"methods,":[188],"mAP":[189],"F1-score":[191]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
