{"id":"https://openalex.org/W3117877810","doi":"https://doi.org/10.1109/rcar49640.2020.9303293","title":"Aerospace target detection based on complex background","display_name":"Aerospace target detection based on complex background","publication_year":2020,"publication_date":"2020-09-28","ids":{"openalex":"https://openalex.org/W3117877810","doi":"https://doi.org/10.1109/rcar49640.2020.9303293","mag":"3117877810"},"language":"en","primary_location":{"id":"doi:10.1109/rcar49640.2020.9303293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar49640.2020.9303293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Real-time Computing and Robotics (RCAR)","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/A5043490549","display_name":"Yanning Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanning Wang","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101891945","display_name":"Xiaoyu Wang","orcid":"https://orcid.org/0000-0001-9397-4485"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Wang","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401045","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0001-6157-0662"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442542","display_name":"Jingjing Liu","orcid":"https://orcid.org/0009-0002-6277-5816"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Liu","raw_affiliation_strings":["National Laboratory of Aerospace Intelligent Control Technology, Beijing Aerospace Automatic Control Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Aerospace Intelligent Control Technology, Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101666810","display_name":"Song Xu","orcid":"https://orcid.org/0000-0002-5722-6428"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Xu","raw_affiliation_strings":["National Laboratory of Aerospace Intelligent Control Technology, Beijing Aerospace Automatic Control Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Aerospace Intelligent Control Technology, Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5043490549"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43162264,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"45","issue":null,"first_page":"505","last_page":"510"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9986000061035156,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/upsampling","display_name":"Upsampling","score":0.8326181173324585},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7478201389312744},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.710574746131897},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6947854161262512},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.618003785610199},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5926339030265808},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5322335362434387},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5067339539527893},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4656674861907959},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.46280282735824585},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3236199617385864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2682010531425476},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23879021406173706},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16210585832595825},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09464466571807861},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08831223845481873}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8326181173324585},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7478201389312744},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.710574746131897},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6947854161262512},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.618003785610199},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5926339030265808},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5322335362434387},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5067339539527893},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4656674861907959},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.46280282735824585},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3236199617385864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2682010531425476},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23879021406173706},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16210585832595825},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09464466571807861},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08831223845481873},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rcar49640.2020.9303293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar49640.2020.9303293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Real-time Computing and Robotics (RCAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1964230011","https://openalex.org/W1964428975","https://openalex.org/W2089665028","https://openalex.org/W2144506857","https://openalex.org/W2168356304","https://openalex.org/W2519080876","https://openalex.org/W2570343428","https://openalex.org/W2579985080","https://openalex.org/W2796347433","https://openalex.org/W2797527871","https://openalex.org/W2884585870","https://openalex.org/W2892213214","https://openalex.org/W2919115771","https://openalex.org/W2963037989","https://openalex.org/W2963420686","https://openalex.org/W2963840672","https://openalex.org/W2964121718","https://openalex.org/W2997747012","https://openalex.org/W3020806379"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3155117723","https://openalex.org/W1991429770","https://openalex.org/W4292830139","https://openalex.org/W4319309705","https://openalex.org/W2894651257","https://openalex.org/W3200590620","https://openalex.org/W4200172193","https://openalex.org/W4303926741"],"abstract_inverted_index":{"Problems":[0],"such":[1],"as":[2,142],"small":[3,95,172],"object":[4,96,173],"and":[5,25,30,47,83,136,154,174,178],"helicopter":[6,29,46],"(and":[7],"ship)":[8],"mutual":[9],"occlusion":[10,128],"are":[11],"difficult":[12],"to":[13,22,42,102,121,126,147,161,185],"detect":[14,127,171],"in":[15,72],"complex":[16,50],"backgrounds,":[17],"which":[18,157],"poses":[19],"great":[20],"challenges":[21],"the":[23,35,43,64,70,73,89,92,111,118,123,148,151,155,167,180,188],"accuracy":[24,90,182],"real-time":[26,40,192],"detection":[27,44,181],"of":[28,34,45,66,91,107,190],"ship.":[31],"The":[32,163],"application":[33],"YOLOV3":[36,67,169],"algorithm":[37,114,133],"with":[38,76],"high":[39],"performance":[41],"ship":[48],"under":[49,187],"backgrounds":[51],"cannot":[52],"reach":[53],"a":[54,80,84],"satisfactory":[55],"level.":[56],"This":[57],"paper":[58],"has":[59],"made":[60],"four":[61],"improvements":[62],"on":[63],"basis":[65],"algorithm:":[68],"l)Replace":[69],"downsampling":[71],"backbone":[74],"network":[75],"dilated":[77],"convolution,":[78],"maintain":[79],"higher":[81],"resolution":[82],"larger":[85],"receptive":[86],"field,":[87],"improve":[88,122,179],"model":[93],"for":[94],"detection;":[97],"2)Introduce":[98],"channel":[99],"attention":[100],"module":[101],"extract":[103],"more":[104],"semantic":[105],"information":[106],"target":[108],"objects;":[109],"3)optimizes":[110],"non-maximum":[112],"suppression":[113],"by":[115],"linear":[116],"declining":[117],"confidence":[119],"score":[120],"model's":[124],"ability":[125],"helicopter(and":[129,176],"ship);":[130],"4)":[131],"IOU":[132,139],"is":[134,140],"optimized":[135],"solved":[137],"When":[138],"used":[141],"Loss,":[143],"Loss":[144],"=0":[145],"due":[146],"disjointness":[149],"between":[150],"prediction":[152],"box":[153],"groundtruth,":[156],"makes":[158],"it":[159],"impossible":[160],"optimize.":[162],"results":[164],"show":[165],"that":[166],"improved":[168],"can":[170],"occlude":[175],"ship),":[177],"from":[183],"74.04%":[184],"89.04%":[186],"premise":[189],"ensuring":[191],"performance.":[193]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
