{"id":"https://openalex.org/W4417002649","doi":"https://doi.org/10.1109/tits.2025.3638627","title":"Removal Then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection","display_name":"Removal Then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection","publication_year":2025,"publication_date":"2025-12-04","ids":{"openalex":"https://openalex.org/W4417002649","doi":"https://doi.org/10.1109/tits.2025.3638627"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2025.3638627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3638627","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","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/A5105918715","display_name":"T. C. Zhao","orcid":"https://orcid.org/0000-0002-2487-3944"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianyi Zhao","raw_affiliation_strings":["Institute of Artificial Intelligence, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045992045","display_name":"Maoxun Yuan","orcid":"https://orcid.org/0000-0001-7463-7328"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maoxun Yuan","raw_affiliation_strings":["Institute of Artificial Intelligence, Beihang University, Haidian, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence, Beihang University, Haidian, Beijing, China","institution_ids":["https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101773676","display_name":"Feng Jiang","orcid":"https://orcid.org/0000-0001-8817-6657"},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Jiang","raw_affiliation_strings":["Beijing Institute of Control and Electronic Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Control and Electronic Technology, Beijing, China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100332750","display_name":"Nan Wang","orcid":"https://orcid.org/0000-0002-4062-4630"},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Wang","raw_affiliation_strings":["Beijing Institute of Control and Electronic Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Control and Electronic Technology, Beijing, China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079657274","display_name":"Xingxing Wei","orcid":"https://orcid.org/0000-0002-0778-8377"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingxing Wei","raw_affiliation_strings":["Institute of Artificial Intelligence, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I4210100255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5105918715"],"corresponding_institution_ids":["https://openalex.org/I4210100255"],"apc_list":null,"apc_paid":null,"fwci":4.284,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.95030792,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"27","issue":"2","first_page":"2504","last_page":"2519"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5367000102996826,"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":0.5367000102996826,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.1525000035762787,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.13570000231266022,"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/object-detection","display_name":"Object detection","score":0.801800012588501},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5264000296592712},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5164999961853027},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5041999816894531},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5033000111579895},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4740000069141388},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44839999079704285},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43810001015663147},{"id":"https://openalex.org/keywords/object-class-detection","display_name":"Object-class detection","score":0.3946000039577484}],"concepts":[{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.801800012588501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7526999711990356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.720300018787384},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6338000297546387},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5264000296592712},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5041999816894531},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5033000111579895},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44839999079704285},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43810001015663147},{"id":"https://openalex.org/C71681937","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object-class detection","level":5,"score":0.3946000039577484},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3718000054359436},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.3142000138759613},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2980000078678131},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.27129998803138733},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C2778971668","wikidata":"https://www.wikidata.org/wiki/Q5510284","display_name":"Fusion rules","level":4,"score":0.25279998779296875},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.25200000405311584},{"id":"https://openalex.org/C53073257","wikidata":"https://www.wikidata.org/wiki/Q7075021","display_name":"Object-oriented design","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3638627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3638627","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"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":{"In":[0],"recent":[1],"years,":[2],"object":[3,40,48,74,211,225],"detection":[4,41,69,75,226],"utilizing":[5],"both":[6,114,162],"visible":[7],"(RGB)":[8],"and":[9,18,36,46,117,150,158,183,238],"thermal":[10],"infrared":[11],"(IR)":[12],"imagery":[13],"has":[14,19],"garnered":[15],"extensive":[16],"attention":[17],"been":[20],"widely":[21],"implemented":[22],"across":[23,50],"a":[24,51,152,170,184,209],"diverse":[25],"array":[26],"of":[27,53,84,110,123,202,232],"fields.":[28],"By":[29],"leveraging":[30],"the":[31,39,81,97,108,115,128,142,192,200,203,214,229],"complementary":[32,82],"properties":[33],"between":[34],"RGB":[35,116],"IR":[37,118],"images,":[38],"task":[42],"can":[43],"achieve":[44],"reliable":[45],"robust":[47],"localization":[49],"variety":[52],"lighting":[54],"conditions,":[55],"from":[56,96,107,141,161],"daytime":[57],"to":[58,78,156,175,189],"nighttime":[59],"environments.":[60],"While":[61],"RGB-IR":[62,224],"multi-modal":[63,73,103,148],"data":[64],"input":[65],"generally":[66],"enhances":[67],"overall":[68],"performance,":[70],"most":[71],"existing":[72],"methods":[76],"fail":[77],"fully":[79],"exploit":[80],"potential":[83],"these":[85],"two":[86],"modalities.":[87,119,163],"We":[88],"believe":[89],"that":[90],"this":[91,136,166],"issue":[92],"arises":[93],"not":[94],"only":[95],"challenges":[98],"associated":[99],"with":[100],"effectively":[101],"integrating":[102],"information":[104,122,149,178],"but":[105],"also":[106],"presence":[109],"redundant":[111,121],"features":[112,160,194],"in":[113],"The":[120,235],"each":[124,180],"modality":[125,181],"will":[126],"exacerbate":[127],"fusion":[129,205],"imprecision":[130],"problems":[131],"during":[132],"propagation.":[133],"To":[134,198],"address":[135],"issue,":[137],"we":[138,168,207],"draw":[139],"inspiration":[140],"human":[143],"cognitive":[144],"mechanisms":[145],"for":[146,195],"processing":[147],"propose":[151],"novel":[153],"coarse-to-fine":[154,204],"perspective":[155],"purify":[157],"fuse":[159],"Specifically,":[164],"following":[165],"perspective,":[167],"design":[169],"Redundant":[171],"Spectrum":[172],"Removal":[173,215],"module":[174,188],"remove":[176],"interfering":[177],"within":[179],"coarsely":[182],"Dynamic":[185],"Feature":[186],"Selection":[187,217],"finely":[190],"select":[191],"desired":[193],"feature":[196],"fusion.":[197],"verify":[199,228],"effectiveness":[201],"strategy,":[206],"construct":[208],"new":[210],"detector":[212],"called":[213],"then":[216],"Detector":[218],"(RSDet).":[219],"Extensive":[220],"experiments":[221],"on":[222],"five":[223],"datasets":[227],"superior":[230],"performance":[231],"our":[233],"method.":[234],"source":[236],"code":[237],"results":[239],"are":[240],"available":[241],"at":[242],"<uri":[243],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[244],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/Zhao-Tian-yi/RSDet.git</uri>":[245]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-04T00:00:00"}