{"id":"https://openalex.org/W4415178766","doi":"https://doi.org/10.1109/lsp.2025.3621166","title":"Lightweight Spatial-Channel-Frequency Network for RGB-Thermal Salient Object Detection","display_name":"Lightweight Spatial-Channel-Frequency Network for RGB-Thermal Salient Object Detection","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415178766","doi":"https://doi.org/10.1109/lsp.2025.3621166"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2025.3621166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3621166","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5100755464","display_name":"Heng Zhou","orcid":"https://orcid.org/0000-0003-2770-2785"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Zhou","raw_affiliation_strings":["School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"],"raw_orcid":"https://orcid.org/0000-0003-2770-2785","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wanting Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanting Hong","raw_affiliation_strings":["School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111910557","display_name":"Zhenxi Zhang","orcid":"https://orcid.org/0009-0009-6297-623X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenxi Zhang","raw_affiliation_strings":["Xidian University, Xi&#x2019;an, China","School of Electronic Engineering, Xidian University, Xi&#x0027;an, China"],"raw_orcid":"https://orcid.org/0009-0009-6297-623X","affiliations":[{"raw_affiliation_string":"Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x0027;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036666352","display_name":"Xiaoxiong Liu","orcid":"https://orcid.org/0000-0002-8061-5974"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxiong Liu","raw_affiliation_strings":["School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021767311","display_name":"Xiaojun Wu","orcid":"https://orcid.org/0000-0003-3606-1211"},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Jun Wu","raw_affiliation_strings":["School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.7251,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.96448484,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"32","issue":null,"first_page":"4009","last_page":"4013"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9825000166893005,"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"}},"topics":[{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9825000166893005,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9681000113487244,"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/salient","display_name":"Salient","score":0.7770000100135803},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7199000120162964},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6822999715805054},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6288999915122986},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5056999921798706},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4296000003814697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40779998898506165},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.3962000012397766}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8238999843597412},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.7770000100135803},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7199000120162964},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6822999715805054},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6288999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5703999996185303},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4514999985694885},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4296000003814697},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40779998898506165},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36469998955726624},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.34279999136924744},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29989999532699585},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.27869999408721924},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.26840001344680786},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3621166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3621166","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2070706591","display_name":null,"funder_award_id":"62332008","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2203804899","display_name":null,"funder_award_id":"62401429","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G565014631","display_name":null,"funder_award_id":"2025M771739","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7966462360","display_name":null,"funder_award_id":"2024M761178","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G8494200937","display_name":null,"funder_award_id":"62173265","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G928427748","display_name":null,"funder_award_id":"JUSRP202501074","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1982075130","https://openalex.org/W2100470808","https://openalex.org/W2887486131","https://openalex.org/W2887522866","https://openalex.org/W2945809413","https://openalex.org/W2963112696","https://openalex.org/W2963163009","https://openalex.org/W2963529609","https://openalex.org/W2995936506","https://openalex.org/W3039479109","https://openalex.org/W3114848016","https://openalex.org/W3159018159","https://openalex.org/W3166092877","https://openalex.org/W3188963955","https://openalex.org/W4285242672","https://openalex.org/W4285244413","https://openalex.org/W4295424676","https://openalex.org/W4307125574","https://openalex.org/W4319265460","https://openalex.org/W4319879007","https://openalex.org/W4367663481","https://openalex.org/W4384284030","https://openalex.org/W4387747474","https://openalex.org/W4391092489","https://openalex.org/W4392607928","https://openalex.org/W4393171245","https://openalex.org/W4401067703","https://openalex.org/W4401246713","https://openalex.org/W4402626612","https://openalex.org/W4403839092","https://openalex.org/W4404521034","https://openalex.org/W4405778882","https://openalex.org/W4406457466","https://openalex.org/W4407375775","https://openalex.org/W4409581147","https://openalex.org/W4409723414","https://openalex.org/W4409917186","https://openalex.org/W4410029423","https://openalex.org/W4410086354","https://openalex.org/W4410294778","https://openalex.org/W4410614273","https://openalex.org/W4412110568"],"related_works":[],"abstract_inverted_index":{"RGB-Thermal":[0],"(RGB-T)":[1],"salient":[2,9,60,107],"object":[3,61],"detection":[4],"aims":[5],"to":[6,25,83,100],"accurately":[7],"locate":[8],"regions":[10],"by":[11],"integrating":[12],"complementary":[13,69,118],"information":[14,54,86],"from":[15],"visible":[16],"and":[17,31,80,88,105,116,127,137,158],"thermal":[18],"modalities.":[19],"However,":[20],"existing":[21,147],"methods":[22],"often":[23],"struggle":[24],"fully":[26],"leverage":[27],"the":[28,35,110],"spatial":[29],"structures":[30],"semantic":[32,89],"differences":[33],"in":[34],"frequency":[36],"domain":[37],"across":[38,160],"modalities,":[39],"while":[40,150],"also":[41],"incurring":[42],"high":[43,152],"computational":[44],"costs.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49,64,92],"propose":[50],"a":[51,67,94,161],"lightweight":[52],"spatial-channel-frequency":[53],"mining":[55,97],"framework":[56],"(SCF-Net)":[57],"for":[58],"RGB-T":[59],"detection.":[62],"Specifically,":[63],"first":[65],"introduce":[66],"local-global":[68],"aggregation":[70],"(LGCA)":[71],"module":[72,99],"that":[73,142],"effectively":[74],"fuses":[75],"local":[76],"textures,":[77],"global":[78],"semantics,":[79],"modality-aware":[81],"features":[82],"enhance":[84],"cross-layer":[85],"consistency":[87],"alignment.":[90],"Furthermore,":[91],"design":[93],"triple":[95],"cue":[96],"(TCM)":[98],"jointly":[101],"explore":[102],"spatial,":[103],"channel,":[104],"frequency-domain":[106],"cues,":[108],"enabling":[109],"extraction":[111],"of":[112,163],"both":[113],"intra-modal":[114],"details":[115],"cross-modal":[117],"information.":[119],"Our":[120],"SCF-Net":[121],"contains":[122],"only":[123],"8.39":[124],"M":[125],"parameters":[126],"supports":[128],"real-time":[129],"inference.":[130],"Extensive":[131],"experiments":[132],"conducted":[133],"on":[134],"VT821,":[135],"VT1000,":[136],"VT5000":[138],"three":[139],"datasets":[140],"demonstrate":[141],"our":[143],"method":[144],"consistently":[145],"outperforms":[146],"mainstream":[148],"models":[149],"maintaining":[151],"operational":[153],"efficiency,":[154],"showing":[155],"strong":[156],"effectiveness":[157],"robustness":[159],"variety":[162],"challenging":[164],"scenarios.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-15T00:00:00"}
