{"id":"https://openalex.org/W7125956505","doi":"https://doi.org/10.1109/smc58881.2025.11342725","title":"WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer","display_name":"WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125956505","doi":"https://doi.org/10.1109/smc58881.2025.11342725"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11342725","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342725","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5124093019","display_name":"Huilin Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huilin Yin","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,Shanghai,China,201804"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,Shanghai,China,201804","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124107593","display_name":"Pengyu Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengyu Wang","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,Shanghai,China,201804"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,Shanghai,China,201804","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124103460","display_name":"Senmao Li","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senmao Li","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,Shanghai,China,201804"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,Shanghai,China,201804","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124069156","display_name":"Jun Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Yan","raw_affiliation_strings":["Tongji University,College of Electronics and Information Engineering,Shanghai,China,201804"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics and Information Engineering,Shanghai,China,201804","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":null,"display_name":"Daniel Watzenig","orcid":null},"institutions":[{"id":"https://openalex.org/I4092182","display_name":"Graz University of Technology","ror":"https://ror.org/00d7xrm67","country_code":"AT","type":"education","lineage":["https://openalex.org/I4092182"]},{"id":"https://openalex.org/I4210112585","display_name":"Virtual Vehicle Research GmbH (Austria)","ror":"https://ror.org/029rc9g37","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210112585"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Daniel Watzenig","raw_affiliation_strings":["Graz University of Technology and the Virtual Vehicle Research,Graz,Austria,8010"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graz University of Technology and the Virtual Vehicle Research,Graz,Austria,8010","institution_ids":["https://openalex.org/I4092182","https://openalex.org/I4210112585"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.2409,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.95415253,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4402","last_page":"4409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5676000118255615,"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":0.5676000118255615,"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/T11622","display_name":"Maritime Navigation and Safety","score":0.13510000705718994,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T12316","display_name":"Oil Spill Detection and Mitigation","score":0.08410000056028366,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6592000126838684},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5889999866485596},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.48179998993873596},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.45980000495910645},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.45350000262260437},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.427700012922287},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4075999855995178},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3889999985694885},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37070000171661377}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7312999963760376},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7228000164031982},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6851000189781189},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6592000126838684},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5889999866485596},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.48179998993873596},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.45980000495910645},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.45350000262260437},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.427700012922287},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.30660000443458557},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.30230000615119934},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.30149999260902405},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.2863999903202057},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11342725","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342725","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/6","score":0.5025869011878967,"display_name":"Clean water and sanitation"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2194775991","https://openalex.org/W2765793020","https://openalex.org/W2989604896","https://openalex.org/W2997242810","https://openalex.org/W3003587986","https://openalex.org/W3006643904","https://openalex.org/W3007565530","https://openalex.org/W3096609285","https://openalex.org/W3159481202","https://openalex.org/W3204554924","https://openalex.org/W4224111766","https://openalex.org/W4312312588","https://openalex.org/W4386065453","https://openalex.org/W4386071560","https://openalex.org/W4388209234","https://openalex.org/W4389385786","https://openalex.org/W4391770072","https://openalex.org/W4392969656","https://openalex.org/W4399665657","https://openalex.org/W4400035744","https://openalex.org/W4402754006","https://openalex.org/W4402979889","https://openalex.org/W4403770406","https://openalex.org/W4405787754"],"related_works":[],"abstract_inverted_index":{"Robust":[0],"object":[1,22,31,89],"detection":[2,23,90],"for":[3,14,100],"Unmanned":[4],"Surface":[5],"Vehicles":[6],"(USVs)":[7],"in":[8,91],"complex":[9],"water":[10,20],"environments":[11],"is":[12,131],"essential":[13],"reliable":[15],"navigation":[16],"and":[17,29,80,103,136,142,165],"operation.":[18],"Specifically,":[19],"surface":[21],"faces":[24],"challenges":[25],"from":[26,43],"blurred":[27],"edges":[28],"diverse":[30],"scales.":[32],"Although":[33],"vision-radar":[34,60],"fusion":[35,61],"offers":[36],"a":[37,58,69,81],"feasible":[38],"solution,":[39],"existing":[40],"approaches":[41],"suffer":[42],"cross-modal":[44,122],"feature":[45],"conflicts,":[46,123],"which":[47],"negatively":[48],"affect":[49],"model":[50,62],"robustness.":[51],"To":[52,119],"address":[53],"this":[54],"problem,":[55],"we":[56,66,95],"propose":[57],"robust":[59],"WS-DETR.":[63],"In":[64],"particular,":[65],"first":[67],"introduce":[68],"Multi-Scale":[70],"Edge":[71],"Information":[72],"Integration":[73],"(MSEII)":[74],"module":[75,130],"to":[76,86,106,133],"enhance":[77],"edge":[78],"perception":[79],"Hierarchical":[82],"Feature":[83,126],"Aggregator":[84],"(HiFA)":[85],"boost":[87],"multi-scale":[88],"the":[92,112,148],"encoder.":[93],"Then,":[94],"adopt":[96],"self-moving":[97],"point":[98,116],"representations":[99],"continuous":[101],"convolution":[102],"residual":[104],"connection":[105],"efficiently":[107],"extract":[108],"irregular":[109,115],"features":[110,138],"under":[111,162],"scenarios":[113],"of":[114],"cloud":[117],"data.":[118],"further":[120],"mitigate":[121],"an":[124],"Adaptive":[125],"Interactive":[127],"Fusion":[128],"(AFIF)":[129],"introduced":[132],"integrate":[134],"visual":[135],"radar":[137],"through":[139],"geometric":[140],"alignment":[141],"semantic":[143],"fusion.":[144],"Extensive":[145],"experiments":[146],"on":[147],"WaterScenes":[149],"dataset":[150],"demonstrate":[151],"that":[152],"WS-DETR":[153],"achieves":[154],"state-of-the-art":[155],"(SOTA)":[156],"performance,":[157],"maintaining":[158],"its":[159],"superiority":[160],"even":[161],"adverse":[163],"weather":[164],"lighting":[166],"conditions.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-01-29T00:00:00"}