{"id":"https://openalex.org/W7147526153","doi":"https://doi.org/10.1109/cnml68938.2026.11452367","title":"Research on Decision-Level Fusion Algorithm for Unmanned Surface Vehicle Object Detection","display_name":"Research on Decision-Level Fusion Algorithm for Unmanned Surface Vehicle Object Detection","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147526153","doi":"https://doi.org/10.1109/cnml68938.2026.11452367"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452367","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452367","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5132582370","display_name":"Jianjun Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]},{"id":"https://openalex.org/I48780066","display_name":"Shenyang University of Chemical Technology","ror":"https://ror.org/03dbpdh75","country_code":"CN","type":"education","lineage":["https://openalex.org/I48780066"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianjun Guo","raw_affiliation_strings":["Shenyang University of Technology,Guangzhou Institute of Industrial Intelligence,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang University of Technology,Guangzhou Institute of Industrial Intelligence,Shenyang,China","institution_ids":["https://openalex.org/I157507598","https://openalex.org/I48780066"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110815878","display_name":"Jinchao Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142539","display_name":"Guangdong Institute of Intelligent Manufacturing","ror":"https://ror.org/049jpjz09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210142539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinchao Xiao","raw_affiliation_strings":["Guangzhou Institute of Industrial Intelligence,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"Guangzhou Institute of Industrial Intelligence,Guangzhou,China","institution_ids":["https://openalex.org/I4210142539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132648746","display_name":"Zhijia Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijia Zhang","raw_affiliation_strings":["Shenyang University of Technology,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang University of Technology,Shenyang,China","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102754396","display_name":"Jihai Liu","orcid":"https://orcid.org/0009-0003-2714-8750"},"institutions":[{"id":"https://openalex.org/I4210142539","display_name":"Guangdong Institute of Intelligent Manufacturing","ror":"https://ror.org/049jpjz09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210142539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jihai Liu","raw_affiliation_strings":["Guangzhou Institute of Industrial Intelligence,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"Guangzhou Institute of Industrial Intelligence,Guangzhou,China","institution_ids":["https://openalex.org/I4210142539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5132582370"],"corresponding_institution_ids":["https://openalex.org/I157507598","https://openalex.org/I48780066"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.86850095,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"680","last_page":"683"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.710099995136261,"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":0.710099995136261,"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/T12316","display_name":"Oil Spill Detection and Mitigation","score":0.06469999998807907,"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"}},{"id":"https://openalex.org/T11622","display_name":"Maritime Navigation and Safety","score":0.05649999901652336,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6585000157356262},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6358000040054321},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.578000009059906},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4993000030517578},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4977000057697296},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4392000138759613},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.42660000920295715},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.41290000081062317},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.3903000056743622}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6585000157356262},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6358000040054321},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6240000128746033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.613099992275238},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5975000262260437},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.578000009059906},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4977000057697296},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4392000138759613},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.41290000081062317},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40700000524520874},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.37709999084472656},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452367","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452367","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2883780447","https://openalex.org/W2897529137","https://openalex.org/W2968296999","https://openalex.org/W2982083293","https://openalex.org/W3167095230","https://openalex.org/W4386050785","https://openalex.org/W4386076539","https://openalex.org/W4413944783"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,22,53,99,151,155,161,172,198],"increasing":[2],"use":[3],"of":[4,26,106,112,118,124],"unmanned":[5],"surface":[6,33],"vehicles":[7],"(USVs)":[8],"in":[9,30],"waterborne":[10],"inspection,":[11],"channel":[12,72],"monitoring,":[13],"and":[14,24,41,57,81,91,95,108,120,143,164,180],"port":[15],"security,":[16],"higher":[17],"demands":[18],"are":[19,62,93,205],"placed":[20],"on":[21],"accuracy":[23],"robustness":[25],"environmental":[27],"perception.":[28,45],"However,":[29],"complex":[31],"water":[32],"environments,":[34],"single":[35],"sensors":[36],"suffer":[37],"from":[38,171],"inherent":[39],"limitations":[40],"cannot":[42],"provide":[43],"reliable":[44],"In":[46],"this":[47],"work,":[48],"YOLOv5s":[49],"is":[50,126,138,158,169,183,195],"adopted":[51],"as":[52],"image-based":[54],"baseline":[55],"detector,":[56],"four":[58],"lightweight":[59,122],"backbone":[60],"networks":[61],"evaluated.":[63],"The":[64,175,201],"results":[65,204],"show":[66],"that":[67],"EfficientViT":[68],"with":[69,191],"a":[70,75,103,109,121,134,165,187],"20%":[71],"reduction":[73],"achieves":[74,98],"good":[76],"balance":[77],"between":[78,177],"inference":[79],"speed":[80],"model":[82],"size.":[83],"For":[84,147],"point":[85,144,152],"cloud":[86,145,153],"object":[87],"detection,":[88,150],"SECOND,":[89],"PointPillars,":[90],"CenterPoint":[92,97,125],"trained":[94],"compared.":[96],"best":[100],"performance,":[101],"reaching":[102],"BEV":[104],"AP":[105,111],"87.7%":[107],"3D":[110,149],"78.6%":[113],"at":[114],"an":[115],"IoU":[116,176],"threshold":[117],"0.5,":[119],"version":[123],"further":[127],"designed":[128],"to":[129,140,185],"reduce":[130],"computational":[131],"cost.":[132],"Finally,":[133],"decision-level":[135],"fusion":[136],"algorithm":[137],"proposed":[139],"integrate":[141],"image":[142,162,181],"detections.":[146],"each":[148],"inside":[154],"bounding":[156,167],"box":[157,168],"projected":[159,173],"onto":[160],"plane,":[163],"2D":[166],"generated":[170],"points.":[174],"these":[178],"boxes":[179],"detections":[182],"used":[184],"construct":[186],"cost":[188],"matrix":[189],"together":[190],"confidence":[192],"scores,":[193],"which":[194],"solved":[196],"by":[197],"Hungarian":[199],"algorithm.":[200],"final":[202],"fused":[203],"obtained":[206],"through":[207],"confidence-weighted":[208],"fusion.":[209]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-04-02T00:00:00"}
