{"id":"https://openalex.org/W6940561474","doi":"https://doi.org/10.1093/comjnl/bxaf025","title":"A Yolo-like lightweight ship detection network for unmanned surface vehicles","display_name":"A Yolo-like lightweight ship detection network for unmanned surface vehicles","publication_year":2025,"publication_date":"2025-04-01","ids":{"openalex":"https://openalex.org/W6940561474","doi":"https://doi.org/10.1093/comjnl/bxaf025"},"language":"en","primary_location":{"id":"doi:10.1093/comjnl/bxaf025","is_oa":false,"landing_page_url":"https://doi.org/10.1093/comjnl/bxaf025","pdf_url":null,"source":{"id":"https://openalex.org/S44643521","display_name":"The Computer Journal","issn_l":"0010-4620","issn":["0010-4620","1460-2067"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Computer Journal","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":null,"display_name":"Weina Zhou","orcid":"https://orcid.org/0000-0002-9322-3734"},"institutions":[{"id":"https://openalex.org/I96733725","display_name":"Shanghai Maritime University","ror":"https://ror.org/04z7qrj66","country_code":"CN","type":"education","lineage":["https://openalex.org/I96733725"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weina Zhou","raw_affiliation_strings":["School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,"],"raw_orcid":"https://orcid.org/0000-0002-9322-3734","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,","institution_ids":["https://openalex.org/I96733725"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wei Shao","orcid":"https://orcid.org/0009-0005-4737-8371"},"institutions":[{"id":"https://openalex.org/I96733725","display_name":"Shanghai Maritime University","ror":"https://ror.org/04z7qrj66","country_code":"CN","type":"education","lineage":["https://openalex.org/I96733725"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Shao","raw_affiliation_strings":["School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,"],"raw_orcid":"https://orcid.org/0009-0005-4737-8371","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,","institution_ids":["https://openalex.org/I96733725"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wenhua Hu","orcid":"https://orcid.org/0009-0003-0120-7169"},"institutions":[{"id":"https://openalex.org/I96733725","display_name":"Shanghai Maritime University","ror":"https://ror.org/04z7qrj66","country_code":"CN","type":"education","lineage":["https://openalex.org/I96733725"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhua Hu","raw_affiliation_strings":["School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,"],"raw_orcid":"https://orcid.org/0009-0003-0120-7169","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,","institution_ids":["https://openalex.org/I96733725"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2635,"currency":"GBP","value_usd":3232},"apc_paid":null,"fwci":3.7051,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.93403235,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"68","issue":"9","first_page":"1118","last_page":"1127"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10451","display_name":"Mycorrhizal Fungi and Plant Interactions","score":0.5612999796867371,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10451","display_name":"Mycorrhizal Fungi and Plant Interactions","score":0.5612999796867371,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.12950000166893005,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10825","display_name":"Plant Pathogens and Fungal Diseases","score":0.03750000149011612,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.676800012588501},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5547000169754028},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5526999831199646},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.4864000082015991},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.43389999866485596},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3977000117301941},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3828999996185303},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.36660000681877136}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8156999945640564},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.676800012588501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5853000283241272},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5547000169754028},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5526999831199646},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.43389999866485596},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39579999446868896},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.36660000681877136},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.34860000014305115},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3163999915122986},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1093/comjnl/bxaf025","is_oa":false,"landing_page_url":"https://doi.org/10.1093/comjnl/bxaf025","pdf_url":null,"source":{"id":"https://openalex.org/S44643521","display_name":"The Computer Journal","issn_l":"0010-4620","issn":["0010-4620","1460-2067"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Computer Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.49677416682243347,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2194775991","https://openalex.org/W2552331162","https://openalex.org/W2966926453","https://openalex.org/W3035414587","https://openalex.org/W3203188564","https://openalex.org/W4220813865","https://openalex.org/W4281940007","https://openalex.org/W4308633807","https://openalex.org/W4312467624","https://openalex.org/W4385158938","https://openalex.org/W4385422915","https://openalex.org/W4385493311","https://openalex.org/W4386076325","https://openalex.org/W4389430576","https://openalex.org/W4391263430","https://openalex.org/W4392471396","https://openalex.org/W6628973269","https://openalex.org/W6731892127","https://openalex.org/W6736170873","https://openalex.org/W6740474667","https://openalex.org/W6750227808","https://openalex.org/W6753441378","https://openalex.org/W6777046832","https://openalex.org/W6800717992","https://openalex.org/W6850929176","https://openalex.org/W6856205870","https://openalex.org/W6856980963"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"The":[1],"widespread":[2],"application":[3],"of":[4,31,78,84,104,179],"unmanned":[5],"surface":[6],"vehicles":[7],"(USVs)":[8],"in":[9,50,134],"maritime":[10],"surveillance":[11],"has":[12],"highlighted":[13],"the":[14,28,65,76,85,88,96,101,105,136,142,164,177,180,197],"need":[15],"for":[16,48,156],"improved":[17],"ship":[18,42],"detection":[19,43,191],"models.":[20],"However,":[21],"hardware":[22],"limitations":[23],"and":[24,75,110,114,183,193,205],"environmental":[25],"interference":[26],"impact":[27],"perception":[29],"capability":[30,165],"USVs.":[32],"In":[33,145],"order":[34,146],"to":[35,64,67,81,130,147,166],"address":[36],"these":[37,123],"challenges,":[38],"YoloS,":[39],"a":[40,54],"lightweight":[41],"network,":[44],"is":[45,61,93],"specifically":[46],"designed":[47],"USVs":[49],"complex":[51],"backgrounds.":[52],"Firstly,":[53],"network":[55,150],"component":[56],"Split":[57],"Widely":[58],"Network":[59,91],"(SWNet)":[60],"put":[62],"forward":[63],"backbone":[66],"reduce":[68],"its":[69],"redundancy.":[70],"SWNet":[71],"leverages":[72],"inter-channel":[73],"interaction":[74],"idea":[77],"group":[79],"convolution":[80],"minimize":[82],"redundancy":[83],"network.":[86,98],"Secondly,":[87],"Small":[89],"Pyramid":[90],"(SPN)":[92],"introduced":[94],"as":[95],"neck":[97],"SPN":[99,120],"enhances":[100],"spatial":[102,128],"information":[103,129],"target":[106,118,143],"regions":[107],"by":[108],"capturing":[109],"highlighting":[111],"significant":[112],"contour":[113],"texture":[115],"details":[116,140],"within":[117,141],"regions.":[119,144],"also":[121],"utilizes":[122],"low-level":[124,157],"features":[125,133,158],"with":[126,200],"enhanced":[127],"guide":[131],"high-level":[132],"identifying":[135],"most":[137],"discriminative":[138],"fine-grained":[139],"further":[148],"achieve":[149,189],"lightweighting,":[151],"only":[152,201],"two":[153],"output":[154],"heads":[155],"are":[159],"retained":[160],"while":[161],"still":[162],"preserving":[163],"extract":[167],"task-critical":[168],"features.":[169],"Extensive":[170],"experiments":[171],"on":[172,196],"different":[173],"datasets":[174],"have":[175],"verified":[176],"effectiveness":[178],"proposed":[181],"method,":[182],"it":[184],"shows":[185],"that":[186],"YoloS":[187],"can":[188],"96.5$\\%$":[190],"accuracy":[192],"80.1":[194],"fps":[195],"SMD":[198],"dataset":[199],"2.78M":[202],"model":[203],"parameters":[204],"10.4G":[206],"floating":[207],"point":[208],"operations.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}