{"id":"https://openalex.org/W7105663707","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228534","title":"Lightweight Underwater Sonar Target Detection Algorithm Based on Context Feature Fusion","display_name":"Lightweight Underwater Sonar Target Detection Algorithm Based on Context Feature Fusion","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105663707","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228534"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228534","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228534","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":null,"display_name":"Weijie Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090971","display_name":"Southeast University","ror":"https://ror.org/00cf0ab87","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210090971"]},{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["BD","CN"],"is_corresponding":true,"raw_author_name":"Weijie Zhou","raw_affiliation_strings":["Southeast University,School of Computer Science,NanJing,China,211189"],"affiliations":[{"raw_affiliation_string":"Southeast University,School of Computer Science,NanJing,China,211189","institution_ids":["https://openalex.org/I76569877","https://openalex.org/I4210090971"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nan Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090971","display_name":"Southeast University","ror":"https://ror.org/00cf0ab87","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210090971"]},{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["BD","CN"],"is_corresponding":false,"raw_author_name":"Nan Wei","raw_affiliation_strings":["Southeast University,School of Automation,NanJing,China,211189"],"affiliations":[{"raw_affiliation_string":"Southeast University,School of Automation,NanJing,China,211189","institution_ids":["https://openalex.org/I76569877","https://openalex.org/I4210090971"]}]},{"author_position":"last","author":{"id":null,"display_name":"Longyu Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]},{"id":"https://openalex.org/I4210090971","display_name":"Southeast University","ror":"https://ror.org/00cf0ab87","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210090971"]}],"countries":["BD","CN"],"is_corresponding":false,"raw_author_name":"Longyu Jiang","raw_affiliation_strings":["Southeast University,School of Computer Science,NanJing,China,211189"],"affiliations":[{"raw_affiliation_string":"Southeast University,School of Computer Science,NanJing,China,211189","institution_ids":["https://openalex.org/I76569877","https://openalex.org/I4210090971"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210090971","https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.5774926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5849999785423279,"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":0.5849999785423279,"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/T11698","display_name":"Underwater Acoustics Research","score":0.12359999865293503,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.12210000306367874,"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/sonar","display_name":"Sonar","score":0.8068000078201294},{"id":"https://openalex.org/keywords/underwater","display_name":"Underwater","score":0.703000009059906},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6836000084877014},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6582000255584717},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5741000175476074},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5622000098228455},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43720000982284546},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.3919000029563904},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3781999945640564}],"concepts":[{"id":"https://openalex.org/C555745239","wikidata":"https://www.wikidata.org/wiki/Q133220","display_name":"Sonar","level":2,"score":0.8068000078201294},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.703000009059906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6984000205993652},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6836000084877014},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6582000255584717},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5741000175476074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5716000199317932},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5622000098228455},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5493999719619751},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C181255713","wikidata":"https://www.wikidata.org/wiki/Q7662740","display_name":"Synthetic aperture sonar","level":3,"score":0.336899995803833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3319999873638153},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C2987395694","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Feature tracking","level":3,"score":0.299699991941452},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.28839999437332153},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.2849000096321106},{"id":"https://openalex.org/C145424490","wikidata":"https://www.wikidata.org/wiki/Q618465","display_name":"Remotely operated underwater vehicle","level":4,"score":0.28450000286102295},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.263700008392334},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2615000009536743},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228534","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228534","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","score":0.7866445779800415,"display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2102605133","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2342423581","https://openalex.org/W2565639579","https://openalex.org/W2752782242","https://openalex.org/W2791767745","https://openalex.org/W2963125010","https://openalex.org/W2963150697","https://openalex.org/W2963857746","https://openalex.org/W2966271978","https://openalex.org/W3035414587","https://openalex.org/W3047190427","https://openalex.org/W3096609285","https://openalex.org/W3167976421","https://openalex.org/W3197086755","https://openalex.org/W3207637583","https://openalex.org/W4289752563","https://openalex.org/W4312580481","https://openalex.org/W4319456229","https://openalex.org/W4386076325","https://openalex.org/W4386076493","https://openalex.org/W4386076539","https://openalex.org/W4391307079","https://openalex.org/W4402218136","https://openalex.org/W4402703046","https://openalex.org/W4402754006","https://openalex.org/W4403770406","https://openalex.org/W4404199198"],"related_works":[],"abstract_inverted_index":{"Underwater":[0],"sonar":[1,119,209],"image":[2],"target":[3,41,210],"detection":[4,26,137,163,178,220],"is":[5,79],"essential":[6],"for":[7,81],"applications":[8],"such":[9],"as":[10,203],"marine":[11],"resource":[12],"exploration,":[13],"military":[14],"defense,":[15],"and":[16,28,39,63,88,107,170,194,219],"underwater":[17,141,208],"archaeology.":[18],"However,":[19],"current":[20],"methods":[21],"face":[22],"difficulties":[23],"in":[24,117,139,207],"balancing":[25],"accuracy":[27,62],"computational":[29,92,217],"efficiency":[30,64,218],"due":[31],"to":[32,131,157],"challenges":[33],"like":[34],"low":[35],"resolution,":[36],"severe":[37],"noise,":[38],"ambiguous":[40],"boundaries.":[42],"To":[43],"address":[44],"these":[45],"issues,":[46],"we":[47],"propose":[48],"a":[49,126,159,204],"lightweight":[50],"algorithm,":[51],"Lightweight":[52],"Sonar":[53],"Detection":[54],"Transformer":[55],"(LS-DETR),":[56],"which":[57],"advances":[58],"the":[59,76,100,111,122,147,151,183],"trade-off":[60],"between":[61,216],"through":[65],"innovative":[66],"module":[67,78,102,124],"designs.":[68],"LS-DETR":[69,165,181,202],"consists":[70],"of":[71,113,154],"three":[72],"core":[73],"modules:":[74],"Firstly,":[75],"RGLFN":[77],"designed":[80],"efficient":[82],"feature":[83,97,105,115],"extraction":[84,116],"by":[85,168,173,192,197],"leveraging":[86],"Ghost":[87],"RepConv":[89],"mechanisms,":[90],"reducing":[91],"complexity":[93],"while":[94,175],"maintaining":[95,176],"high-quality":[96],"representations.":[98],"Second,":[99],"CAMFPN":[101],"enhances":[103],"multi-scale":[104],"fusion":[106],"contextual":[108],"modeling,":[109],"addressing":[110],"challenge":[112],"robust":[114],"high-noise":[118],"images.":[120],"Third,":[121],"DPBAttention":[123],"introduces":[125],"dynamic":[127],"position":[128],"bias":[129],"mechanism":[130],"model":[132],"spatial":[133],"relationships,":[134],"significantly":[135],"improving":[136],"robustness":[138],"complex":[140],"environments.":[142],"Extensive":[143],"experiments":[144],"conducted":[145],"on":[146],"LASA":[148],"dataset":[149],"demonstrate":[150],"superior":[152],"performance":[153],"LS-DETR.":[155],"Compared":[156],"RT-DETR,":[158],"state-of-the-art":[160],"real-time":[161],"object":[162],"model,":[164,187],"reduces":[166],"GFLOPs":[167],"36%":[169],"parameter":[171],"count":[172],"28%,":[174],"comparable":[177],"accuracy.":[179,221],"Furthermore,":[180],"outperforms":[182],"latest":[184],"YOLO":[185],"series":[186],"YOLOv11,":[188],"with":[189],"mAP@50":[190],"increasing":[191,196],"5.4%":[193],"mAP@50-95":[195],"10.7%.":[198],"These":[199],"results":[200],"establish":[201],"new":[205],"benchmark":[206],"detection,":[211],"achieving":[212],"an":[213],"optimal":[214],"balance":[215]},"counts_by_year":[],"updated_date":"2025-11-15T23:13:30.683059","created_date":"2025-11-14T00:00:00"}
