{"id":"https://openalex.org/W6892978463","doi":"https://doi.org/10.5281/zenodo.13692547","title":"SWDD: Sonar Wall Detection Dataset","display_name":"SWDD: Sonar Wall Detection Dataset","publication_year":2024,"publication_date":"2024-09-05","ids":{"openalex":"https://openalex.org/W6892978463","doi":"https://doi.org/10.5281/zenodo.13692547"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.13692547","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.13692547","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.13692547","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Aubard, Martin","orcid":"https://orcid.org/0009-0000-3070-8067"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aubard, Martin","raw_affiliation_strings":["OceanScan Marine Systems & Technology"],"raw_orcid":"https://orcid.org/0009-0000-3070-8067","affiliations":[{"raw_affiliation_string":"OceanScan Marine Systems & Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Antal, L\u00e1szl\u00f3","orcid":"https://orcid.org/0009-0005-4977-0959"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Antal, L\u00e1szl\u00f3","raw_affiliation_strings":["RWTH Aachen University"],"raw_orcid":"https://orcid.org/0009-0005-4977-0959","affiliations":[{"raw_affiliation_string":"RWTH Aachen University","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Madureira, Maria","orcid":"https://orcid.org/0000-0002-0264-4710"},"institutions":[{"id":"https://openalex.org/I4210137189","display_name":"Center for Interdisciplinary Studies","ror":"https://ror.org/03whr7s66","country_code":"IN","type":"facility","lineage":["https://openalex.org/I4210137189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Madureira, Maria","raw_affiliation_strings":["Interdisciplinary Studies Research Center (ISRC), ISEP/IPP"],"raw_orcid":"https://orcid.org/0000-0002-0264-4710","affiliations":[{"raw_affiliation_string":"Interdisciplinary Studies Research Center (ISRC), ISEP/IPP","institution_ids":["https://openalex.org/I4210137189"]}]},{"author_position":"middle","author":{"id":null,"display_name":"F. Teixeira, Luis","orcid":"https://orcid.org/0000-0002-4050-7880"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"F. Teixeira, Luis","raw_affiliation_strings":["Universidade do Porto"],"raw_orcid":"https://orcid.org/0000-0002-4050-7880","affiliations":[{"raw_affiliation_string":"Universidade do Porto","institution_ids":["https://openalex.org/I182534213"]}]},{"author_position":"last","author":{"id":null,"display_name":"\u00c1brah\u00e1m, Erika","orcid":"https://orcid.org/0000-0002-5647-6134"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"\u00c1brah\u00e1m, Erika","raw_affiliation_strings":["RWTH Aachen University"],"raw_orcid":"https://orcid.org/0000-0002-5647-6134","affiliations":[{"raw_affiliation_string":"RWTH Aachen University","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/sonar","display_name":"Sonar","score":0.9013000130653381},{"id":"https://openalex.org/keywords/side-scan-sonar","display_name":"Side-scan sonar","score":0.8939999938011169},{"id":"https://openalex.org/keywords/synthetic-aperture-sonar","display_name":"Synthetic aperture sonar","score":0.5806999802589417},{"id":"https://openalex.org/keywords/underwater","display_name":"Underwater","score":0.48730000853538513},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4758000075817108},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4717000126838684},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.41769999265670776},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.41519999504089355},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.3693999946117401}],"concepts":[{"id":"https://openalex.org/C555745239","wikidata":"https://www.wikidata.org/wiki/Q133220","display_name":"Sonar","level":2,"score":0.9013000130653381},{"id":"https://openalex.org/C2776355146","wikidata":"https://www.wikidata.org/wiki/Q357527","display_name":"Side-scan sonar","level":3,"score":0.8939999938011169},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6187000274658203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5814999938011169},{"id":"https://openalex.org/C181255713","wikidata":"https://www.wikidata.org/wiki/Q7662740","display_name":"Synthetic aperture sonar","level":3,"score":0.5806999802589417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5672000050544739},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.48730000853538513},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4758000075817108},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C137184094","wikidata":"https://www.wikidata.org/wiki/Q6764300","display_name":"Marine mammals and sonar","level":3,"score":0.3643999993801117},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3499000072479248},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.328900009393692},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.257999986410141},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.13692547","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.13692547","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.5281/zenodo.13692547","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.13692547","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1563635349","display_name":"Reliable AI for Marine Robotics","funder_award_id":"956200","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,140],"repository":[1,330],"contains":[2],"three":[3,473,547,687],"side":[4,92,456],"scan":[5,93,457],"sonar":[6,94,458,554,566],"datasets:":[7,689],"SWDD:":[8],"Sonar":[9,26,47,68,419,670],"Wall":[10],"Detection":[11],"Dataset,":[12],"which":[13,483,771],"is":[14,30,51,231,282,363,378,437,576,626,697,761,783],"part":[15,35,56],"of":[16,36,57,123,128,134,145,205,259,394,408,572,642,787],"the":[17,37,58,102,108,158,192,198,216,219,226,254,262,285,298,360,373,392,395,409,424,450,453,461,467,487,491,495,500,504,517,525,530,541,573,579,621,640,643,655,660,682,686,736,746,756,767,785,788],"paper":[18,38,59,410,436,661],"Knowledge":[19,354],"Distillation":[20,355],"in":[21,101,275,356,460,533,540,578,659,700,723,741],"YOLOX-ViT":[22,291,357],"for":[23,44,65,130,235,238,242,310,416,667,728],"Side":[24],"Scan":[25],"Object":[27,48,69,420,671],"Detection.":[28,49,70],"SWDD-Validation":[29,442,471,544,574,625],"an":[31,332],"extension":[32],"version":[33,54],"and":[34,55,87,176,184,240,290,295,340,404,478,513,516,553,567,678,692,705,720,725,730,782],"ROSAR:":[39,60,411,662],"An":[40,61,412,663],"Adversarial":[41,62,413,664],"Re-Training":[42,63,414,665],"Framework":[43,64,415,666],"Robust":[45,66,417,668],"Side-Scan":[46,67,418,669],"SWDD-Adversarial":[50,647,649,760],"another":[52,306],"extended":[53],"SWDD":[71,73,299,361,377,444,493,683,769],"Dataset":[72,582],"has":[74,98,222,446,651],"been":[75,99,168,189,223,265,293,319,447,652],"recorded":[76,448],"with":[77,125,171,213,225,250,321,347,383,452,631],"a":[78,89,120,126,131,143,203,257,335,379,384,510,627,632,762,775],"lightweight":[79],"autonomous":[80],"underwater":[81],"vehicle":[82],"(LAUV),":[83],"operated":[84,118],"by":[85,389,537,637,654,674,745,780],"OceanScan-MST":[86],"carrying":[88],"Klein":[90,454],"3500":[91,455],"(SSS).":[95],"The":[96,116,150,164,229,244,280,353,435,443,470,480,543,570,648,694],"AUV":[97],"deployed":[100,459],"Porto":[103,462],"de":[104,463],"Leix\u00f5es":[105,464],"harbor":[106,465],"following":[107,284,374,401,466,580,622,688,747,757],"harbor's":[109,468],"walls":[110],"while":[111,499],"collecting":[112],"SSS":[113,117,333,338,526],"raw":[114,159],"data.":[115],"at":[119,366],"high":[121],"frequency":[122],"900kHz":[124],"range":[127,133],"75m":[129],"total":[132,204],"150m":[135],"from":[136,305,344,486,709,715,735],"port":[137],"to":[138,194,215,247,267,271,423,557],"starboard.":[139],"setup":[141],"produced":[142],"resolution":[144,258],"4.168":[146],"pixels":[147],"per":[148],"line.":[149],"data":[151,160,193,217,274,485,534],"were":[152],"processed":[153],"using":[154,297,359,449,774],"Neptus":[155],"software,":[156],"transforming":[157],"into":[161,233],"waterfall":[162],"images.":[163,196,228,542],"216":[165],"images":[166,249,255,263,727,743],"have":[167,188,256,264,292,318],"manually":[169,323,349],"annotated":[170,283,324,350],"two":[172,400],"different":[173,551,565,716],"classes,":[174],"wall":[175,512],"noWall.":[177],"Data":[178],"augmentation":[179],"such":[180],"as":[181,490],"noise,":[182],"flips,":[183],"combined":[185],"noise-flip":[186],"transformations":[187],"applied,":[190],"increasing":[191],"864":[195,199],"Across":[197],"images,":[200],"there":[201],"are":[202,406],"2,616":[206],"labeled":[207],"samples.":[208],"To":[209],"ensure":[210],"robust":[211],"training,":[212,236],"respect":[214],"quality,":[218],"original":[220,492,768],"dataset":[221,230,281,329,362,381,445,545,575,629,650,733,764],"mixed":[224],"augmented":[227],"divided":[232],"70%":[234],"15%":[237,241],"validation,":[239],"testing.":[243],"authors":[245],"chose":[246],"generate":[248],"500":[251,599,607,615],"lines,":[252],"meaning":[253],"4.168x500.":[260],"Finally,":[261],"resized":[266],"640":[268,270],"\u00d7":[269],"use":[272],"this":[273,314,328,345],"specific":[276],"computer":[277],"vision":[278],"algorithms.":[279],"COCO":[286],"annotation":[287],"format.":[288],"YOLOX":[289],"trained":[294],"compared":[296],"dataset.":[300],"A":[301],"6-minute":[302,336],"57-second":[303,337],"video":[304,346],"survey":[307],"was":[308,502,772],"used":[309],"model":[311],"comparison.":[312],"From":[313],"video,":[315,339],"6243":[316,341],"frames":[317,343],"extracted":[320,342],"its":[322,348],"ground":[325,351],"truth.":[326,352],"Thus,":[327],"offers":[331],"dataset,":[334,482,497,519,684,770],"code":[358],"publicly":[364,698],"available":[365,699],"KD-YOLOX-ViT.":[367],"For":[368,616,751],"(re-)using/publishing":[369,617,752],"SWDD-Validation,":[370,618],"please":[371,619,754],"include":[372,620,755],"copyright":[375,623,758],"text:":[376,624,759],"public":[380,628,763],"collected":[382,520,549,630,773],"Light":[385,633,776],"Autonomous":[386,634,777],"Underwater":[387,635,778],"Vehicle":[388,636,779],"Oceanscan-MST,":[390,638],"within":[391,639,784],"scope":[393,641,786],"H2020":[396,644,789],"REMARO":[397,645,790],"project.":[398,646,791],"\u26a0\ufe0fThe":[399],"datasets":[402,548,707],"(SWDD-Validation":[403],"SWDD-Adversarial)":[405],"parts":[407],"Detection,":[421,672],"submitted":[422],"2025":[425],"IEEE":[426],"Symposium":[427],"on":[428,503,681,766],"Maritime":[429],"Informatics":[430],"&":[431],"Robotics":[432],"(MARIS":[433],"2025).":[434],"still":[438],"under":[439,521,550,564],"review.":[440],"\u26a0\ufe0f":[441],"LAUV":[451,501],"walls.":[469],"comports":[472],"field-collected":[474],"detected:":[475],"SWDD-Clean,":[476],"SWDD-Surface,":[477],"SWDD-Noisy.":[479],"SWDD-Clean":[481,592],"includes":[484],"same":[488],"mission":[489],"dataset;":[494],"SWDD-Surface":[496,600],"captured":[498],"surface":[505],"during":[506],"windy":[507],"weather,":[508],"featuring":[509],"non-straight":[511],"wave-induced":[514],"variations;":[515],"SWDD-Noisy":[518,608],"stormy":[522],"conditions,":[523],"where":[524],"transducer":[527],"intermittently":[528],"exited":[529],"water,":[531],"resulting":[532,714,722,740],"loss":[535],"represented":[536],"black":[538],"lines":[539],"provides":[546],"weather":[552],"setups":[555],"aiming":[556],"study":[558],"object":[559],"detection":[560],"models'":[561],"robustness":[562],"variation":[563],"noise":[568],"conditions.":[569],"metadata":[571],"depicted":[577],"table.":[581],"#":[583,585],"Image":[584],"Bbox":[586],"Freq.":[587],"(kHz)":[588],"Range":[589],"(m)":[590],"Resolution":[591],"148":[593],"248":[594],"900":[595,603],"50":[596],"4168":[597,613],"x":[598,606,614],"98":[601],"153":[602],"75":[604],"6552":[605],"551":[609],"800":[610],"455":[611],"100":[612],"generated":[653],"ROSAR":[656,695],"framework":[657,696],"proposed":[658],"which,":[673],"leveraging":[675],"adversarial":[676,710,737],"PGD":[677,711],"Patch":[679,749],"attack":[680],"generates":[685],"P1-SWDD,":[690],"P2-SWDD,":[691],"Patch-SWDD.":[693],"our":[701],"GitHub":[702],"[repository].":[703],"P1":[704,729],"P2-SWDD":[706],"result":[708],"attacks,":[712],"each":[713],"safety":[717],"properties":[718],"(P1":[719],"P2),":[721],"1017":[724],"1462":[726],"P2.":[731],"Patch-SWDD":[732],"results":[734],"patch":[738],"attack,":[739],"151":[742],"provided":[744],"repository:":[748],"Attack.":[750],"SWDD-Adversarial,":[753],"based":[765],"Oceanscan-MST":[781]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}