{"id":"https://openalex.org/W4411922407","doi":"https://doi.org/10.1007/s11063-025-11769-3","title":"EA-DETR: Edge-Aware Detection Transformer for Water Surface Floating Object Identification","display_name":"EA-DETR: Edge-Aware Detection Transformer for Water Surface Floating Object Identification","publication_year":2025,"publication_date":"2025-07-02","ids":{"openalex":"https://openalex.org/W4411922407","doi":"https://doi.org/10.1007/s11063-025-11769-3"},"language":"en","primary_location":{"id":"doi:10.1007/s11063-025-11769-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-025-11769-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-025-11769-3.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11063-025-11769-3.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108128673","display_name":"Jiayi Wang","orcid":"https://orcid.org/0009-0002-9109-0285"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayi Wang","raw_affiliation_strings":["School of Mathematics, Hohai University, Nanjing, 210098, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, Hohai University, Nanjing, 210098, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100341795","display_name":"Xiangyang Liu","orcid":"https://orcid.org/0000-0002-2481-3909"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangyang Liu","raw_affiliation_strings":["School of Mathematics, Hohai University, Nanjing, 210098, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, Hohai University, Nanjing, 210098, China","institution_ids":["https://openalex.org/I163340411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100341795"],"corresponding_institution_ids":["https://openalex.org/I163340411"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":1.0916,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.76897684,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"57","issue":"4","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9879999756813049,"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":0.9879999756813049,"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":0.9789999723434448,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9625999927520752,"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/computational-intelligence","display_name":"Computational intelligence","score":0.5085691213607788},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4832092523574829},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43667882680892944},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2779977321624756},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14807423949241638},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.10923847556114197}],"concepts":[{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.5085691213607788},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4832092523574829},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43667882680892944},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2779977321624756},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14807423949241638},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.10923847556114197},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11063-025-11769-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-025-11769-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-025-11769-3.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11063-025-11769-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-025-11769-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-025-11769-3.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411922407.pdf","grobid_xml":"https://content.openalex.org/works/W4411922407.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1676314349","https://openalex.org/W1970507342","https://openalex.org/W1981276685","https://openalex.org/W2017745767","https://openalex.org/W2045997052","https://openalex.org/W2059266708","https://openalex.org/W2080130515","https://openalex.org/W2139577851","https://openalex.org/W2612690371","https://openalex.org/W2734349601","https://openalex.org/W2884585870","https://openalex.org/W2964338223","https://openalex.org/W2973159718","https://openalex.org/W2981689412","https://openalex.org/W2990138404","https://openalex.org/W2997747012","https://openalex.org/W3043960864","https://openalex.org/W3084798277","https://openalex.org/W3096609285","https://openalex.org/W3097065222","https://openalex.org/W3131922516","https://openalex.org/W4362597616","https://openalex.org/W4395112528","https://openalex.org/W4400565393","https://openalex.org/W4402754006","https://openalex.org/W4404327454","https://openalex.org/W4407565868","https://openalex.org/W4408272695","https://openalex.org/W6885167142"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Designed":[0],"to":[1,107],"address":[2],"the":[3,139,157,169,191],"challenge":[4],"of":[5,47,60,117,183],"identifying":[6],"water":[7,188],"surface":[8,189],"floating":[9],"objects":[10,119],"with":[11,154],"transparent":[12,118],"features":[13,57,180],"under":[14],"complex":[15],"backgrounds":[16],"such":[17],"as":[18],"irritating":[19],"light":[20],"sources,":[21],"we":[22],"introduce":[23],"Edge-Aware":[24,43],"Detection":[25],"Transformer(EA-DETR),":[26],"a":[27,84,93,102],"novel":[28],"end-to-end":[29],"object":[30],"detection":[31],"model":[32],"based":[33,96],"on":[34,97,138,156],"DINO-ResNet50.":[35],"Three":[36],"innovative":[37],"strategies":[38],"are":[39],"mainly":[40],"incorporated.":[41],"First,":[42],"Denoising(EAD)":[44],"Module,":[45,51],"composed":[46],"convolutions":[48],"and":[49,101,135,152,165,190],"Squeeze-and-Excitation(SE)":[50],"accentuates":[52],"boundary":[53,179],"information":[54,116],"across":[55],"multi-scale":[56],"after":[58],"Backbone":[59],"ResNet50.":[61],"Secondly,":[62],"Mamba-like":[63],"Linear":[64],"Attention(MLLA)":[65],"Module":[66],"continuously":[67],"suppresses":[68],"incorrect":[69],"positional":[70],"weights":[71],"while":[72],"emphasizing":[73],"correct":[74],"ones":[75],"through":[76],"linear":[77],"operations":[78],"in":[79],"Transformer":[80],"Encoder":[81],"layers.":[82],"Finally,":[83],"dynamic":[85],"matching":[86],"approach":[87],"combines":[88],"flexible":[89],"coefficients":[90],"for":[91,176],"CIoU,":[92],"regularization":[94],"item":[95],"bounding":[98],"box":[99],"sizes":[100],"frame":[103],"utilizing":[104],"Sober":[105],"operator":[106],"convert":[108],"coordinated":[109],"into":[110],"edges,":[111],"thus":[112],"capturing":[113],"more":[114],"detailed":[115],"by":[120,127,187],"calculating":[121],"extra":[122],"segmentation":[123],"mask":[124],"loss":[125],"fostered":[126],"boundaries.":[128],"EA-DETR":[129],"achieves":[130],"72.4%":[131],"AP(0.5:0.95),":[132,162],"98.7%":[133],"AP(0.5)":[134],"65.7%":[136],"AR(0.5:0.95)":[137],"FloW-ImG":[140],"Dataset,":[141],"significantly":[142],"outperforming":[143],"other":[144],"DETR-like":[145],"models.":[146],"Furthermore,":[147],"it":[148],"demonstrates":[149],"ideal":[150],"generalization":[151],"robustness,":[153],"results":[155],"Trash_ICRA19":[158],"Dataset":[159],"exhibiting":[160],"44.1%":[161],"74.2%":[163],"AP(0.5),":[164],"64.5%":[166],"AR(0.5:0.95).":[167],"Overall,":[168],"architecture":[170],"proposed":[171],"can":[172],"provide":[173],"effective":[174],"tools":[175],"learning":[177],"ambiguous":[178],"without":[181],"disturbance":[182],"extreme":[184],"lightness":[185],"caused":[186],"sun.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
