{"id":"https://openalex.org/W4408199525","doi":"https://doi.org/10.1109/cisp-bmei64163.2024.10906219","title":"Research on Rotated Object Detection Based on YOLOv5s","display_name":"Research on Rotated Object Detection Based on YOLOv5s","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4408199525","doi":"https://doi.org/10.1109/cisp-bmei64163.2024.10906219"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei64163.2024.10906219","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei64163.2024.10906219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100759651","display_name":"Jie Xu","orcid":"https://orcid.org/0000-0001-8238-9344"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Xu","raw_affiliation_strings":["School of Automation and Artificial Intelligence, Nanjing University of Posts and Tel-ecommunications,Jiangsu,Nanjing,China,210023"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation and Artificial Intelligence, Nanjing University of Posts and Tel-ecommunications,Jiangsu,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101001158","display_name":"Yanyun Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyun Cheng","raw_affiliation_strings":["School of Automation and Artificial Intelligence, Nanjing University of Posts and Tel-ecommunications,Jiangsu,Nanjing,China,210023"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation and Artificial Intelligence, Nanjing University of Posts and Tel-ecommunications,Jiangsu,Nanjing,China,210023","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41198531"],"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7253000140190125,"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":0.7253000140190125,"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/computer-science","display_name":"Computer science","score":0.6212427020072937},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5860222578048706},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48655930161476135},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4464702010154724},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.42879974842071533},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.19299864768981934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6212427020072937},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5860222578048706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48655930161476135},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4464702010154724},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.42879974842071533},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.19299864768981934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei64163.2024.10906219","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei64163.2024.10906219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2193145675","https://openalex.org/W2963037989","https://openalex.org/W3140922383","https://openalex.org/W3177105943","https://openalex.org/W4214705290","https://openalex.org/W4324116440","https://openalex.org/W4372347372","https://openalex.org/W4386065355","https://openalex.org/W4391935863","https://openalex.org/W6620707391","https://openalex.org/W6788870397","https://openalex.org/W6796744688","https://openalex.org/W6851391018"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,64,86,97,129,133,138,153,172,186,194],"issues":[3],"of":[4,41,59,91,132,140,145,180,196],"low":[5],"detection":[6,109,142,178,200],"accuracy":[7,179,195],"and":[8,44,56,61,148,150,217],"high":[9,214],"miss":[10,154],"rates":[11],"in":[12,69,111,210],"remote":[13,197],"sensing":[14,198],"object":[15,199],"detection,":[16],"this":[17,36,166],"study":[18],"proposes":[19],"an":[20,73],"improved":[21],"YOLOv5s_CSL":[22,187],"method":[23,37],"integrated":[24],"with":[25,128,158],"attention":[26,80],"mechanisms.":[27],"Firstly,":[28],"by":[29,94],"employing":[30],"phase-shift":[31],"encoding":[32],"for":[33,143,156,207,219],"angle":[34,117,141],"precision,":[35],"uses":[38],"cosine":[39],"functions":[40],"varying":[42,92],"phases":[43],"frequencies":[45],"to":[46,89],"replace":[47],"traditional":[48],"CSL":[49,70],"discontinuous":[50],"encoding,":[51],"thus":[52],"enabling":[53],"automatic":[54],"derivation":[55],"continuous":[57],"optimization":[58],"angles,":[60],"effectively":[62,151],"resolving":[63],"class":[65],"square":[66],"problem":[67],"inherent":[68],"encoding.":[71],"Secondly,":[72],"innovative":[74],"dynamic":[75],"receptive":[76,98],"field":[77],"feature":[78],"fusion":[79],"mechanism":[81],"is":[82],"introduced,":[83],"which":[84],"enhances":[85],"model's":[87],"response":[88],"targets":[90],"scales":[93],"automatically":[95],"adjusting":[96],"field,":[99],"while":[100],"also":[101,202],"facilitating":[102],"effective":[103,205],"cross-space":[104],"information":[105],"transfer,":[106],"significantly":[107,136],"improving":[108],"capabilities":[110],"complex":[112,211],"environments.":[113],"Lastly,":[114],"a":[115,177,182],"new":[116,204],"regression":[118],"loss":[119,127],"function":[120],"was":[121],"designed,":[122],"combining":[123],"mean":[124],"squared":[125],"error":[126],"geometric":[130],"characteristics":[131],"bounding":[134],"boxes,":[135],"enhancing":[137],"flexibility":[139],"objects":[144,157],"various":[146],"shapes":[147],"sizes,":[149],"reducing":[152],"rate":[155],"large":[159],"aspect":[160],"ratios.":[161],"Experimental":[162],"results":[163],"demonstrate":[164],"that":[165],"algorithm":[167],"performed":[168],"exceptionally":[169],"well":[170],"on":[171],"DOTA-v1.0":[173],"test":[174],"set,":[175],"achieving":[176],"77.47%,":[181],"significant":[183],"improvement":[184],"over":[185],"method.":[188],"These":[189],"innovations":[190],"not":[191],"only":[192],"enhance":[193],"but":[201],"provide":[203],"tools":[206],"image":[208],"recognition":[209],"scenarios,":[212],"offering":[213],"application":[215],"value":[216],"prospects":[218],"widespread":[220],"use.":[221]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
