{"id":"https://openalex.org/W4413533468","doi":"https://doi.org/10.1007/s10791-025-09681-4","title":"Improved YOLOv7 algorithm combined with computer vision for detecting dense small objects","display_name":"Improved YOLOv7 algorithm combined with computer vision for detecting dense small objects","publication_year":2025,"publication_date":"2025-08-25","ids":{"openalex":"https://openalex.org/W4413533468","doi":"https://doi.org/10.1007/s10791-025-09681-4"},"language":"en","primary_location":{"id":"doi:10.1007/s10791-025-09681-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09681-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09681-4.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"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":"Discover Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09681-4.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100525305","display_name":"Yun Du","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146584","display_name":"Henan Forestry Vocational College","ror":"https://ror.org/050g87e49","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210146584"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yun Du","raw_affiliation_strings":["Henan Women\u2019s Vocational College, Zhengzhou, 450000, China","Henan Women's Vocational College, Zhengzhou, 450000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Henan Women\u2019s Vocational College, Zhengzhou, 450000, China","institution_ids":["https://openalex.org/I4210146584"]},{"raw_affiliation_string":"Henan Women's Vocational College, Zhengzhou, 450000, China","institution_ids":["https://openalex.org/I4210146584"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100525305"],"corresponding_institution_ids":["https://openalex.org/I4210146584"],"apc_list":null,"apc_paid":null,"fwci":2.6775,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.90841197,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"28","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9817000031471252,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9811999797821045,"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":0.6373209357261658},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.565057098865509},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5386986136436462},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.32622030377388}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6373209357261658},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.565057098865509},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5386986136436462},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.32622030377388}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10791-025-09681-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09681-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09681-4.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"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":"Discover Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ae3567dbd77d47ed96c846ae5b06f726","is_oa":true,"landing_page_url":"https://doaj.org/article/ae3567dbd77d47ed96c846ae5b06f726","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Computing, Vol 28, Iss 1, Pp 1-16 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s10791-025-09681-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09681-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09681-4.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"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":"Discover Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413533468.pdf","grobid_xml":"https://content.openalex.org/works/W4413533468.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W4381855672","https://openalex.org/W4382940191","https://openalex.org/W4386727162","https://openalex.org/W4387499231","https://openalex.org/W4388430363","https://openalex.org/W4388797907","https://openalex.org/W4390344085","https://openalex.org/W4390394489","https://openalex.org/W4390619508","https://openalex.org/W4390947456","https://openalex.org/W4391407087","https://openalex.org/W4391841967","https://openalex.org/W4392807613","https://openalex.org/W4392811632","https://openalex.org/W4404547095","https://openalex.org/W4406273479","https://openalex.org/W4407871004"],"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":{"Dense":[0],"small":[1,51],"object":[2],"detection":[3,22,36,57,74,96],"in":[4,13,33,94],"complex":[5,106],"scenes":[6],"has":[7],"always":[8],"been":[9],"a":[10],"major":[11],"challenge":[12],"the":[14,28,55,61,67,72,86,101,109,118,122,125],"field":[15],"of":[16,30,35,49,59,66,76,92,121],"computer":[17],"vision.":[18],"A":[19],"novel":[20],"fusion":[21],"framework":[23,45],"is":[24],"proposed":[25],"to":[26],"address":[27],"shortcomings":[29],"existing":[31],"methods":[32],"terms":[34],"accuracy,":[37],"computational":[38],"efficiency,":[39],"and":[40,71,127,132,155],"feature":[41,63],"extraction":[42],"capability.":[43],"This":[44],"achieves":[46,89],"accurate":[47],"recognition":[48],"dense":[50],"objects":[52],"by":[53],"integrating":[54],"efficient":[56],"capability":[58],"YOLOv7,":[60],"dynamic":[62],"weighting":[64],"characteristics":[65],"self":[68],"attention":[69],"mechanism,":[70],"multi-scale":[73],"advantages":[75],"Single":[77],"Shot":[78],"Multibox":[79],"Detection":[80],"algorithm.":[81],"Experimental":[82],"verification":[83],"shows":[84],"that":[85],"improved":[87],"model":[88,123],"an":[90],"accuracy":[91,120],"0.94\u20130.96":[93],"vehicle":[95],"tasks,":[97],"significantly":[98],"better":[99],"than":[100],"traditional":[102],"method\u2019s":[103],"0.86\u20130.92.":[104],"In":[105,116],"video":[107],"scenes,":[108],"F1":[110],"score":[111],"remains":[112],"consistently":[113],"above":[114,140],"0.92.":[115],"addition,":[117],"average":[119],"on":[124],"BDD100K":[126],"MSCOCO":[128],"datasets":[129],"reached":[130],"95.96%":[131],"95.23%,":[133],"respectively,":[134],"demonstrating":[135],"excellent":[136],"generalization":[137],"ability.":[138],"The":[139],"research":[141],"results":[142],"provide":[143],"reliable":[144],"technical":[145],"support":[146],"for":[147],"practical":[148],"applications":[149],"such":[150],"as":[151],"drone":[152],"aerial":[153],"photography":[154],"intelligent":[156],"transportation.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
