{"id":"https://openalex.org/W7147189270","doi":"https://doi.org/10.1109/cw68232.2025.00035","title":"YOLOv11-TinyED: An Edge-Optimized Lightweight Framework for Sporopollen Detection in the Qinghai-Tibet Plateau","display_name":"YOLOv11-TinyED: An Edge-Optimized Lightweight Framework for Sporopollen Detection in the Qinghai-Tibet Plateau","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W7147189270","doi":"https://doi.org/10.1109/cw68232.2025.00035"},"language":null,"primary_location":{"id":"doi:10.1109/cw68232.2025.00035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cw68232.2025.00035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Cyberworlds (CW\uff09","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/A5132545506","display_name":"Fubo Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I20616075","display_name":"Qinghai Normal University","ror":"https://ror.org/03az1t892","country_code":"CN","type":"education","lineage":["https://openalex.org/I20616075"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fubo Wang","raw_affiliation_strings":["School of Computer Science, Qinghai Normal University,Xining,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Qinghai Normal University,Xining,China","institution_ids":["https://openalex.org/I20616075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020749269","display_name":"Shengling Geng","orcid":"https://orcid.org/0000-0002-9897-3147"},"institutions":[{"id":"https://openalex.org/I20616075","display_name":"Qinghai Normal University","ror":"https://ror.org/03az1t892","country_code":"CN","type":"education","lineage":["https://openalex.org/I20616075"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengling Geng","raw_affiliation_strings":["Academy of Plateau Science and Sustainability, Qinghai Normal University, The State Key Laboratory of Tibetan Intelligence,Xining,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academy of Plateau Science and Sustainability, Qinghai Normal University, The State Key Laboratory of Tibetan Intelligence,Xining,China","institution_ids":["https://openalex.org/I20616075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046255943","display_name":"Mingcong Dang","orcid":null},"institutions":[{"id":"https://openalex.org/I20616075","display_name":"Qinghai Normal University","ror":"https://ror.org/03az1t892","country_code":"CN","type":"education","lineage":["https://openalex.org/I20616075"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingcong Dang","raw_affiliation_strings":["School of Computer, Science Qinghai Normal University,Xining,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, Science Qinghai Normal University,Xining,China","institution_ids":["https://openalex.org/I20616075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20616075"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81008371,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.16060000658035278,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.16060000658035278,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13905","display_name":"Telecommunications and Broadcasting Technologies","score":0.14429999887943268,"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.12759999930858612,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6255000233650208},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5339999794960022},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4794999957084656},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.47929999232292175},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44290000200271606},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.40450000762939453},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.3790000081062317},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.37599998712539673}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6255000233650208},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6225000023841858},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5424000024795532},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4794999957084656},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.47929999232292175},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44290000200271606},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.391400009393692},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3824000060558319},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3790000081062317},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.37599998712539673},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C2780312720","wikidata":"https://www.wikidata.org/wiki/Q5689100","display_name":"Head (geology)","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.304500013589859},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3018999993801117},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C2780030769","wikidata":"https://www.wikidata.org/wiki/Q4968575","display_name":"Plateau (mathematics)","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2549999952316284},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cw68232.2025.00035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cw68232.2025.00035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Cyberworlds (CW\uff09","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","score":0.8243253231048584,"display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2049466529","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2963037989","https://openalex.org/W3034971973","https://openalex.org/W3090841701","https://openalex.org/W3194790201","https://openalex.org/W4312513746","https://openalex.org/W4400762160"],"related_works":[],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,8,21,40,55,70,75,82,89],"inefficiency":[3],"of":[4,12,69,96],"traditional":[5],"methods":[6],"and":[7,44,53,64,108],"high":[9],"computational":[10,62],"overhead":[11],"deep":[13],"learning":[14],"models":[15],"in":[16],"sporopollen":[17,134],"spore":[18,135],"recognition":[19],"on":[20,74,106],"Tibetan":[22,83],"Plateau,":[23],"this":[24],"paper":[25],"proposes":[26],"a":[27,92],"lightweight":[28,123],"object":[29],"detection":[30],"model,":[31],"YOLOv11-TinyED.":[32],"The":[33,67],"model":[34,71,90],"incorporates":[35],"two":[36],"innovative":[37],"designs:":[38],"1)":[39],"SEA":[41],"attention":[42],"module":[43],"SEAConv":[45],"convolution,":[46],"which":[47],"dynamically":[48],"enhance":[49],"critical":[50],"feature":[51,65],"channels,":[52],"2)":[54],"Hybrid":[56],"Head":[57],"Self-Attention":[58],"(HHSA)":[59],"module,":[60],"balancing":[61],"cost":[63],"diversity.":[66],"effectiveness":[68],"is":[72],"validated":[73],"constructed":[76],"QT-Sporopollen":[77],"dataset":[78],"(22":[79],"classes)":[80],"from":[81],"Plateau.":[84],"Experimental":[85],"results":[86],"demonstrate":[87],"that":[88],"achieves":[91],"compact":[93],"parameter":[94],"size":[95],"only":[97],"1.83":[98],"MB,":[99],"delivers":[100],"real-time":[101],"inference":[102],"at":[103],"83":[104],"FPS":[105],"LubanCat,":[107],"attains":[109],"an$\\text{m":[110],"A":[111],"P}":[112],"{@}":[113],"\\text{0.":[114],"5}$of$\\text{9":[115],"9.":[116],"4":[117],"3":[118],"\\%}$,":[119],"significantly":[120],"outperforming":[121],"other":[122],"models.":[124],"This":[125],"research":[126],"provides":[127],"an":[128],"efficient":[129],"solution":[130],"for":[131],"edge-device-based":[132],"intelligent":[133],"recognition.":[136],"Code:":[137],"https://github.com/HeHuangAI/YOLOv11-TinyED.":[138]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-02T00:00:00"}
