{"id":"https://openalex.org/W4405490612","doi":"https://doi.org/10.1109/iccp63557.2024.10792992","title":"Context-Based Adaptation of Neural Network Compression for Unmanned Aerial Vehicle (UAV) Weed Detection","display_name":"Context-Based Adaptation of Neural Network Compression for Unmanned Aerial Vehicle (UAV) Weed Detection","publication_year":2024,"publication_date":"2024-10-17","ids":{"openalex":"https://openalex.org/W4405490612","doi":"https://doi.org/10.1109/iccp63557.2024.10792992"},"language":"en","primary_location":{"id":"doi:10.1109/iccp63557.2024.10792992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp63557.2024.10792992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Intelligent Computer Communication and Processing (ICCP)","raw_type":"proceedings-article"},"type":"article","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/A5115513756","display_name":"Ioana C. Igret","orcid":null},"institutions":[{"id":"https://openalex.org/I3125347698","display_name":"Babe\u0219-Bolyai University","ror":"https://ror.org/02rmd1t30","country_code":"RO","type":"education","lineage":["https://openalex.org/I3125347698"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Ioana C. Igret","raw_affiliation_strings":["Babes-Bolyai University,Faculty of Mathematics and Computer Science,Cluj-Napoca,Romania"],"affiliations":[{"raw_affiliation_string":"Babes-Bolyai University,Faculty of Mathematics and Computer Science,Cluj-Napoca,Romania","institution_ids":["https://openalex.org/I3125347698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024234396","display_name":"Alina L. Machidon","orcid":"https://orcid.org/0000-0002-9330-3865"},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Alina L. Machidon","raw_affiliation_strings":["University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia"],"affiliations":[{"raw_affiliation_string":"University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia","institution_ids":["https://openalex.org/I153976015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082970512","display_name":"Octavian Machidon","orcid":"https://orcid.org/0000-0003-3133-1008"},"institutions":[{"id":"https://openalex.org/I153976015","display_name":"University of Ljubljana","ror":"https://ror.org/05njb9z20","country_code":"SI","type":"education","lineage":["https://openalex.org/I153976015"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Octavian M. Machidon","raw_affiliation_strings":["University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia"],"affiliations":[{"raw_affiliation_string":"University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia","institution_ids":["https://openalex.org/I153976015"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5115513756"],"corresponding_institution_ids":["https://openalex.org/I3125347698"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13085202,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9800000190734863,"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":0.9800000190734863,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7027137875556946},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6762644052505493},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6211156845092773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5173727869987488},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.5148664712905884},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4719086289405823},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4123503565788269},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08185729384422302}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7027137875556946},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6762644052505493},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6211156845092773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5173727869987488},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5148664712905884},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4719086289405823},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4123503565788269},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08185729384422302},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccp63557.2024.10792992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp63557.2024.10792992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Intelligent Computer Communication and Processing (ICCP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1995609428","https://openalex.org/W2767767563","https://openalex.org/W2950863339","https://openalex.org/W2956274063","https://openalex.org/W2963363373","https://openalex.org/W3010345596","https://openalex.org/W3022353848","https://openalex.org/W3034238686","https://openalex.org/W3039502206","https://openalex.org/W3130607817","https://openalex.org/W3131368198","https://openalex.org/W3165299669","https://openalex.org/W3165807380","https://openalex.org/W6638783484","https://openalex.org/W6757036269","https://openalex.org/W6797075700"],"related_works":["https://openalex.org/W2997567050","https://openalex.org/W1483272040","https://openalex.org/W4283377908","https://openalex.org/W1526712007","https://openalex.org/W1533421371","https://openalex.org/W2003050223","https://openalex.org/W2091777911","https://openalex.org/W2766405861","https://openalex.org/W2360975119","https://openalex.org/W2912421143"],"abstract_inverted_index":{"UAV":[0,63],"computer":[1],"vision":[2],"in":[3,48],"precision":[4],"agriculture":[5],"can":[6,128],"enhance":[7],"efficiency":[8],"and":[9,21,75,84,155],"reduce":[10],"environmental":[11],"impact":[12],"compared":[13],"to":[14,130,132,147],"traditional":[15],"techniques.":[16],"However,":[17],"the":[18,87,111,140,143,153],"computational":[19],"limitations":[20],"power":[22],"constraints":[23],"of":[24,142,157],"UAVs":[25],"hinder":[26],"their":[27],"performance,":[28],"especially":[29],"for":[30,59],"real-time":[31,61],"deep":[32],"learning":[33],"tasks.":[34],"Compressed":[35],"neural":[36,89,95],"network":[37,90],"models":[38],"offer":[39],"a":[40,55,100],"solution,":[41],"yet":[42],"fixed":[43],"compression":[44,91],"levels":[45],"may":[46],"result":[47],"unacceptable":[49],"accuracy":[50],"loss.":[51],"This":[52,150],"paper":[53],"proposes":[54],"novel":[56],"context-aware":[57],"approach":[58,109,127],"energy-efficient,":[60],"on-":[62],"weed":[64,159],"detection.":[65],"By":[66],"incorporating":[67],"contextual":[68],"factors":[69],"such":[70],"as":[71],"brightness,":[72],"saturation,":[73],"contrast,":[74],"vegetation":[76],"indices,":[77],"our":[78,108,126],"method":[79],"estimates":[80],"input":[81],"image":[82],"difficulty":[83],"dynamically":[85],"selects":[86],"optimal":[88],"level.":[92],"Leveraging":[93],"slimmable":[94,121],"networks,":[96],"which":[97],"enable":[98],"training":[99],"single":[101],"model":[102],"with":[103,118,145],"various":[104],"widths":[105],"during":[106],"inference,":[107],"ensures":[110],"best":[112],"accuracy-resource":[113],"consumption":[114],"trade-off.":[115],"Experimental":[116],"results":[117],"two":[119],"different":[120],"networks":[122],"architectures":[123],"showed":[124],"that":[125],"lead":[129],"up":[131,146],"35":[133],"%":[134],"less":[135],"computations":[136],"while":[137],"also":[138],"increasing":[139],"quality":[141],"inference":[144],"2":[148],"%.":[149],"significantly":[151],"improves":[152],"practicality":[154],"performance":[156],"UAV-based":[158],"detection":[160],"systems.":[161]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
