{"id":"https://openalex.org/W4386067017","doi":"https://doi.org/10.23919/mva57639.2023.10216087","title":"A Hybrid Wheat Head Detection model with Incorporated CNN and Transformer","display_name":"A Hybrid Wheat Head Detection model with Incorporated CNN and Transformer","publication_year":2023,"publication_date":"2023-07-23","ids":{"openalex":"https://openalex.org/W4386067017","doi":"https://doi.org/10.23919/mva57639.2023.10216087"},"language":"en","primary_location":{"id":"doi:10.23919/mva57639.2023.10216087","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/mva57639.2023.10216087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 18th International Conference on Machine Vision and Applications (MVA)","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/A5102063971","display_name":"Sho Harada","orcid":null},"institutions":[{"id":"https://openalex.org/I173915773","display_name":"Yamaguchi University","ror":"https://ror.org/03cxys317","country_code":"JP","type":"education","lineage":["https://openalex.org/I173915773"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Sho Harada","raw_affiliation_strings":["Yamaguchi University,Graduate School of Science and Technology for Innovation,Yamaguchi,Japan,753-8511"],"affiliations":[{"raw_affiliation_string":"Yamaguchi University,Graduate School of Science and Technology for Innovation,Yamaguchi,Japan,753-8511","institution_ids":["https://openalex.org/I173915773"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086851360","display_name":"Xian\u2010Hua Han","orcid":"https://orcid.org/0000-0002-5003-3180"},"institutions":[{"id":"https://openalex.org/I173915773","display_name":"Yamaguchi University","ror":"https://ror.org/03cxys317","country_code":"JP","type":"education","lineage":["https://openalex.org/I173915773"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xian-Hua Han","raw_affiliation_strings":["Yamaguchi University,Graduate School of Science and Technology for Innovation,Yamaguchi,Japan,753-8511"],"affiliations":[{"raw_affiliation_string":"Yamaguchi University,Graduate School of Science and Technology for Innovation,Yamaguchi,Japan,753-8511","institution_ids":["https://openalex.org/I173915773"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102063971"],"corresponding_institution_ids":["https://openalex.org/I173915773"],"apc_list":null,"apc_paid":null,"fwci":0.8246,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.82268953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9994999766349792,"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":0.9994999766349792,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9607999920845032,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9416999816894531,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/computer-science","display_name":"Computer science","score":0.7883553504943848},{"id":"https://openalex.org/keywords/subnet","display_name":"Subnet","score":0.7208905220031738},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6672253608703613},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5525389909744263},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5457004308700562},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5333841443061829},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.505931556224823},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49661070108413696},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4965274930000305},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.42265379428863525},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3273921012878418},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08648234605789185}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7883553504943848},{"id":"https://openalex.org/C21099817","wikidata":"https://www.wikidata.org/wiki/Q7631721","display_name":"Subnet","level":2,"score":0.7208905220031738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6672253608703613},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5525389909744263},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5457004308700562},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5333841443061829},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.505931556224823},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49661070108413696},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4965274930000305},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.42265379428863525},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3273921012878418},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08648234605789185},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/mva57639.2023.10216087","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/mva57639.2023.10216087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 18th International Conference on Machine Vision and Applications (MVA)","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":43,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2061563558","https://openalex.org/W2186094539","https://openalex.org/W2407521645","https://openalex.org/W2511730936","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2727409292","https://openalex.org/W2752782242","https://openalex.org/W2884585870","https://openalex.org/W2950800384","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963446712","https://openalex.org/W2963927307","https://openalex.org/W3012573144","https://openalex.org/W3018757597","https://openalex.org/W3064678530","https://openalex.org/W3097065222","https://openalex.org/W3106250896","https://openalex.org/W3117599028","https://openalex.org/W3118028009","https://openalex.org/W3137394779","https://openalex.org/W3174060225","https://openalex.org/W3210586215","https://openalex.org/W4224065213","https://openalex.org/W4293584584","https://openalex.org/W4298395628","https://openalex.org/W6620707391","https://openalex.org/W6639102338","https://openalex.org/W6686583229","https://openalex.org/W6731892127","https://openalex.org/W6750227808","https://openalex.org/W6753412334","https://openalex.org/W6753494528","https://openalex.org/W6760424586","https://openalex.org/W6763367864","https://openalex.org/W6775170262","https://openalex.org/W6777046832","https://openalex.org/W6785652829","https://openalex.org/W6787883080"],"related_works":["https://openalex.org/W2102539527","https://openalex.org/W2131631951","https://openalex.org/W2356206668","https://openalex.org/W2130707537","https://openalex.org/W3200778902","https://openalex.org/W1977409556","https://openalex.org/W2361602549","https://openalex.org/W4362683600","https://openalex.org/W2105155969","https://openalex.org/W4394867575"],"abstract_inverted_index":{"Wheat":[0,206],"head":[1,38,69,84,185],"detection":[2,39,60,66,106,199],"is":[3],"an":[4,131],"important":[5],"research":[6],"topic":[7],"for":[8,62,114,141],"production":[9],"estimation":[10],"and":[11,41,71,97,112,129,156,168,189],"growth":[12,93],"management.":[13],"Motivated":[14],"by":[15,108,160],"the":[16,20,30,36,48,58,63,78,82,110,162,169,175,178,187,204,213,220,227],"great":[17],"advantages":[18],"of":[19,77,81,191,215],"deep":[21],"convolution":[22],"neural":[23],"networks":[24],"(DCNNs)":[25],"in":[26,74,165],"many":[27],"vision":[28],"tasks,":[29],"deep-learning":[31],"based":[32],"methods":[33,55,223],"have":[34,211],"dominated":[35],"wheat":[37,68,83,105,149,192],"field,":[40],"manifest":[42],"remarkable":[43],"performance":[44],"improvement":[45],"compared":[46],"with":[47],"traditional":[49],"image":[50],"processing":[51],"methods.":[52],"The":[53],"existing":[54,221],"usually":[56],"divert":[57],"proposed":[59,217],"models":[61],"generic":[64],"object":[65],"to":[67,91,125,136,146,173,194],"detection,":[70],"are":[72],"insu\ufb03cient":[73],"taking":[75],"account":[76],"specific":[79],"characteristics":[80],"images":[85],"such":[86],"as":[87,224,226],"large":[88],"variations":[89],"due":[90],"different":[92],"stages,":[94],"high":[95],"density":[96],"overlaps.":[98],"This":[99],"work":[100],"exploits":[101],"a":[102,122,154,183,196],"novel":[103,155],"hybrid":[104],"model":[107,218],"incorporating":[109,161],"CNN":[111],"transformer":[113,158],"modeling":[115],"long-range":[116],"dependence.":[117],"Specifically,":[118],"we":[119,152],"firstly":[120],"employ":[121],"backbone":[123],"ResNet":[124],"extract":[126],"multi-scale":[127,180],"features,":[128],"leverage":[130],"inter-scale":[132],"feature":[133,170],"fusion":[134],"module":[135,164],"aggregate":[137],"coarse-to-fine":[138],"features":[139],"together":[140],"capturing":[142],"su\ufb03cient":[143],"spatial":[144],"detail":[145],"localize":[147],"small-size":[148],"head.":[150],"Moreover,":[151],"propose":[153],"e\ufb03cient":[157],"block":[159],"self-attention":[163],"channel":[166],"direction":[167],"feed-forward":[171],"subnet":[172],"explore":[174],"interaction":[176],"among":[177],"aggregated":[179],"features.":[181],"Finally":[182],"prediction":[184],"produces":[186],"centerness":[188],"size":[190],"heads":[193],"obtain":[195],"simple":[197],"anchor-free":[198],"model.":[200,229],"Extensive":[201],"experiments":[202],"on":[203],"Global":[205],"Head":[207],"Detection":[208],"(GWHD)":[209],"dataset":[210],"demonstrated":[212],"superiority":[214],"our":[216],"over":[219],"state-of-the-art":[222],"well":[225],"baseline":[228]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}