{"id":"https://openalex.org/W3000452974","doi":"https://doi.org/10.1109/taai48200.2019.8959823","title":"Locating Wood Pith in a Wood Stem Cross Sectional Image Using YOLO Object Detection","display_name":"Locating Wood Pith in a Wood Stem Cross Sectional Image Using YOLO Object Detection","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3000452974","doi":"https://doi.org/10.1109/taai48200.2019.8959823","mag":"3000452974"},"language":"en","primary_location":{"id":"doi:10.1109/taai48200.2019.8959823","doi":"https://doi.org/10.1109/taai48200.2019.8959823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taai48200.2019.8959823","pdf_url":null,"source":{"id":"https://openalex.org/S7407087555","display_name":"Conference on Technologies and Applications of Artificial Intelligence (TAAI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Technologies and Applications of Arti\ufb01cial Intelligence (TAAI)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5071113438","display_name":"Wattanapong Kurdthongmee","orcid":"https://orcid.org/0000-0001-6467-1039","observed_orcids":["https://orcid.org/0000-0001-6467-1039"]},"institutions":[{"id":"https://openalex.org/I96916377","display_name":"Walailak University","ror":"https://ror.org/04b69g067","country_code":"TH","type":"education","lineage":["https://openalex.org/I96916377"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Wattanapong Kurdthongmee","raw_affiliation_strings":["Division of Computer Engineering, Walailak University, Nakornsithammarat, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Computer Engineering, Walailak University, Nakornsithammarat, Thailand","institution_ids":["https://openalex.org/I96916377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061905232","display_name":"Korakot Suwannarat","orcid":"https://orcid.org/0000-0002-2157-958X","observed_orcids":["https://orcid.org/0000-0002-2157-958X"]},"institutions":[{"id":"https://openalex.org/I96916377","display_name":"Walailak University","ror":"https://ror.org/04b69g067","country_code":"TH","type":"education","lineage":["https://openalex.org/I96916377"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Korrakot Suwannarat","raw_affiliation_strings":["Division of Computer Engineering, Walailak University, Nakornsithammarat, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Computer Engineering, Walailak University, Nakornsithammarat, Thailand","institution_ids":["https://openalex.org/I96916377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I96916377"],"apc_list":null,"apc_paid":null,"fwci":1.2018,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.73811452,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":97},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.548799991607666,"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":0.548799991607666,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.37400001287460327,"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"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.06880000233650208,"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/yolo","display_name":"YOLO","score":0.9509999752044678},{"id":"https://openalex.org/keywords/tiny-yolo","display_name":"Tiny-YOLO","score":0.9829999804496765},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.9760000109672546},{"id":"https://openalex.org/keywords/wood-quality-assessment","display_name":"wood quality assessment","score":0.8989999890327454},{"id":"https://openalex.org/keywords/training-iterations","display_name":"training iterations","score":0.6570000052452087},{"id":"https://openalex.org/keywords/detection-accuracy","display_name":"detection accuracy","score":0.6840000152587891}],"concepts":[{"id":"https://openalex.org/C15228724","wikidata":"https://www.wikidata.org/wiki/Q192576","display_name":"Pith","level":2,"score":0.9607846140861511},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6023594737052917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5645256638526917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5009393692016602},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4934186041355133},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4874327480792999},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4852125942707062},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4022618532180786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3630148768424988},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3260672986507416},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.10720637440681458},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taai48200.2019.8959823","doi":"https://doi.org/10.1109/taai48200.2019.8959823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taai48200.2019.8959823","pdf_url":null,"source":{"id":"https://openalex.org/S7407087555","display_name":"Conference on Technologies and Applications of Artificial Intelligence (TAAI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Technologies and Applications of Arti\ufb01cial Intelligence (TAAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1483870316","https://openalex.org/W2032204667","https://openalex.org/W2067911292","https://openalex.org/W2089944454","https://openalex.org/W2168784183","https://openalex.org/W2796347433","https://openalex.org/W2891978237","https://openalex.org/W2902390267","https://openalex.org/W2916869646","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W4417234330","https://openalex.org/W3008772830","https://openalex.org/W4404908002","https://openalex.org/W4416953158","https://openalex.org/W7204521504","https://openalex.org/W4286850918","https://openalex.org/W4388262645","https://openalex.org/W4308409799","https://openalex.org/W6995839761","https://openalex.org/W1985841678"],"abstract_inverted_index":{"Sawmills":[0],"rely":[1],"on":[2],"locating":[3],"wood":[4,12,26,30,33,57,68,90,109],"pith":[5,34,91,113,270],"at":[6,228],"the":[7,17,21,49,67,121,138,144,154,179,186,199,203,211,236,264,267],"end":[8],"surface":[9],"of":[10,29,56,181,221,226,231,240,266],"a":[11,53,72,89,103,219,243],"stem":[13],"either":[14],"to":[15,24,88,132,152,177,235,263],"automate":[16],"process":[18],"and":[19,58,123,126,135,193,206,223],"maximize":[20],"yield":[22],"or":[23],"perform":[25],"quality":[27],"assessment":[28],"planks.":[31],"Previously,":[32],"location":[35],"was":[36,100],"approximated":[37],"by":[38],"applying":[39],"image":[40,55],"processing":[41],"algorithms":[42],"along":[43],"with":[44,97,107,112,117,189,242,257],"some":[45],"geometric":[46],"relations":[47],"between":[48,120,202],"annual":[50],"rings":[51],"within":[52],"cross-sectional":[54,110],"its":[59,190],"pith.":[60],"This":[61],"produces":[62,250],"failed":[63],"detection":[64,87,165,175,238,271],"results":[65],"if":[66],"stems":[69],"deviate":[70],"from":[71],"circular":[73],"shape.":[74],"In":[75],"this":[76,170],"paper,":[77],"we":[78,173],"propose":[79],"employing":[80],"YOLO":[81],"(you":[82],"only":[83,251],"look":[84],"once)":[85],"object":[86,164],"identification":[92],"problem.":[93],"The":[94,214],"Tiny-YOLO":[95],"network":[96],"23":[98],"layers":[99],"trained":[101,156,187],"using":[102],"transfer":[104],"learning":[105],"approach":[106],"345":[108],"images":[111],"annotations.":[114],"Different":[115],"experiments":[116],"variable":[118],"ratios":[119],"training":[122,127,222,229],"testing":[124,139,191,224],"datasets":[125,225],"iterations":[128,230],"were":[129],"carried":[130],"out":[131],"produce":[133],"checkpoints":[134],"test":[136],"against":[137],"datasets.":[140],"We":[141],"found":[142],"that":[143,218],"minimum":[145],"loss":[146],"function":[147],"which":[148],"is":[149,166],"commonly":[150],"used":[151,174],"indicate":[153],"best":[155],"model":[157,188],"in":[158],"other":[159],"deep":[160],"neural":[161],"networks":[162],"(DNNs)":[163],"not":[167],"effective":[168],"for":[169],"application.":[171],"Therefore,":[172],"accuracy,":[176],"reflect":[178],"percentage":[180],"detectable":[182],"piths":[183,205],"while":[184],"inferencing":[185],"dataset":[192],"an":[194],"average":[195,200,245,253],"distance":[196,246,254],"error,":[197],"i.e.,":[198],"distances":[201],"detected":[204],"their":[207],"ground":[208],"truths,":[209],"as":[210],"accuracy":[212],"measurement.":[213],"experimental":[215],"result":[216],"shows":[217],"ratio":[220],"70:30,":[227],"170,000,":[232],"gives":[233],"rise":[234],"maximum":[237],"value":[239],"76.3%":[241],"16.6-pixel":[244],"error.":[247],"Additionally,":[248],"it":[249],"25%":[252],"error;":[255],"10.34/41.76":[256],"50%":[258],"standard":[259],"deviation;":[260],"25.33/53.95,":[261],"compared":[262],"state":[265],"art":[268],"nonDNN-based":[269],"approach.":[272]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2020-01-23T00:00:00"}
