{"id":"https://openalex.org/W4364305286","doi":"https://doi.org/10.1109/aipr57179.2022.10092233","title":"Toward CNN Architectures for Image Detection","display_name":"Toward CNN Architectures for Image Detection","publication_year":2022,"publication_date":"2022-10-11","ids":{"openalex":"https://openalex.org/W4364305286","doi":"https://doi.org/10.1109/aipr57179.2022.10092233"},"language":"en","primary_location":{"id":"doi:10.1109/aipr57179.2022.10092233","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aipr57179.2022.10092233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5004270568","display_name":"Steven A. Israel","orcid":null},"institutions":[{"id":"https://openalex.org/I1343143571","display_name":"Draper Laboratory","ror":"https://ror.org/04378d909","country_code":"US","type":"funder","lineage":["https://openalex.org/I1343143571"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Steven A Israel","raw_affiliation_strings":["The Charles Stark Draper Laboratory, Inc,Reston,VA,20190"],"affiliations":[{"raw_affiliation_string":"The Charles Stark Draper Laboratory, Inc,Reston,VA,20190","institution_ids":["https://openalex.org/I1343143571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014225344","display_name":"John M. Irvine","orcid":"https://orcid.org/0000-0003-4294-9380"},"institutions":[{"id":"https://openalex.org/I44896327","display_name":"Mitre (United States)","ror":"https://ror.org/03ks2a131","country_code":"US","type":"company","lineage":["https://openalex.org/I44896327"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John M Irvine","raw_affiliation_strings":["The MITRE Corporation,Bedford,MA,01730"],"affiliations":[{"raw_affiliation_string":"The MITRE Corporation,Bedford,MA,01730","institution_ids":["https://openalex.org/I44896327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086990492","display_name":"Ieuan M. Israel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ieuan M Israel","raw_affiliation_strings":["Ludis Analytics,Somerville,MA,02143"],"affiliations":[{"raw_affiliation_string":"Ludis Analytics,Somerville,MA,02143","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004270568"],"corresponding_institution_ids":["https://openalex.org/I1343143571"],"apc_list":null,"apc_paid":null,"fwci":0.3064,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57068187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"54","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9988999962806702,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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":0.8034971356391907},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7508636713027954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6940679550170898},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.628711998462677},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.571009635925293},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5684239864349365},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5633985996246338},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5001246929168701},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4918108284473419},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4309839606285095},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42476707696914673},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37510406970977783},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11871013045310974}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8034971356391907},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7508636713027954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6940679550170898},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.628711998462677},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.571009635925293},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5684239864349365},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5633985996246338},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5001246929168701},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4918108284473419},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4309839606285095},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42476707696914673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37510406970977783},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11871013045310974},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/aipr57179.2022.10092233","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/aipr57179.2022.10092233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"},{"score":0.4699999988079071,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W654913055","https://openalex.org/W2009263119","https://openalex.org/W2108598243","https://openalex.org/W2113107995","https://openalex.org/W2541482366","https://openalex.org/W2783125572","https://openalex.org/W3019600191","https://openalex.org/W3118843071","https://openalex.org/W3160665059","https://openalex.org/W3162611156","https://openalex.org/W4205687621","https://openalex.org/W4281625506","https://openalex.org/W4285347395"],"related_works":["https://openalex.org/W2969228573","https://openalex.org/W2963690996","https://openalex.org/W2911497689","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W3010730661"],"abstract_inverted_index":{"Currently,":[0],"convolutional":[1,110],"neural":[2,111],"networks":[3,70],"(CNNs)":[4],"drive":[5],"the":[6,42,55,87,90,172,208],"state-of-the-art":[7],"for":[8,117],"object":[9],"detection":[10,38,88],"and":[11,39,76,101,139,154,164,176,183,214],"classification":[12],"in":[13,131],"imagery.":[14],"Pre-trained":[15],"models":[16,94],"exist":[17],"with":[18,64,109],"hundreds":[19,29],"of":[20,30,48,144,191],"million":[21],"computational":[22,80],"artificial":[23],"neurons":[24],"to":[25,54,114,196,218],"classify":[26],"images":[27,168,199],"into":[28],"different":[31,142],"classes.":[32,49,146,166],"However,":[33],"most":[34],"image":[35,107,226],"characterization":[36],"problems,":[37],"classification,":[40],"require":[41],"discrimination":[43,63],"across":[44],"only":[45],"a":[46,178,187],"handful":[47],"By":[50],"focusing":[51],"our":[52],"training":[53],"limited":[56],"class":[57],"list,":[58],"we":[59,150],"expect":[60],"dramatically":[61],"improved":[62],"lower":[65],"resource":[66],"costs.":[67],"Ideally,":[68],"these":[69,213],"are":[71,204],"trained":[72,151],"using":[73],"smaller":[74],"datasets":[75],"needed":[77],"far":[78],"fewer":[79],"neurons.":[81],"As":[82],"researchers":[83],"focus":[84],"more":[85,224],"on":[86,186],"paradigm,":[89],"expected":[91],"smaller,":[92],"nimbler":[93],"that":[95,203,222],"balance":[96],"performance":[97],"optimization":[98],"between":[99,181],"correctness":[100],"throughput.":[102],"Our":[103,125],"previous":[104,215],"studies":[105],"matched":[106],"quality":[108],"network":[112],"parameters":[113,221],"develop":[115],"strategies":[116],"automating":[118],"orchestrated":[119],"collection":[120],"based":[121],"upon":[122],"information":[123],"needs.":[124],"current":[126],"study":[127],"extends":[128],"those":[129],"goals":[130],"two":[132],"directions:":[133],"1.":[134],"incorporate":[135,141],"hidden":[136,159],"layer":[137],"depth;":[138],"2.":[140],"number":[143,190],"output":[145,165],"In":[147],"this":[148],"study,":[149],"multiple":[152],"CNNs":[153],"varied":[155,193],"their":[156],"window":[157],"sizes,":[158],"layers,":[160],"latent":[161],"space":[162],"dimensions,":[163],"The":[167,189,198,210],"were":[169],"acquired":[170],"from":[171,194,207,212],"rare":[173],"planes":[174],"database":[175],"have":[177],"variable":[179],"size":[180],"100":[182],"400":[184],"pixels":[185],"side.":[188],"classes":[192],"2":[195],"8.":[197],"contain":[200],"synthetic":[201],"targets":[202],"visually":[205],"separable":[206],"background.":[209],"outcome":[211],"experiments":[216],"is":[217],"identify":[219],"candidate":[220],"enable":[223],"efficient":[225],"search":[227],"models.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
