{"id":"https://openalex.org/W3029934601","doi":"https://doi.org/10.1117/12.2558542","title":"Background adaptive faster R-CNN for semi-supervised convolutional object detection of threats in x-ray images","display_name":"Background adaptive faster R-CNN for semi-supervised convolutional object detection of threats in x-ray images","publication_year":2020,"publication_date":"2020-05-26","ids":{"openalex":"https://openalex.org/W3029934601","doi":"https://doi.org/10.1117/12.2558542","mag":"3029934601"},"language":"en","primary_location":{"id":"doi:10.1117/12.2558542","doi":"https://doi.org/10.1117/12.2558542","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2558542","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anomaly Detection and Imaging with X-Rays (ADIX) V","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2010.01202","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070896748","display_name":"John Brevard Sigman","orcid":"https://orcid.org/0000-0003-2440-8695","observed_orcids":["https://orcid.org/0000-0003-2440-8695"]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John B. Sigman","raw_affiliation_strings":["Duke Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke Univ. (United States)","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072522626","display_name":"Gregory P. Spell","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregory P. Spell","raw_affiliation_strings":["Duke Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke Univ. (United States)","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061499526","display_name":"Kevin J Liang","orcid":"https://orcid.org/0000-0002-0221-9108","observed_orcids":["https://orcid.org/0000-0002-0221-9108"]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin J. Liang","raw_affiliation_strings":["Duke Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke Univ. (United States)","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016448581","display_name":"Lawrence Carin","orcid":"https://orcid.org/0000-0001-6277-7948","observed_orcids":["https://orcid.org/0000-0001-6277-7948"]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence Carin","raw_affiliation_strings":["Duke Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke Univ. (United States)","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.8358,"has_fulltext":true,"cited_by_count":24,"citation_normalized_percentile":{"value":0.80989462,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5605000257492065,"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":0.5605000257492065,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.43140000104904175,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.0017000000225380063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/threat-detection","display_name":"threat detection","score":0.718999981880188},{"id":"https://openalex.org/keywords/x-ray-images","display_name":"X-ray images","score":0.8579999804496765},{"id":"https://openalex.org/keywords/semi-supervised-object-detection","display_name":"semi-supervised object detection","score":0.8830000162124634},{"id":"https://openalex.org/keywords/faster-r-cnn","display_name":"Faster R-CNN","score":0.9449999928474426},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"domain adaptation","score":0.9480000138282776},{"id":"https://openalex.org/keywords/background-adaptation","display_name":"background adaptation","score":0.7129999995231628},{"id":"https://openalex.org/keywords/false-alarm-reduction","display_name":"false alarm reduction","score":0.7739999890327454},{"id":"https://openalex.org/keywords/aviation-security","display_name":"aviation security","score":0.6159999966621399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8085997104644775},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7816495895385742},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6919543147087097},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6464961767196655},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5554660558700562},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5505509376525879},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5407540798187256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5072362422943115},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.44237229228019714},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4418710470199585},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.41074472665786743},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.41038334369659424},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36460167169570923},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2697358727455139},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07461956143379211},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1117/12.2558542","doi":"https://doi.org/10.1117/12.2558542","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2558542","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anomaly Detection and Imaging with X-Rays (ADIX) V","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2010.01202","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.01202","pdf_url":"https://arxiv.org/pdf/2010.01202","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2010.01202","doi":"https://doi.org/10.48550/arxiv.2010.01202","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2010.01202","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2010.01202","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.01202","pdf_url":"https://arxiv.org/pdf/2010.01202","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306110","display_name":"U.S. Department of Homeland Security","ror":"https://ror.org/00jyr0d86"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3029934601.pdf","grobid_xml":"https://content.openalex.org/works/W3029934601.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1483870316","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1861492603","https://openalex.org/W1969304603","https://openalex.org/W2102605133","https://openalex.org/W2108315410","https://openalex.org/W2124386111","https://openalex.org/W2147800946","https://openalex.org/W2189527060","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2400159428","https://openalex.org/W2407521645","https://openalex.org/W2510597476","https://openalex.org/W2557728737","https://openalex.org/W2570343428","https://openalex.org/W2624777067","https://openalex.org/W2790341021","https://openalex.org/W2790624640","https://openalex.org/W2792998389","https://openalex.org/W2884561390","https://openalex.org/W2921464306","https://openalex.org/W2962843773","https://openalex.org/W2964115968","https://openalex.org/W2975131999","https://openalex.org/W2996091868","https://openalex.org/W2997849134","https://openalex.org/W2998115938","https://openalex.org/W3006201185","https://openalex.org/W3106250896","https://openalex.org/W4239510810","https://openalex.org/W4298289240"],"related_works":["https://openalex.org/W2914804954","https://openalex.org/W4413143008","https://openalex.org/W4286432907","https://openalex.org/W4320031102","https://openalex.org/W3113581687","https://openalex.org/W3127626886","https://openalex.org/W4384201840","https://openalex.org/W3006025454","https://openalex.org/W3103293225","https://openalex.org/W3181789955"],"abstract_inverted_index":{"Recently,":[0],"progress":[1],"has":[2],"been":[3],"made":[4],"in":[5,19,41,69,99],"the":[6,29,42,82,86,129,168,192,230,241,263,295,313,324,330,336],"supervised":[7,252],"training":[8,64,156],"of":[9,28,54,114,126,170,187,197,274,299,326,335,348],"Convolutional":[10,231],"Object":[11],"Detectors":[12],"(e.g.":[13],"Faster":[14,150,296],"RCNN)":[15],"for":[16,38,51,128,142,158,206,212,288],"threat":[17,111,131,307],"recognition":[18,132,308],"carry-on":[20],"luggage":[21],"using":[22],"X-ray":[23],"images.":[24],"This":[25,152,304],"is":[26,154,226],"part":[27],"Transportation":[30],"Security":[31],"Administration's":[32],"(TSA's)":[33],"mission":[34],"to":[35,62,74,218,237,259,277,294,333],"ensure":[36],"safety":[37],"air":[39],"travelers":[40],"United":[43],"States.":[44],"Collecting":[45],"more":[46],"data":[47,65,77,80,116,127,174,185],"reliably":[48],"improves":[49],"performance":[50],"this":[52,100,135,143,224],"class":[53],"deep":[55,171],"algorithm,":[56],"but":[57],"requires":[58],"time":[59],"and":[60,123,191,210,248,269,316],"money":[61],"produce":[63],"with":[66,93,189],"threats":[67],"staged":[68],"realistic":[70],"contexts.":[71],"In":[72,134],"contrast":[73],"these":[75,108,115,289],"hand-collected":[76,184,264,331],"containing":[78],"threats,":[79,190],"from":[81,167,240,301,312],"real-world,":[83],"known":[84],"as":[85],"Stream-of-Commerce":[87],"(SOC),":[88],"can":[89,235,317],"be":[90],"collected":[91],"quickly":[92],"minimal":[94],"cost;":[95],"while":[96],"technically":[97],"unlabeled,":[98],"work":[101],"we":[102,118,137,146],"make":[103],"a":[104,139,155,183,194,250],"practical":[105],"assumption":[106],"that":[107,334],"are":[109,178,215,284,292,342],"without":[110,200,255],"objects.":[112,261],"Because":[113],"constraints,":[117],"will":[119],"use":[120],"both":[121,302],"labeled":[122,314,349],"unlabeled":[124],"sources":[125,175,243],"automatic":[130],"problem.":[133],"paper,":[136],"present":[138],"semi-supervised":[140],"approach":[141,153],"problem":[144],"which":[145,162],"call":[147],"Background":[148],"Adaptive":[149],"R-CNN.":[151],"method":[157],"two-stage":[159],"object":[160,208,267,280],"detectors":[161],"uses":[163],"Domain":[164],"Adaptation":[165],"methods":[166],"field":[169],"learning.":[172],"The":[173,286],"described":[176],"earlier":[177],"considered":[179],"two":[180,242,345],"\u201cdomains\":":[181],"one":[182,205,211],"domain":[186,196,203],"images":[188,198,239,300],"other":[193],"real-world":[195],"assumed":[199],"threats.":[201,350],"Two":[202],"discriminators,":[204],"discriminating":[207],"proposals":[209,268],"image":[213,270],"features,":[214],"adversarially":[216],"trained":[217],"prevent":[219],"encoding":[220,225],"domain-specific":[221],"information.":[222],"Penalizing":[223],"important":[227],"because":[228],"otherwise":[229],"Neural":[232],"Network":[233],"(CNN)":[234],"learn":[236],"distinguish":[238],"based":[244,309],"on":[245,310,329,344],"superficial":[246],"characteristics,":[247],"minimize":[249],"purely":[251],"loss":[253],"function":[254],"improving":[256],"its":[257],"ability":[258],"recognize":[260],"For":[262],"data,":[265,315],"only":[266],"features":[271,328],"completely":[272],"outside":[273],"areas":[275],"corresponding":[276],"ground":[278],"truth":[279],"bounding":[281],"boxes":[282],"(background)":[283],"used.":[285],"losses":[287,298],"domain-adaptive":[290],"discriminators":[291],"added":[293],"R-CNN":[297],"domains.":[303],"technique":[305],"enables":[306],"examples":[311],"reduce":[318],"false":[319],"alarm":[320],"rates":[321],"by":[322],"matching":[323],"statistics":[325],"extracted":[327],"backgrounds":[332],"real":[337],"world":[338],"data.":[339],"Performance":[340],"improvements":[341],"demonstrated":[343],"independently-collected":[346],"datasets":[347]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2020-06-05T00:00:00"}
