{"id":"https://openalex.org/W2964526584","doi":"https://doi.org/10.1109/tsp.2019.8769038","title":"Deep Learning for Logo Detection","display_name":"Deep Learning for Logo Detection","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2964526584","doi":"https://doi.org/10.1109/tsp.2019.8769038","mag":"2964526584"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2019.8769038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2019.8769038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","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/A5018875802","display_name":"Karel Pale\u010dek","orcid":"https://orcid.org/0000-0003-0693-6603"},"institutions":[{"id":"https://openalex.org/I147009085","display_name":"Technical University of Liberec","ror":"https://ror.org/02jtk7k02","country_code":"CZ","type":"education","lineage":["https://openalex.org/I147009085"]}],"countries":["CZ"],"is_corresponding":true,"raw_author_name":"Karel Palecek","raw_affiliation_strings":["Department name of organization, Technical University of Liberec, Liberec, Czech Republic"],"affiliations":[{"raw_affiliation_string":"Department name of organization, Technical University of Liberec, Liberec, Czech Republic","institution_ids":["https://openalex.org/I147009085"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5018875802"],"corresponding_institution_ids":["https://openalex.org/I147009085"],"apc_list":null,"apc_paid":null,"fwci":0.4049,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.64786595,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"abs 1605 7678","issue":null,"first_page":"609","last_page":"612"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9979000091552734,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9976000189781189,"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/computer-science","display_name":"Computer science","score":0.8446434140205383},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8030121326446533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6575559973716736},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6136252880096436},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6078588962554932},{"id":"https://openalex.org/keywords/logo","display_name":"Logo (programming language)","score":0.6059912443161011},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5230558514595032},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5025999546051025},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.4506351351737976},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33826708793640137},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.0755048394203186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8446434140205383},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8030121326446533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6575559973716736},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6136252880096436},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6078588962554932},{"id":"https://openalex.org/C2778720087","wikidata":"https://www.wikidata.org/wiki/Q201436","display_name":"Logo (programming language)","level":2,"score":0.6059912443161011},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5230558514595032},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5025999546051025},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.4506351351737976},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33826708793640137},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0755048394203186},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2019.8769038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2019.8769038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1966525090","https://openalex.org/W1980972548","https://openalex.org/W2103577800","https://openalex.org/W2419597278","https://openalex.org/W2557728737","https://openalex.org/W2565639579","https://openalex.org/W2574981201","https://openalex.org/W2610420510","https://openalex.org/W2730200455","https://openalex.org/W2886184223","https://openalex.org/W2890715498","https://openalex.org/W2963012557","https://openalex.org/W2963331658","https://openalex.org/W2963351448","https://openalex.org/W2988916019","https://openalex.org/W3102262568","https://openalex.org/W6675452470","https://openalex.org/W6717177883","https://openalex.org/W6752488411","https://openalex.org/W6754632766"],"related_works":["https://openalex.org/W4382895929","https://openalex.org/W2164977745","https://openalex.org/W4390721878","https://openalex.org/W85029034","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W3210378990","https://openalex.org/W3034745255","https://openalex.org/W4254103348"],"abstract_inverted_index":{"We":[0,14,37,68,125],"present":[1],"a":[2,39,71,94,131],"deep":[3],"learning":[4],"system":[5],"for":[6,93],"automatic":[7],"logo":[8],"detection":[9,73],"in":[10,141],"real":[11],"world":[12],"images.":[13,145],"base":[15],"our":[16,127],"detector":[17,132],"on":[18,79,121],"the":[19,105,116,119,137],"popular":[20,62,81],"framework":[21],"of":[22,43,76,104,115,118,136],"FasterR-CNN":[23],"and":[24,46,49,86,144],"compare":[25],"its":[26],"performance":[27,74],"to":[28,111,129,133],"other":[29],"models":[30,78],"such":[31,64],"as":[32,65],"Mask":[33],"R-CNN":[34],"or":[35,61],"RetinaNet.":[36],"perform":[38],"detailed":[40],"empirical":[41],"analysis":[42],"various":[44,77],"design":[45],"architecture":[47],"choices":[48],"show":[50],"how":[51],"these":[52],"can":[53],"have":[54],"much":[55],"higher":[56],"influence":[57],"than":[58],"algorithmic":[59],"tweaks":[60],"techniques":[63],"data":[66],"augmentation.":[67],"also":[69],"provide":[70],"systematic":[72],"comparison":[75,96],"multiple":[80],"datasets":[82],"including":[83],"FlickrLogos-32,":[84],"TopLogo-10":[85],"recently":[87,98],"introduced":[88],"QMUL-OpenLogo":[89],"benchmark,":[90],"which":[91],"allows":[92],"direct":[95],"between":[97],"proposed":[99],"extensions.":[100],"By":[101],"careful":[102],"optimization":[103],"training":[106],"procedure":[107],"we":[108],"were":[109],"able":[110],"achieve":[112],"significant":[113],"improvements":[114],"state":[117],"art":[120],"all":[122],"mentioned":[123],"datasets.":[124],"apply":[126],"observations":[128],"build":[130],"detect":[134],"logos":[135],"Red":[138],"Bull":[139],"brand":[140],"online":[142],"media":[143]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
