{"id":"https://openalex.org/W2903300682","doi":"https://doi.org/10.1109/icpr.2018.8546289","title":"Attention-based Neural Network for Traffic Sign Detection","display_name":"Attention-based Neural Network for Traffic Sign Detection","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903300682","doi":"https://doi.org/10.1109/icpr.2018.8546289","mag":"2903300682"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8546289","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5014482509","display_name":"Jing Zhang","orcid":"https://orcid.org/0000-0003-2541-4923"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030769176","display_name":"Le Hui","orcid":"https://orcid.org/0000-0003-0851-6805"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Le Hui","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037595310","display_name":"Jianfeng Lu","orcid":"https://orcid.org/0000-0002-9190-507X"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianfeng Lu","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101832219","display_name":"Yuhua Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhua Zhu","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5014482509"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":1.149,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.84083355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1839","last_page":"1844"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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":0.9993000030517578,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T12549","display_name":"Image and Object Detection Techniques","score":0.987500011920929,"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":0.8363451957702637},{"id":"https://openalex.org/keywords/traffic-sign","display_name":"Traffic sign","score":0.7835338115692139},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7080724239349365},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6552767753601074},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.589716374874115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.552125871181488},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5270141363143921},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5233190059661865},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5053001046180725},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47446924448013306},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4423050284385681},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.43337082862854004},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.4286346137523651},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.3850819766521454},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3699101507663727},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35741618275642395},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35649174451828003},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11249274015426636}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8363451957702637},{"id":"https://openalex.org/C2983860417","wikidata":"https://www.wikidata.org/wiki/Q170285","display_name":"Traffic sign","level":3,"score":0.7835338115692139},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7080724239349365},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6552767753601074},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.589716374874115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.552125871181488},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5270141363143921},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5233190059661865},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5053001046180725},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47446924448013306},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4423050284385681},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.43337082862854004},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.4286346137523651},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.3850819766521454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3699101507663727},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35741618275642395},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35649174451828003},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11249274015426636},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/icpr.2018.8546289","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7400000095367432,"display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1832693441","https://openalex.org/W1861492603","https://openalex.org/W1934410531","https://openalex.org/W1998871722","https://openalex.org/W2031489346","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2111502429","https://openalex.org/W2130306094","https://openalex.org/W2141399712","https://openalex.org/W2147527908","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2184393491","https://openalex.org/W2194775991","https://openalex.org/W2221625691","https://openalex.org/W2288122362","https://openalex.org/W2293634267","https://openalex.org/W2307770531","https://openalex.org/W2470322391","https://openalex.org/W2474389331","https://openalex.org/W2479866714","https://openalex.org/W2613718673","https://openalex.org/W2625219738","https://openalex.org/W2706729717","https://openalex.org/W2752782242","https://openalex.org/W2951527505","https://openalex.org/W2963037989","https://openalex.org/W2963173190","https://openalex.org/W2963420686","https://openalex.org/W2963446712","https://openalex.org/W2964099383","https://openalex.org/W3106250896","https://openalex.org/W6618372016","https://openalex.org/W6620707391","https://openalex.org/W6639102338","https://openalex.org/W6679349572","https://openalex.org/W6680834391","https://openalex.org/W6682137061","https://openalex.org/W6684191040","https://openalex.org/W6686211706","https://openalex.org/W6696934422","https://openalex.org/W6697925102","https://openalex.org/W6704241340","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2285052147","https://openalex.org/W2026121273","https://openalex.org/W2079555348","https://openalex.org/W2743258233","https://openalex.org/W2806866760","https://openalex.org/W2586441539","https://openalex.org/W2902062472","https://openalex.org/W2311479510","https://openalex.org/W1905704307","https://openalex.org/W2546942002"],"abstract_inverted_index":{"Existing":[0],"object":[1,46],"detection":[2,47,129,146],"pipelines":[3],"can":[4],"show":[5],"superior":[6],"performance":[7,118,135],"for":[8],"large":[9],"objects":[10,20],"with":[11,52,137],"high":[12],"resolution":[13],"but":[14],"fail":[15],"to":[16,67],"detect":[17],"very":[18],"small":[19,45,124],"such":[21],"as":[22],"traffic":[23,27,90,108,127,144],"signs.":[24],"So,":[25],"detecting":[26,120],"signs":[28,91],"is":[29],"a":[30,39],"proverbially":[31],"challenging":[32],"problem.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37,57],"propose":[38],"novel":[40],"end-to-end":[41],"architecture":[42],"that":[43,112],"improves":[44,116],"by":[48,72,99],"combining":[49],"Faster":[50],"R-CNN":[51],"the":[53,64,69,75,81,87,96,100,106,113,117,123,142],"attention":[54,65,101,114],"mechanism.":[55,102],"Specifically,":[56],"focus":[58],"on":[59,141],"channel-wise":[60,78],"features":[61,98],"and":[62,86],"utilize":[63],"mechanism":[66,115],"enhance":[68],"feature":[70],"responses":[71],"explicitly":[73],"modeling":[74],"interdependencies":[76],"between":[77],"features.":[79],"Finally,":[80],"regression":[82],"of":[83,89,105,119],"bounding":[84],"boxes":[85],"classification":[88],"are":[92],"generated":[93],"after":[94],"selecting":[95],"discriminative":[97],"Extensive":[103],"evaluations":[104],"largest":[107,143],"sign":[109,128,145],"dataset":[110],"demonstrate":[111],"objects,":[121],"especially":[122],"targets.":[125],"For":[126],"task,":[130],"our":[131],"method":[132],"achieves":[133],"better":[134],"compared":[136],"many":[138],"state-of-the-art":[139],"approaches":[140],"dataset,":[147],"Tsinghua-Tencent":[148],"100K.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}