{"id":"https://openalex.org/W2793809976","doi":"https://doi.org/10.1109/ipta.2017.8310092","title":"An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network","display_name":"An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2793809976","doi":"https://doi.org/10.1109/ipta.2017.8310092","mag":"2793809976"},"language":"en","primary_location":{"id":"doi:10.1109/ipta.2017.8310092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2017.8310092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","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/A5046742660","display_name":"Jimit Mistry","orcid":null},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Jimit Mistry","raw_affiliation_strings":["Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India"],"affiliations":[{"raw_affiliation_string":"Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007469479","display_name":"Aashish Kumar Misraa","orcid":null},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aashish K. Misraa","raw_affiliation_strings":["Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India"],"affiliations":[{"raw_affiliation_string":"Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101052512","display_name":"Meenu Agarwal","orcid":null},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Meenu Agarwal","raw_affiliation_strings":["Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India"],"affiliations":[{"raw_affiliation_string":"Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003186638","display_name":"Ayushi Vyas","orcid":null},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ayushi Vyas","raw_affiliation_strings":["Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India"],"affiliations":[{"raw_affiliation_string":"Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014088674","display_name":"Vishal Chudasama","orcid":"https://orcid.org/0000-0002-3727-5484"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vishal M. Chudasama","raw_affiliation_strings":["Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India"],"affiliations":[{"raw_affiliation_string":"Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India","institution_ids":["https://openalex.org/I42014448"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047033264","display_name":"Kishor Upla","orcid":"https://orcid.org/0000-0001-6306-0682"},"institutions":[{"id":"https://openalex.org/I42014448","display_name":"Sardar Vallabhbhai National Institute of Technology Surat","ror":"https://ror.org/02y394t43","country_code":"IN","type":"education","lineage":["https://openalex.org/I42014448"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kishor P. Upla","raw_affiliation_strings":["Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India"],"affiliations":[{"raw_affiliation_string":"Electronics Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India","institution_ids":["https://openalex.org/I42014448"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5046742660"],"corresponding_institution_ids":["https://openalex.org/I42014448"],"apc_list":null,"apc_paid":null,"fwci":1.1833,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.87541846,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9997000098228455,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8352102041244507},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.785840630531311},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7029213309288025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6897872090339661},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5266302227973938},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4899384379386902},{"id":"https://openalex.org/keywords/license","display_name":"License","score":0.48805034160614014},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4123498797416687},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35426485538482666}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8352102041244507},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.785840630531311},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7029213309288025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6897872090339661},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5266302227973938},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4899384379386902},{"id":"https://openalex.org/C2780560020","wikidata":"https://www.wikidata.org/wiki/Q79719","display_name":"License","level":2,"score":0.48805034160614014},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4123498797416687},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35426485538482666},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipta.2017.8310092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2017.8310092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1534921592","https://openalex.org/W1545641654","https://openalex.org/W1571669821","https://openalex.org/W1861492603","https://openalex.org/W1976017491","https://openalex.org/W1999116937","https://openalex.org/W2000629924","https://openalex.org/W2049078995","https://openalex.org/W2108598243","https://openalex.org/W2145023731","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2168296665","https://openalex.org/W2549635795","https://openalex.org/W2570343428","https://openalex.org/W2618679224","https://openalex.org/W2963037989","https://openalex.org/W6628973269","https://openalex.org/W6684191040","https://openalex.org/W6731892127"],"related_works":["https://openalex.org/W2606446052","https://openalex.org/W2036021480","https://openalex.org/W3195777957","https://openalex.org/W2382668227","https://openalex.org/W2348482143","https://openalex.org/W2024584030","https://openalex.org/W3104168426","https://openalex.org/W4312856422","https://openalex.org/W2969228573","https://openalex.org/W2963690996"],"abstract_inverted_index":{"Detection":[0],"of":[1,15,54,80,148,159,163,171,181,198,232],"helmeted":[2,55,199,237],"and":[3,32,56,93,225,238],"non-helmeted":[4,57,202,239],"motorcyclist":[5,58],"is":[6,86,125,138],"mandatory":[7],"now-a-days":[8],"in":[9,71,96,111,131,165,174],"order":[10,112,166],"to":[11,22,40,113,127,167,188,207,252],"ensure":[12],"the":[13,18,65,69,78,115,120,132,149,153,169,175,215,230,245],"safety":[14],"riders":[16],"on":[17,140,195,235],"road.":[19],"However,":[20],"due":[21],"many":[23],"constraints":[24],"such":[25,84],"as":[26,186],"poor":[27],"video":[28],"quality,":[29],"occlusion,":[30],"illumination,":[31],"other":[33,253],"varying":[34],"factors":[35],"it":[36,143],"becomes":[37],"very":[38],"difficult":[39],"detect":[41,128,145],"them":[42],"accurately.":[43],"In":[44,152,214],"this":[45,136],"paper,":[46],"we":[47,101,156,218],"introduce":[48],"an":[49],"approach":[50,234],"for":[51],"automatic":[52],"detection":[53,95,117,158,173,259],"using":[59,212],"convolutional":[60],"neural":[61],"network":[62],"(CNN).":[63],"During":[64],"past":[66],"several":[67],"years,":[68],"advancements":[70],"deep":[72],"learning":[73],"models":[74],"have":[75],"drastically":[76],"improved":[77],"performance":[79],"object":[81,94],"detection.":[82],"One":[83],"model":[85,124,137],"YOLOv2":[87,103,123,190],"[1]":[88],"which":[89,192],"combines":[90],"both":[91],"classification":[92],"a":[97],"single":[98],"architecture.":[99],"Here,":[100],"use":[102,157,219],"at":[104],"two":[105,220],"different":[106,129,221,236],"stages":[107],"one":[108],"after":[109],"another":[110],"improve":[114],"helmet":[116,172,226,258],"accuracy.":[118,260],"At":[119],"first":[121],"stage,":[122],"used":[126,185],"objects":[130],"test":[133],"image.":[134,177],"Since":[135],"trained":[139,194],"COCO":[141,150,224],"dataset,":[142],"can":[144],"all":[146],"classes":[147],"dataset.":[151],"proposed":[154,216,246],"approach,":[155,217],"person":[160],"class":[161],"instead":[162],"motorcycle":[164],"increase":[168],"accuracy":[170],"input":[176,187],"The":[178,201],"cropped":[179],"images":[180,203],"detected":[182],"persons":[183],"are":[184,204],"second":[189],"stage":[191],"was":[193],"our":[196,233],"dataset":[197],"images.":[200,240],"processed":[205],"further":[206],"extract":[208],"license":[209],"plate":[210],"by":[211],"OpenALPR.":[213],"datasets":[222],"i.e.,":[223],"datasets.":[227],"We":[228],"tested":[229],"potential":[231],"Experimental":[241],"results":[242],"show":[243],"that":[244],"method":[247],"performs":[248],"better":[249],"when":[250],"compared":[251],"existing":[254],"approaches":[255],"with":[256],"94.70%":[257]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
