{"id":"https://openalex.org/W4404393430","doi":"https://doi.org/10.1142/s0218001424590195","title":"Multi-Target Detection Method of Intelligent Driving Traffic Scene Based on Faster R-CNN++","display_name":"Multi-Target Detection Method of Intelligent Driving Traffic Scene Based on Faster R-CNN++","publication_year":2024,"publication_date":"2024-11-15","ids":{"openalex":"https://openalex.org/W4404393430","doi":"https://doi.org/10.1142/s0218001424590195"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001424590195","doi":"https://doi.org/10.1142/s0218001424590195","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001424590195","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-d","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"},"volume":null,"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/A5103054421","display_name":"Qiangqiang Xu","orcid":"https://orcid.org/0000-0002-4190-137X","observed_orcids":["https://orcid.org/0000-0002-4190-137X"]},"institutions":[{"id":"https://openalex.org/I13985625","display_name":"East China Jiaotong University","ror":"https://ror.org/05x2f1m38","country_code":"CN","type":"education","lineage":["https://openalex.org/I13985625"]},{"id":"https://openalex.org/I4405281530","display_name":"Sichuan Police College","ror":"https://ror.org/04fzz4e55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405281530"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiangqiang Xu","raw_affiliation_strings":["Department of Road Traffic Management, Sichuan Police College, Sichuan, P. R. China","Intelligent Policing Key Laboratory of Sichuan Province, Sichuan, P. R. China","School of Transportation Engineering, East China Jiaotong University, Jiangxi, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Road Traffic Management, Sichuan Police College, Sichuan, P. R. China","institution_ids":["https://openalex.org/I4405281530"]},{"raw_affiliation_string":"Intelligent Policing Key Laboratory of Sichuan Province, Sichuan, P. R. China","institution_ids":[]},{"raw_affiliation_string":"School of Transportation Engineering, East China Jiaotong University, Jiangxi, P. R. China","institution_ids":["https://openalex.org/I13985625"]}]},{"author_position":"last","author":{"id":null,"display_name":"Junhua Guo","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I13985625","display_name":"East China Jiaotong University","ror":"https://ror.org/05x2f1m38","country_code":"CN","type":"education","lineage":["https://openalex.org/I13985625"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junhua Guo","raw_affiliation_strings":["School of Transportation Engineering, East China Jiaotong University, Jiangxi, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Transportation Engineering, East China Jiaotong University, Jiangxi, P. R. China","institution_ids":["https://openalex.org/I13985625"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1437,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43639166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"39","issue":"01","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9848999977111816,"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":0.9848999977111816,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.01360000018030405,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.00039999998989515007,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multi-target-detection","display_name":"multi-target detection","score":0.8989999890327454},{"id":"https://openalex.org/keywords/intelligent-driving","display_name":"intelligent driving","score":0.8259999752044678},{"id":"https://openalex.org/keywords/faster-r-cnn","display_name":"Faster R-CNN","score":0.9800000190734863},{"id":"https://openalex.org/keywords/region-proposal-network","display_name":"region proposal network","score":0.8730000257492065},{"id":"https://openalex.org/keywords/driver-behavior-recognition","display_name":"driver behavior recognition","score":0.4909999966621399},{"id":"https://openalex.org/keywords/autonomous-driving-perception","display_name":"autonomous driving perception","score":0.6510000228881836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7330814003944397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.671578049659729},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6137306690216064},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4732185900211334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39584988355636597},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1135900616645813},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.08347141742706299}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001424590195","doi":"https://doi.org/10.1142/s0218001424590195","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001424590195","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-d","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1997731668","https://openalex.org/W3135688577","https://openalex.org/W3136021864","https://openalex.org/W3154571917","https://openalex.org/W3154833188","https://openalex.org/W3177990660","https://openalex.org/W3209814988","https://openalex.org/W4206816915","https://openalex.org/W4286322060","https://openalex.org/W4308148751","https://openalex.org/W4308259563","https://openalex.org/W4315498985","https://openalex.org/W4317738585","https://openalex.org/W4320002812","https://openalex.org/W4362633829","https://openalex.org/W4362654659","https://openalex.org/W4363676220","https://openalex.org/W4364323147","https://openalex.org/W4376870884","https://openalex.org/W4385076301","https://openalex.org/W4385495695","https://openalex.org/W4385584842","https://openalex.org/W4385765298","https://openalex.org/W4387089021"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"The":[0,152],"current":[1],"multi-objective":[2],"detection":[3,12,81],"methods":[4],"are":[5,57],"plagued":[6],"by":[7,106],"several":[8],"issues,":[9],"including":[10],"insufficient":[11],"efficiency,":[13],"slow":[14],"speeds,":[15],"and":[16,50,83,138,149],"difficulties":[17],"in":[18,125],"deciphering":[19],"driving":[20],"rules.":[21],"To":[22],"address":[23],"these":[24],"challenges,":[25],"we":[26,111],"introduce":[27],"a":[28],"traffic":[29,159,171],"scene":[30,160],"classification":[31,161],"method":[32,124,130,162],"that":[33,155],"utilizes":[34],"Improved":[35],"Faster":[36],"Regions":[37],"with":[38],"Convolutional":[39],"Neural":[40],"Network":[41,77],"features":[42,61,93],"(Faster":[43],"R-CNN++).":[44],"Initially,":[45],"the":[46,51,63,67,74,84,102,107,120,144,166],"shared":[47],"convolutional":[48],"layer":[49,56,91],"Recurrent":[52],"Criss-Cross":[53],"Attention":[54],"(RCCA)":[55],"employed":[58],"to":[59,79,95],"extract":[60],"from":[62],"input":[64],"image.":[65],"Subsequently,":[66],"derived":[68],"feature":[69,103],"map":[70],"is":[71],"fed":[72],"into":[73],"Region":[75,85,97],"Proposal":[76],"(RPN)":[78],"generate":[80],"boxes,":[82],"of":[86,98,122,169],"Interest":[87,99],"Align":[88],"(RoI":[89],"Align)":[90],"selects":[92],"corresponding":[94],"each":[96],"(RoI)":[100],"on":[101,143],"map,":[104],"guided":[105],"RPN\u2019s":[108],"output.":[109],"Ultimately,":[110],"adopt":[112],"an":[113],"alternating":[114],"training":[115],"approach.":[116],"Simulation":[117],"experiments":[118],"confirm":[119],"effectiveness":[121],"our":[123,156],"multi-scene":[126],"object":[127],"detection.":[128],"Our":[129],"has":[131,139],"demonstrated":[132],"significant":[133],"improvements":[134],"over":[135],"existing":[136],"algorithms":[137],"delivered":[140],"outstanding":[141],"performance":[142],"BDD-100[Formula:":[145],"see":[146],"text]K,":[147],"ApolloScape,":[148],"NuScenes":[150],"datasets.":[151],"results":[153],"indicate":[154],"deep":[157],"learning-based":[158],"can":[163],"accurately":[164],"discern":[165],"behavioral":[167],"characteristics":[168],"various":[170],"participants.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-10-08T09:06:37.294493","created_date":"2025-10-10T00:00:00"}