{"id":"https://openalex.org/W4376852364","doi":"https://doi.org/10.1145/3573942.3574111","title":"Traffic Road Detection Based on Dynamic Anchor Frame","display_name":"Traffic Road Detection Based on Dynamic Anchor Frame","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4376852364","doi":"https://doi.org/10.1145/3573942.3574111"},"language":"en","primary_location":{"id":"doi:10.1145/3573942.3574111","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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/A5037827806","display_name":"Xingya Yan","orcid":"https://orcid.org/0000-0003-0380-527X"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xingya Yan","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-0380-527X","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073513913","display_name":"Yujiao Ding","orcid":"https://orcid.org/0000-0003-3430-0864"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujiao Ding","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-3430-0864","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102904971","display_name":"Yue Li","orcid":"https://orcid.org/0000-0003-3780-5569"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Li","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-3780-5569","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5037827806"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17218386,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"894","last_page":"899"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9890000224113464,"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":0.9890000224113464,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9483000040054321,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9322999715805054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.8334780931472778},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.7661557197570801},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7460235357284546},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.7044216990470886},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5000903606414795},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4851720929145813},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4840752184391022},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.47523975372314453},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41919559240341187},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3802846372127533},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.22534453868865967},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19355225563049316},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.18597564101219177},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08276081085205078}],"concepts":[{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.8334780931472778},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.7661557197570801},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7460235357284546},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.7044216990470886},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5000903606414795},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4851720929145813},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4840752184391022},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.47523975372314453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41919559240341187},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3802846372127533},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.22534453868865967},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19355225563049316},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.18597564101219177},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08276081085205078},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573942.3574111","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.550000011920929,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W2904472017"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W4390524233"],"abstract_inverted_index":{"In":[0,182,197],"recent":[1],"years,":[2],"deep":[3],"convolution":[4,40],"neural":[5,41],"networks":[6,42],"have":[7],"made":[8],"great":[9],"progress":[10],"in":[11,31,78,129,136],"object":[12,32,80,116],"detection":[13,81,117],"tasks.":[14],"Generally":[15],"speaking,":[16],"the":[17,21,56,64,121,126,133,143,151,170,185,194,199,202,220,226,232,239,245,250,253,266,272],"bounding":[18,24,47],"box":[19,25,128,135,145,153,172,204],"and":[20,60,62,83,241,278],"type":[22],"of":[23,58,66,93,124,180,201,244,252,268,271],"play":[26],"a":[27,98,130,178,209],"very":[28],"important":[29],"role":[30],"detection.":[33],"However,":[34],"it":[35,236],"is":[36,53,96,104,139,149,155,190,262],"not":[37,156],"easy":[38],"for":[39,115],"to":[43,54,177,192,248,264],"directly":[44],"generate":[45,108],"disordered":[46],"boxes.":[48,217],"A":[49],"widely":[50,76],"used":[51,77,191,263],"solution":[52],"adopt":[55],"idea":[57],"divide":[59],"conquer":[61],"introduce":[63],"concept":[65],"anchor":[67,71,100,127,134,144,152,161,171,187,203,216,222,228,246,254],"box.":[68],"At":[69],"present,":[70],"frame":[72,101,188,223,229,247],"mechanism":[73],"has":[74,84,231],"been":[75],"top-level":[79],"framework,":[82],"achieved":[85],"good":[86,276],"results":[87,279],"on":[88],"common":[89],"datasets.":[90],"The":[91,147,257],"innovation":[92],"this":[94,137,183],"paper":[95],"that":[97,150,169],"novel":[99],"generation":[102,224],"method":[103,123,138,189],"proposed,":[105],"which":[106,167],"can":[107,174],"error":[109],"frames":[110],"with":[111,219],"various":[112],"aspect":[113,242],"ratios":[114],"frames.":[118],"Different":[119],"from":[120,160],"previous":[122],"generating":[125],"predefined":[131,215],"way,":[132],"dynamically":[140],"generated":[141],"by":[142,164,208],"generator.":[146],"feature":[148],"generator":[154,173,205,230],"fixed,":[157],"but":[158],"learns":[159],"boxes":[162],"defined":[163],"fixed":[165],"rules,":[166],"means":[168],"be":[175],"adapted":[176],"variety":[179],"scenarios.":[181],"paper,":[184],"dynamic":[186],"detect":[193],"traffic":[195],"road.":[196],"addition,":[198],"weights":[200],"are":[206,214,280],"predicted":[207],"small":[210],"network":[211],"whose":[212],"inputs":[213],"Compared":[218],"traditional":[221],"methods,":[225],"proposed":[227],"following":[233],"innovations:":[234],"(1)":[235],"adaptive":[237,258],"adjusts":[238],"size":[240,273],"ratio":[243],"improve":[249],"quality":[251],"frame.":[255],"(2)":[256],"IOU":[259],"country":[260],"value":[261],"balance":[265],"number":[267],"positive":[269],"samples":[270],"target.":[274],"Finally,":[275],"efficiency":[277],"obtained.":[281]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
