{"id":"https://openalex.org/W2919013816","doi":"https://doi.org/10.1145/3297067.3297081","title":"Object Detection Based on Binocular Vision with Convolutional Neural Network","display_name":"Object Detection Based on Binocular Vision with Convolutional Neural Network","publication_year":2018,"publication_date":"2018-11-28","ids":{"openalex":"https://openalex.org/W2919013816","doi":"https://doi.org/10.1145/3297067.3297081","mag":"2919013816"},"language":"en","primary_location":{"id":"doi:10.1145/3297067.3297081","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297067.3297081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 International Conference on Signal Processing and Machine Learning","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/A5089671069","display_name":"Zekun Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zekun Luo","raw_affiliation_strings":["Tongji University, School of Electronics and Information, Engineering, Shanghai, P. R. China"],"affiliations":[{"raw_affiliation_string":"Tongji University, School of Electronics and Information, Engineering, Shanghai, P. R. China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101436834","display_name":"Xia Wu","orcid":"https://orcid.org/0000-0001-6234-7323"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Wu","raw_affiliation_strings":["Tongji University, School of Electronics and Information, Engineering, Shanghai, P. R. China"],"affiliations":[{"raw_affiliation_string":"Tongji University, School of Electronics and Information, Engineering, Shanghai, P. R. China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080936291","display_name":"Qingquan Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159565","display_name":"SAIC Motor (China)","ror":"https://ror.org/051b9ta18","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159565"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingquan Zou","raw_affiliation_strings":["SAIC Motor Corporation Limited, Research & Advanced Technology, Department, Shanghai, P. R. China"],"affiliations":[{"raw_affiliation_string":"SAIC Motor Corporation Limited, Research & Advanced Technology, Department, Shanghai, P. R. China","institution_ids":["https://openalex.org/I4210159565"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100451721","display_name":"Xiao Xiao","orcid":"https://orcid.org/0000-0002-2319-5563"},"institutions":[{"id":"https://openalex.org/I4210159565","display_name":"SAIC Motor (China)","ror":"https://ror.org/051b9ta18","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159565"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Xiao","raw_affiliation_strings":["SAIC Motor Corporation Limited, Research & Advanced Technology, Department, Shanghai, P. R. China"],"affiliations":[{"raw_affiliation_string":"SAIC Motor Corporation Limited, Research & Advanced Technology, Department, Shanghai, P. R. China","institution_ids":["https://openalex.org/I4210159565"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5089671069"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":0.2089,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.5816744,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"60","last_page":"65"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9983000159263611,"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":0.9955000281333923,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8180670738220215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7664295434951782},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.758118212223053},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7281196713447571},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6650905013084412},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6184883713722229},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5926412343978882},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5831996202468872},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.47282278537750244},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41970834136009216},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3018729090690613},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21751296520233154},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.12441924214363098},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08219906687736511}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8180670738220215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7664295434951782},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.758118212223053},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7281196713447571},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6650905013084412},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6184883713722229},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5926412343978882},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5831996202468872},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.47282278537750244},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41970834136009216},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3018729090690613},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21751296520233154},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.12441924214363098},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08219906687736511},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3297067.3297081","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297067.3297081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 International Conference on Signal Processing and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1679571772","https://openalex.org/W1904365287","https://openalex.org/W1907877624","https://openalex.org/W1979846720","https://openalex.org/W1998372407","https://openalex.org/W2102605133","https://openalex.org/W2123491588","https://openalex.org/W2141746519","https://openalex.org/W2150066425","https://openalex.org/W2184393491","https://openalex.org/W2315372424","https://openalex.org/W2407521645","https://openalex.org/W2470740761","https://openalex.org/W2481401919","https://openalex.org/W2741430497","https://openalex.org/W2743473392","https://openalex.org/W2772020038","https://openalex.org/W2963037989","https://openalex.org/W4235719576","https://openalex.org/W6719862936"],"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":{"Autonomous":[0],"vehicles":[1,20],"are":[2],"widely":[3],"accepted":[4],"as":[5],"one":[6,78],"of":[7,100,113,132,175,183,196,211],"the":[8,35,111,129,150,156,160,168,186,197,212],"most":[9,17,52],"potential":[10],"technologies":[11],"in":[12,46,77,185,205],"alleviating":[13],"traffic":[14],"problems.":[15],"In":[16,63],"existing":[18],"autonomous":[19],"for":[21,189],"object":[22,53,71,151,190],"detection":[23,54,72],"and":[24,48,73,110,127,159,180],"distance":[25,61,74,152,198],"measurement,":[26],"compared":[27],"with":[28,39],"radar":[29],"or":[30],"LIDAR":[31],"which":[32],"obviously":[33],"increases":[34],"cost,":[36],"camera":[37,58,90],"combined":[38],"Convolutional":[40],"Neural":[41],"Network":[42],"(CNN)":[43],"has":[44],"advantage":[45],"accuracy":[47,174,182],"low":[49],"cost.":[50],"However,":[51],"methods":[55],"applied":[56],"on":[57,83,177],"cannot":[59],"perform":[60],"measurement.":[62],"this":[64],"paper,":[65],"we":[66],"simultaneously":[67],"carry":[68],"out":[69],"real-time":[70],"measurement":[75,199],"(DDM)":[76],"system":[79,215],"by":[80,106,141,155],"utilizing":[81],"CNN":[82,119],"a":[84,88,98],"binocular":[85,89],"camera.":[86],"Firstly,":[87],"is":[91,104,116,120,153,200,216],"used":[92],"to":[93,122,145],"acquire":[94],"disparity":[95,108,142,157],"maps.":[96],"Secondly,":[97],"set":[99],"high-quality":[101],"region":[102,114,125,138],"proposals":[103,115,126,139],"generated":[105,140],"those":[107,124,136],"maps":[109,143],"number":[112],"reduced.":[117],"Thirdly,":[118],"utilized":[121],"classify":[123],"get":[128],"bounding":[130,161],"box":[131],"detected":[133],"objects.":[134],"Consequently,":[135],"reduced":[137],"lead":[144],"improved":[146],"computational":[147],"efficiency.":[148],"Finally,":[149],"measured":[154],"map":[158],"box.":[162],"The":[163,192,208],"experiment":[164],"results":[165],"show":[166],"that":[167],"proposed":[169],"method":[170],"can":[171],"achieve":[172],"an":[173,181],"87.2%":[176],"KITTI":[178],"dataset":[179],"68%":[184],"real":[187,206],"environment":[188],"detection.":[191],"average":[193],"relative":[194],"error":[195],"0.85%":[201],"within":[202],"10":[203],"meters":[204],"environment.":[207],"operation":[209],"time":[210],"whole":[213],"DDM":[214],"less":[217],"than":[218],"80":[219],"ms.":[220]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}