{"id":"https://openalex.org/W2984448044","doi":"https://doi.org/10.1109/mmsp.2019.8901808","title":"MRNet: A Competition model for MMSP on Embedded Deep Learning Object Detection","display_name":"MRNet: A Competition model for MMSP on Embedded Deep Learning Object Detection","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2984448044","doi":"https://doi.org/10.1109/mmsp.2019.8901808","mag":"2984448044"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp.2019.8901808","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2019.8901808","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)","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/A5100365151","display_name":"Bin Li","orcid":"https://orcid.org/0000-0002-1998-819X"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bin Li","raw_affiliation_strings":["Xiangtan University School of Information Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"Xiangtan University School of Information Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101644130","display_name":"Yuyu Chen","orcid":"https://orcid.org/0000-0002-6868-1039"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuyu Chen","raw_affiliation_strings":["Xiangtan University School of Information Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"Xiangtan University School of Information Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048995460","display_name":"Wenfeng Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenfeng Xue","raw_affiliation_strings":["Xiangtan University School of Information Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"Xiangtan University School of Information Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100434210","display_name":"Jiaqi Chen","orcid":"https://orcid.org/0009-0002-5126-3460"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Chen","raw_affiliation_strings":["Xiangtan University School of Information Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"Xiangtan University School of Information Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066506625","display_name":"Zun Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zun Weng","raw_affiliation_strings":["Xiangtan University School of Information Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"Xiangtan University School of Information Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035423120","display_name":"Fen Xiao","orcid":"https://orcid.org/0000-0001-7511-9418"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fen Xiao","raw_affiliation_strings":["Xiangtan University School of Information Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"Xiangtan University School of Information Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100365151"],"corresponding_institution_ids":["https://openalex.org/I4610292"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12457471,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"111","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9973000288009644,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/deep-learning","display_name":"Deep learning","score":0.8173938989639282},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8083186745643616},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7318822145462036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6900732517242432},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6447993516921997},{"id":"https://openalex.org/keywords/competition","display_name":"Competition (biology)","score":0.6079655289649963},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5572665333747864},{"id":"https://openalex.org/keywords/learning-object","display_name":"Learning object","score":0.5025217533111572},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4333563446998596},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.42714864015579224},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4208317995071411},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.33856475353240967},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.18915513157844543},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14883247017860413}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8173938989639282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8083186745643616},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7318822145462036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6900732517242432},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6447993516921997},{"id":"https://openalex.org/C91306197","wikidata":"https://www.wikidata.org/wiki/Q45767","display_name":"Competition (biology)","level":2,"score":0.6079655289649963},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5572665333747864},{"id":"https://openalex.org/C2779542340","wikidata":"https://www.wikidata.org/wiki/Q1062461","display_name":"Learning object","level":2,"score":0.5025217533111572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4333563446998596},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.42714864015579224},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4208317995071411},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.33856475353240967},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.18915513157844543},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14883247017860413},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmsp.2019.8901808","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2019.8901808","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2091600398","https://openalex.org/W2160815625","https://openalex.org/W2167235559","https://openalex.org/W2194775991","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963163009","https://openalex.org/W2963267406","https://openalex.org/W2963351448","https://openalex.org/W2963626522","https://openalex.org/W3106250896","https://openalex.org/W4293406525"],"related_works":["https://openalex.org/W2559114496","https://openalex.org/W1451354128","https://openalex.org/W2082067302","https://openalex.org/W2922314686","https://openalex.org/W2367157437","https://openalex.org/W4391382592","https://openalex.org/W4387253492","https://openalex.org/W4308080241","https://openalex.org/W4205668735","https://openalex.org/W1502661168"],"abstract_inverted_index":{"Object":[0,92],"detection":[1],"in":[2,14,28,85,118],"computer":[3],"vision":[4],"area":[5],"has":[6],"been":[7],"extensively":[8],"studied":[9],"and":[10,106],"making":[11],"tremendous":[12],"progress":[13],"recent":[15],"years":[16],"using":[17],"deep":[18,29,65],"learning":[19,30,66],"methods.":[20],"However,":[21],"due":[22],"to":[23,36,50,59],"the":[24,55,86,97,101,115,119],"heavy":[25],"computation":[26],"required":[27],"based":[31],"algorithms,":[32],"it":[33],"is":[34],"hard":[35],"run":[37],"these":[38],"models":[39],"on":[40,109],"embedded":[41,74],"systems,":[42],"which":[43,69],"have":[44],"limited":[45],"computing":[46],"capabilities.":[47],"In":[48,96],"response":[49],"this":[51],"situation,":[52],"we":[53],"combine":[54],"idea":[56],"of":[57,100],"MobileNet":[58],"improve":[60],"RetinaNet,":[61],"design":[62],"a":[63],"lightweight":[64],"model":[67,82,102],"MRNet":[68],"not":[70],"only":[71],"suitable":[72],"for":[73],"systems":[75],"but":[76],"also":[77],"achieve":[78],"high":[79],"accuracy.":[80],"The":[81],"was":[83],"demonstrated":[84],"MMSP":[87],"2019":[88],"Embedded":[89],"Deep":[90],"Learning":[91],"Detection":[93],"Model":[94],"Competition.":[95],"comprehensive":[98],"evaluation":[99],"size,":[103],"computational":[104],"complexity":[105],"running":[107],"speed":[108],"TX2.":[110],"our":[111],"proposed":[112],"method":[113],"won":[114],"third":[116],"place":[117],"competition.":[120]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
