{"id":"https://openalex.org/W2962970995","doi":"https://doi.org/10.1109/bigdata.2018.8621865","title":"YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers","display_name":"YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2962970995","doi":"https://doi.org/10.1109/bigdata.2018.8621865","mag":"2962970995"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8621865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8621865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"preprint","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/A5103594074","display_name":"Rachel Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rachel Huang","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, United States","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041470466","display_name":"Jonathan Pedoeem","orcid":null},"institutions":[{"id":"https://openalex.org/I8031829","display_name":"Cooper Union","ror":"https://ror.org/05pbkcd34","country_code":"US","type":"education","lineage":["https://openalex.org/I8031829"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan Pedoeem","raw_affiliation_strings":["Electrical Engineering, The Cooper Union, New York, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering, The Cooper Union, New York, United States","institution_ids":["https://openalex.org/I8031829"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014052149","display_name":"Cuixian Chen","orcid":"https://orcid.org/0000-0003-0938-8841"},"institutions":[{"id":"https://openalex.org/I153901656","display_name":"University of North Carolina Wilmington","ror":"https://ror.org/02t0qr014","country_code":"US","type":"education","lineage":["https://openalex.org/I153901656"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cuixian Chen","raw_affiliation_strings":["Mathematics and Statistics UNC Wilmington, North Carolina, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mathematics and Statistics UNC Wilmington, North Carolina, United States","institution_ids":["https://openalex.org/I153901656"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":20.1807,"has_fulltext":false,"cited_by_count":592,"citation_normalized_percentile":{"value":0.99436038,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2503","last_page":"2510"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"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":0.9894999861717224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/laptop","display_name":"Laptop","score":0.8750061988830566},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8256298303604126},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.7482168674468994},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6771509647369385},{"id":"https://openalex.org/keywords/graphics-processing-unit","display_name":"Graphics processing unit","score":0.6515029668807983},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.5731903910636902},{"id":"https://openalex.org/keywords/graphics","display_name":"Graphics","score":0.4688210189342499},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.4637758135795593},{"id":"https://openalex.org/keywords/general-purpose-computing-on-graphics-processing-units","display_name":"General-purpose computing on graphics processing units","score":0.44935911893844604},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics","score":0.42729029059410095},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.42364639043807983},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4204957187175751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37981724739074707},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3480578064918518},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.1983460783958435},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.1651802957057953}],"concepts":[{"id":"https://openalex.org/C2780008327","wikidata":"https://www.wikidata.org/wiki/Q3962","display_name":"Laptop","level":2,"score":0.8750061988830566},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8256298303604126},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.7482168674468994},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6771509647369385},{"id":"https://openalex.org/C2779851693","wikidata":"https://www.wikidata.org/wiki/Q183484","display_name":"Graphics processing unit","level":2,"score":0.6515029668807983},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.5731903910636902},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.4688210189342499},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.4637758135795593},{"id":"https://openalex.org/C50630238","wikidata":"https://www.wikidata.org/wiki/Q971505","display_name":"General-purpose computing on graphics processing units","level":3,"score":0.44935911893844604},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.42729029059410095},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.42364639043807983},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4204957187175751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37981724739074707},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3480578064918518},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.1983460783958435},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.1651802957057953},{"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:10.1109/bigdata.2018.8621865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8621865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","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":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W2076063813","https://openalex.org/W2102605133","https://openalex.org/W2119144962","https://openalex.org/W2130306094","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2279098554","https://openalex.org/W2557728737","https://openalex.org/W2570343428","https://openalex.org/W2607699895","https://openalex.org/W2612445135","https://openalex.org/W2768205026","https://openalex.org/W2796347433","https://openalex.org/W2949117887","https://openalex.org/W2962965870","https://openalex.org/W2963037989","https://openalex.org/W2963125010","https://openalex.org/W2964299589","https://openalex.org/W2964350391","https://openalex.org/W3047986474","https://openalex.org/W4293584584","https://openalex.org/W4297775537","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6675026286","https://openalex.org/W6677580257","https://openalex.org/W6679349572","https://openalex.org/W6694260854","https://openalex.org/W6726275242","https://openalex.org/W6737664043","https://openalex.org/W6740474667","https://openalex.org/W6746211484","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W2151046618","https://openalex.org/W1972148443","https://openalex.org/W1969233021","https://openalex.org/W2167646277","https://openalex.org/W2027443981","https://openalex.org/W2388314963","https://openalex.org/W3158047141","https://openalex.org/W2063573318","https://openalex.org/W1656096860","https://openalex.org/W2360624069"],"abstract_inverted_index":{"This":[0,77],"paper":[1],"focuses":[2],"on":[3,13,33,39,57,93],"YOLO-LITE,":[4],"a":[5,18,23,44,58,67,105,120],"real-time":[6,116],"object":[7,96,117],"detection":[8,97,118],"model":[9,29,111],"developed":[10],"to":[11,103,119],"run":[12],"portable":[14],"devices":[15],"such":[16],"as":[17],"laptop":[19],"or":[20],"cellphone":[21],"lacking":[22],"Graphics":[24],"Processing":[25],"Unit":[26],"(GPU).":[27],"The":[28],"was":[30,101],"first":[31],"trained":[32],"the":[34,40,84,94,113],"PASCAL":[35],"VOC":[36],"dataset":[37],"then":[38],"COCO":[41],"dataset,":[42],"achieving":[43],"mAP":[45],"of":[46,87,115,122],"33.81%":[47],"and":[48,61,73,108],"12.26%":[49],"respectively.":[50],"YOLO-LITE":[51,100],"runs":[52],"at":[53],"about":[54],"21":[55],"FPS":[56,63],"non-GPU":[59],"computer":[60],"10":[62],"after":[64],"implemented":[65],"onto":[66],"website":[68],"with":[69],"only":[70],"7":[71],"layers":[72],"482":[74],"million":[75],"FLOPS.":[76],"speed":[78],"is":[79],"3.8":[80],"\u00d7":[81],"faster":[82],"than":[83],"fastest":[85],"state":[86],"art":[88],"model,":[89],"SSD":[90],"MobilenetvI.":[91],"Based":[92],"original":[95],"algorithm":[98],"YOLOV2,":[99],"designed":[102],"create":[104],"smaller,":[106],"faster,":[107],"more":[109],"efficient":[110],"increasing":[112],"accessibility":[114],"variety":[121],"devices.":[123]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":55},{"year":2024,"cited_by_count":83},{"year":2023,"cited_by_count":129},{"year":2022,"cited_by_count":119},{"year":2021,"cited_by_count":104},{"year":2020,"cited_by_count":60},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-07T14:30:12.667765","created_date":"2025-10-10T00:00:00"}