{"id":"W4229457396","doi":"10.18280/ts.390227","title":"Face Mask Detection Using Lightweight Deep Learning Architecture and Raspberry Pi Hardware: An Approach to Reduce Risk of Coronavirus Spread While Entrance to Indoor Spaces","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face recognition and analysis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Face masks; Raspberry pi; Coronavirus disease 2019 (COVID-19); Pandemic; Computer science; Public health; Face (sociological concept); Computer security; Identification (biology); Deep learning; Control (management); Artificial intelligence; Business; Internet privacy; Disease; Medicine; Internet of Things; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000458419,0.0002020516,0.0002635821,0.0002865299,0.000462835,0.0001447019,0.0004049062,0.00003796423,0.0001103449],"category_scores_gemma":[0.00001537695,0.0002015686,0.00008758328,0.0007444629,0.00003021379,0.0002542602,0.0002203119,0.0003059728,0.000005070397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008479465,"about_ca_system_score_gemma":0.00002832652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001418479,"about_ca_topic_score_gemma":0.00003668816,"domain_scores_codex":[0.997986,0.0003078741,0.0002935339,0.0005917097,0.0005100315,0.000310888],"domain_scores_gemma":[0.9992811,0.00005038135,0.0001754165,0.0002130572,0.0000564495,0.0002235813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001518056,0.0003566048,0.001985722,0.00004641774,0.0001373287,0.000006810608,0.01335525,0.507635,0.1605481,0.0001419568,0.00001188927,0.3156231],"study_design_scores_gemma":[0.001510543,0.001233273,0.01032836,0.00006283759,0.0001884132,0.00007921933,0.003439581,0.8672071,0.1076085,0.000240094,0.007244075,0.0008579688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5037308,0.00009633422,0.4956246,0.0001091829,0.00004849753,0.0002348619,0.00001722777,0.00005234144,0.00008611077],"genre_scores_gemma":[0.9720253,0.00001367645,0.0276006,0.0001490777,0.0000559041,0.00006245178,0.00001154994,0.00001496723,0.00006645548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4682945,"threshold_uncertainty_score":0.8219728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02447009869076944,"score_gpt":0.2433158605521527,"score_spread":0.2188457618613832,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}