{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001972636,0.000462557,0.0003188858,0.0004304933,0.0002009664,0.0002483158,0.0006232508,0.0004549758,0.001249392],"category_scores_gemma":[0.0004261467,0.0001729481,0.0002970239,0.0001926947,0.0001366948,0.000565124,0.0003787504,0.0003437706,0.0003529218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003323932,"about_ca_system_score_gemma":0.0004540425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004312459,"about_ca_topic_score_gemma":0.004237815,"domain_scores_codex":[0.9998282,0.00001889333,0.000006569698,0.00004286122,0.0000682083,0.00003523043],"domain_scores_gemma":[0.9999015,0.00001950421,0.00001364382,0.00001036498,0.00004444718,0.00001053997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004248294,0.0003476688,0.005778584,0.0001156129,0.00008644567,0.0002391298,0.00008683302,0.02734636,0.1000318,0.001074708,0.004517101,0.8599509],"study_design_scores_gemma":[0.00002239021,0.0003094494,0.006173665,0.00001843576,0.00005876109,0.0002678036,0.00004470811,0.9501244,0.0387181,0.001072439,0.003163579,0.00002614497],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.270087,0.001723231,0.7182113,0.0005898828,0.0002443801,0.0001219062,0.0001573982,0.003843747,0.005021128],"genre_scores_gemma":[0.8561123,0.00060654,0.1367515,0.0002831578,0.0000624577,0.00006043318,0.0002053581,0.00004512848,0.005873141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004312459,"threshold_uncertainty_score":0.008574724,"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."}}