{"id":"W4385386790","doi":"10.18280/ria.370312","title":"Automatic Medical Face Mask Recognition for COVID-19 Mitigation: Utilizing YOLO V5 Object Detection","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face recognition and analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Artificial intelligence; Computer vision; Computer science; Face (sociological concept); Object (grammar); Face detection; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Facial recognition system; Object detection; Pattern recognition (psychology); Medicine; Virology; Pathology","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":[0.0004702295,0.000488912,0.0003364147,0.0008728166,0.0003020617,0.0005042578,0.0005152922,0.0007039973,0.003382201],"category_scores_gemma":[0.0008499961,0.0002001165,0.000336655,0.0002579194,0.0001825613,0.0003972955,0.0007715344,0.0003692848,0.001263186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002447414,"about_ca_system_score_gemma":0.0007107608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002105113,"about_ca_topic_score_gemma":0.003917956,"domain_scores_codex":[0.9996203,0.00004369066,0.00001586817,0.00004790698,0.0001978033,0.00007435534],"domain_scores_gemma":[0.9997143,0.00004853176,0.00003615301,0.00004008748,0.000140446,0.00002052823],"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":[0.0006123783,0.0001192931,0.006136398,0.0001994725,0.00006525827,0.0004065572,0.0001003516,0.00434478,0.4688672,0.002050519,0.006020503,0.5110773],"study_design_scores_gemma":[0.0000521262,0.0008291601,0.04069664,0.0001055744,0.0001676229,0.002351499,0.000181377,0.3630149,0.563371,0.001354255,0.02779109,0.00008470444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1627657,0.001032238,0.8225113,0.0004804535,0.0002949616,0.0003387568,0.0004319421,0.003085559,0.009059167],"genre_scores_gemma":[0.5286013,0.0006861215,0.4596693,0.0005623585,0.00008834204,0.0001541746,0.001126419,0.0002139726,0.008897983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003382201,"threshold_uncertainty_score":0.01131463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08522740235494113,"score_gpt":0.3271107469772671,"score_spread":0.2418833446223259,"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."}}