{"id":"W3215468872","doi":"10.18280/ts.380525","title":"FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"License; False alarm; Computer science; Convolutional neural network; Filter (signal processing); Artificial intelligence; Constant false alarm rate; Generalization; ALARM; Pattern recognition (psychology); Real-time computing; Computer vision; Algorithm; Engineering; Mathematics","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":[0.0007260304,0.001245061,0.0007505045,0.001236056,0.0004229351,0.0007332374,0.001702549,0.00106945,0.00231168],"category_scores_gemma":[0.001986001,0.000349352,0.0008094493,0.0006012474,0.0003863125,0.001386784,0.0008401341,0.001485399,0.0007581454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00104225,"about_ca_system_score_gemma":0.00111661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01766601,"about_ca_topic_score_gemma":0.01567297,"domain_scores_codex":[0.9995787,0.00004145072,0.00002747798,0.0001013895,0.0001684674,0.00008243947],"domain_scores_gemma":[0.9995155,0.0001379396,0.00006427197,0.00005751624,0.0001988208,0.00002589821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000430945,0.0001637748,0.004980869,0.0001190867,0.0001561555,0.0002397783,0.00006573449,0.1839418,0.01361063,0.002620885,0.01130096,0.7823694],"study_design_scores_gemma":[0.00001079885,0.0000545058,0.000978335,0.00001285602,0.00002134541,0.0001047399,0.0000117412,0.9863339,0.009259282,0.001037429,0.002158847,0.00001614183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03092553,0.0006864324,0.9611813,0.0001885465,0.0001630302,0.00007396768,0.0003388075,0.004422582,0.002019822],"genre_scores_gemma":[0.5882228,0.0006725416,0.3949502,0.0004618442,0.0001175151,0.0001450131,0.002659676,0.0003181016,0.01245227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01766601,"threshold_uncertainty_score":0.03512639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01315371317435439,"score_gpt":0.2053627368788887,"score_spread":0.1922090237045344,"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."}}